能把健康问题交给 ChatGPT 吗？OpenAI 谈医疗 AI 的信任、能力与边界

简要说明Brief Description

一场关于通用人工智能模型如何支持患者和临床医生、如何评估医疗领域的信任与安全，以及智能体、更低成本和科学研究可能如何改善医疗服务的对话。A conversation about how general AI models can support patients and clinicians, how medical trust and safety are evaluated, and where agents, lower costs, and scientific research may improve care.

目录Table of Contents

• 简介
  Intro
• 内森·拉本兹儿子的故事
  The story of Nathan Labenz's son
• 口袋里的医生
  A doctor in your pocket
• 教 AI 了解它正在与谁交谈
  Teaching AI who it's talking to
• 医学共识及其 5—17 年的滞后期
  Medical consensus and its 5–17 year lag
• 心理健康问题
  The mental health problem
• 信息与实际照护
  Information vs actual care
• 健康领域的一步“第37手”
  A "move 37" for health
• 降低护理成本
  Driving down the cost of care
• 程序性护理及下一步安排
  Procedural care and what's next

简介Intro

嘉宾：我们在医疗保健上花费超过5.3万亿美元。如果把它当作一个独立国家，其GDP将位居世界第三。然而，我们仍然面临大量资源匮乏的问题。Open 的使命是确保 AGI 惠及全人类，并让人们能够获得可信、充足、可靠的健康智能。我最受触动的故事之一，是内森·莱文特的故事。他和儿子经历了一段漫长的历程：儿子一度极度疲劳、易怒，AI 帮助他们一步步应对整个过程，最后发现他被诊断出患有一种侵袭性很强的白血病。你在心理健康方面也做了很多工作，这里面有很多复杂之处。考虑到这种情况可能意味着很多不同的事情，你会如何帮助引导用户？Guest: 5.3 plus trillion we spend on healthcare. If it were its own country, it'd be the third largest GDP. And yet we have a ton of scarcity. Open's mission is to ensure AGI benefits all of humanity and give people access to trustworthy, abundant, reliable health intelligence. One of the stories that I was most touched by was the story of Nathan Levent. He basically had this kind of long saga with his son where at some point he was suffering a lot of fatigue and irritableness and AI helped them navigate the process and it turns out he was diagnosed with an aggressive form of leukemia. some of the work that you've done around mental health. There's a lot of complexity. How do you help guide a user on that given that could mean so many things?

我们刚刚一直在与世界各地许多持证心理健康专家合作，探讨如何恰当地定义模型行为、应对各种棘手情境，并确保达成足够的共识，以便真正衡量模型的表现。We've just been working with a bunch of licensed mental health experts across the world to think about the right ways to define model behavior and a bunch of tricky scenarios and ensure that we can get to a level of consensus where we can actually measure performance.

主持人：我们来谈谈信任。一个人和大语言模型交谈时，可能满心希望听到“一切都好”，但从医学角度来看，他们或许需要听到截然不同的说法。你们如何教会人工智能识别这种差别？Host: Let's talk about trust. A person can be speaking to an LLM really hoping to hear that everything is okay, but from a medical perspective, they need to hear perhaps something very different. How do you teach an AI to recognize the difference?

Nathan Labenz 儿子的故事The story of Nathan Labenz's son

主持人：大家好，欢迎回到播客节目。我是杰伊·鲁加尼，A16Z 的合伙人，负责投资医疗保健和生物技术公司。今天和我一起的是 OpenAI 的卡琳·辛加尔。他负责 OpenAI 的健康 AI 研究，我们有很多话题要聊。感谢你来参加节目。Host: hey everyone. Welcome back to the podcast. I'm Jay Rugani, a partner at A16Z, leading investments in healthcare and biotechnology companies. I'm joined by Karin Singal of OpenAI. He leads health AI research at OpenAI and we've got a lot to talk about. Thanks for being here.

嘉宾：谢谢你们的邀请。Guest: Thanks for having me.

主持人：我想从一个大问题开始。今年早些时候有一份报告，你们也公开谈过。每周有数亿人使用 ChatGPT 咨询健康相关问题。他们都在问些什么？他们问的哪些问题，是我们主流医疗体系无法解答的？Host: I want to start with the big question. there was a report earlier this year. You all have talked about it publicly. Hundreds of millions of people each week use ChatGPT for health rellated questions. what are they asking? What are the kinds of questions they're asking that they're not getting from our main healthare system?

嘉宾：是的。让我退一步，聊聊我们为什么一开始会投资健康领域。你知道，Open 的使命是确保 AGI 造福全人类。我们认为，从宏观层面看，这可以通过三种方式实现。第一，构建并部署 AGI：打造能力强大的模型，围绕它们开发产品，进行部署并建立合作伙伴关系。第二，预防各种安全风险。第三，让潜在的积极影响成为现实。我们认为，健康是实现积极影响的一种非常具体的途径，同时，我们也可以借此解决医疗保健领域出现的一些安全问题。因此，我们真正投入精力确保两件事。第一，我们能够提高人类健康的底线，让人们免费或以极低的成本获得值得信赖、丰富且可靠的健康信息，并进一步提高上限，让过去不可能实现的事情成为可能。Guest: Yeah. let me zoom out and talk a little bit about why we're investing in health in the first place. so you know open's mission is to ensure AGI benefits all of humanity. And so we see that at a high level happening in three ways. the first is building and deploying AGI. So building capable models, building products around them, deploying them, partnerships. The second is preventing downside safety of various kinds. The third is making upside happen. and so we view health as a really concrete way to make upside happen and also work on some of the safety problems that come up in healthcare along the way. And so really we're really invested in making sure basically two things. One is that we can raise the floor of human health and give people access to trustworthy abundant reliable health intelligence for free or nearly free and really raise the ceiling as well and make things possible that weren't possible before.

因此，要了解如何真正提高服务的基础水平，最好的方式就是看看我们在消费领域是如何开展工作的。正如我们此前所报告的，每周有超过3亿人使用 ChatGPT 咨询健康与身心 wellness 相关问题，我们认为，确保把这件事做好是一项极其重要的责任，而且这类使用一直在迅速增长。我认为，你能看到的一点是，只要想想看病的体验——即使是在美国，甚至是在湾区——如果你想预约专科医生，有时也要等上几周甚至几个月；你可能得花很多时间弄清楚如何预约、安排时间以及处理各种类似的事情。等了这么久之后，也许终于能和医生聊15分钟，而你觉得自己准备得并不充分，走出诊室时可能还会想：“哦，我还有六个问题想问呢。”And so the best way to see the kind of raising the floor is really to think about how we've been working in the consumer space. and so as we reported there's more than 300 million people weekly using ChatGPT for health and wellness types of questions and we take that as like an incredibly important responsibility to get that right. and that's been rapidly growing over time. I think one of the things that you see is just if you think about the experience of getting healthcare even in the US, even in the Bay Area, you know, if you're looking to get a specialist appointment, even here in the Bay Area, sometimes it can take weeks or months, you might spend a lot of time figuring out how to get an appointment and scheduling and all these kinds of things. And after all that time, maybe get 15 minutes with the doctor where you don't feel adequately prepared and you maybe walk out of it thinking, "Oh, I had like six more questions that I wanted to ask."

我们希望 AI 能够陪伴你走过这段护理历程，因为健康并不只是局限于这种 15 分钟的时间段。AI 确实可以帮助你规划和应对整个护理历程。这也反映了如今人们在消费端使用 AI 的方式。人们带着问题、担忧以及想要了解的信息来咨询。他们想知道如何安排就诊、如何提问，或者如何跟进医生告诉他们的事情，诸如此类。我们发现，在两次就诊之间，AI 最能帮助消费端的用户。你知道，我们也在临床医生端和企业端进行类似的投入。是啊，真了不起。And our hope is that AI can be part of this care journey with you and like health is not something that is just kind of restricted to these kind of 15-minute intervals. And AI can actually help you navigate your care journey. And so that actually reflects how people are using AI on the consumer side today. Like people are coming in with questions and concerns, information that they'd like to have. they want to figure out how to kind of navigate their care visits or ask questions or figure out how to follow up things that they that the doctor told them or things like this. And we're finding that in between these visits is when AI can help people most on the consumer side. And you know, we're making similar investments on the clinician side and on the enterprise side as well. Yeah, it's amazing.

主持人：我们的医疗保健体系非常出色。如果我病得很重，没有哪个国家比美国更适合我去。不过，我们的医疗资源却极度短缺。你知道，你们也看过一些统计数据：超过一亿人无法定期看初级保健医生。即便能看上医生的人，往往也得等上40多天。有时候，正如你刚才所说，你终于见到了医生，结果只有短短15分钟的问诊，而你还有更多问题。我上周因为一件事去看了皮肤科医生。我事先把所有问题都记在 Apple 备忘录里了，结果还是忘了问一件事，现在我又在预约复诊，而这次还得再等四个月。我们每个人都会遇到这种事，也都体会过这种感受。你们最近有没有遇到过某个具体的患者故事或案例，能帮助我们想象未来会是什么样，以及你们的工作将把我们带向何方？Host: We have an extraordinary health care system. if I got really sick, there's no country I'd rather be in than the US. And yet we have a ton of scarcity. We have, you know, you've seen some of these statistics, over 100 million people don't access to a regular primary care doc. Even the ones who do often have to wait 40 plus days to see that doctor. Sometimes you meet the doctor exactly as you said and it's a quick 15inute visit and you have more questions. I saw a dermatologist last week about something. I wrote down all my questions beforehand in an Apple notes and I still forgot to ask something and now I'm scheduling a follow-up and it's another four months out. It happens to all of us. We've all felt that. is there a specific patient story or recent case that you all have seen that kind of help us imagine where the world is going and where your work is taking us?

嘉宾：我们团队的工作让人感到欣慰的一种方式，就是看到这些数字，了解有多少人在使用这项技术，以及它对他们似乎有多大帮助。另一种方式就是听到人们的反馈，对吧？人们会通过邮件或其他各种方式联系我们，每周好几次，告诉我们这项技术是如何帮助他们的。Guest: One way in which the work of our team is gratifying is just like seeing the numbers and seeing how many people are using the tech and how useful it seems to be for them. one other way is just hearing from people, right? So we get people coming in sending us emails or whatever it is reaching out in various ways multiple times a week saying like here's how this has helped me.

我觉得，最让我触动的故事之一，是内森·莱文特和他儿子的故事。他主持《认知革命》播客，也在节目中讲过这段经历。简单来说，他和儿子经历了一段漫长而曲折的过程：有段时间，他年幼的儿子一直很疲倦、容易烦躁，还有类似的症状。AI 帮助他们理清情况，让他们意识到这可能是件相当严重的事——当时他们正在度假，于是决定带孩子去做检查。即使医生当时不太确定，甚至有些淡化病情，AI 还是一再提醒他们：“嘿，要认真对待这件事。”最终，孩子被诊断出患有一种侵袭性很强的白血病。I think one of the things that I one of the stories that I you know was most touched by was the story of Nathan Levent and his son so he hosts the Cognitive Revolution podcast and kind of told his story in that podcast but he basically had this kind of long saga with his son where at some point he was kind of suffering a lot of fatigue and irritableness and things like this. his very young son. and AI helped them kind of navigate the process of realizing that this was like a pretty serious thing while they were on vacation and deciding to take them for testing and repeatedly telling them like, "Hey, take this seriously even when doctors were a little bit less sure and actually kind of downplaying it." And it turns out he was diagnosed with an aggressive form of leukemia.

在他整个就医过程中，我是说，我是说，Nathan 提到，当时他经常使用 GBD5 Pro 来帮助自己了解就医流程、看懂化验报告之类的内容，甚至是在专科医生有时间帮忙解读之前。我觉得这让他们安心了很多。Nathan 其实在自己的播客里讲过这个故事。我不知道在这里能不能推荐其他播客，不过我确实会向大家推荐 Nathan 的那期节目。and throughout his care journey, I mean, I mean, Nathan reports kind of using GBD5 Pro at that time, you know, quite a bit for helping navigate the process, helping understand lab reports and things like this even before, you know, a specialist had time to kind of help interpret. And I think, you know, it brought them a lot of peace of mind. and so Nathan actually tells that story. I don't know if I'm allowed to advertise other podcasts here, but Nathan actually tells that story in his podcast and would recommend that for folks

主持人：从很多方面来说都是如此。那么，OpenAI 正在打造健康智能，并在每一步都将健康智能融入你的模型，但人们用来编写代码或规划假期的也是同一个模型。你能否帮助大家理解，从架构角度来看，你们为什么要这样构建？为什么通用人工智能是如此复杂的医学领域的恰当基础？Host: in many ways. So, OpenAI is building health intelligence and health intelligence into your model at every step, but it's the same model that folks use to write code or plan a vacation. Can you help folks understand why you're building it that way from an architectural standpoint? Why is a general purpose AI the right foundation for something as complicated as medicine?

嘉宾：是的，我的意思是，人们经常会问我们，比如这种通用模型和专用模型之间的争论。我之前的工作其实就是开发专用模型，所以我确实对这个问题的两方面都有一定经验。我认为这里最重要的一点是，我们讨论的实际上是在通用模型中进行专门化训练，而我认为这其实是两全其美的做法。我们为 ChatGPT 开发的模型，也就是 ChatGPT 中发布的所有模型，在 ChatGPT 模型训练的每一个阶段，都会加入专门的医疗健康数据；过去一年里，我们的所有模型都是如此。Guest: Yeah, I mean, people often ask us like, you know, this kind of general versus specialized model debate. And I used to work on specialized models actually in a previous role. And so I definitely have kind of experience on kind of both sides of this question. I think the kind of the most important thing here is like what we're talking about here is actually specialized training in a general model which is I think actually the best of both worlds. So what we do for ChatGPT and all the models that we ship in ChatGPT every across every stage of model training for ChatGPT we actually have specialized healthcare data added to each phase of training since all of our models in the last year.

因此，现在你拥有了一个模型，它既受益于大型前沿模型在规模、整体改进、智能和推理能力方面的优势，也受益于专项训练带来的好处——例如，我们与全球各地的许多医生合作，并整合由此获得的数据。所以我认为，采用这种方法，你可以兼得两方面的优势。要是你另辟蹊径，说“嘿，我们要训练一个更小的专用模型，专门用于医疗保健”……as a result is now you have a model that benefits from all the scale and general improvements and intelligence and reasoning of the big frontier models and also the benefits from the specialized training that we get from for example integrating data that we get from working with a bunch of physicians across the globe and so I think you get the best of both worlds with this approach if you kind of forked and said like hey we're going to train a kind of smaller specialized model that is just for healthcare

就像我以前用过的那种，也像很多人用过的那种，你会发现那个模型很快就会落后于前沿水平。like I used to and like many people have what you'd find is that model would just kind of fall behind the frontier relatively really quickly

因为前沿技术的发展速度和通用模型的进步都非常快。所以，如果你押注规模化，我们作为 OpenAI，也作为整个行业，发现情况会越来越好。我们正在做的，就是搭上规模化前沿发展的顺风车，并利用我们能够将评估和训练数据整合进模型这一优势，让模型既能专精于特定领域，也能受益于通用能力的提升。because the pace of the frontier and the general model improvements is so fast. Then if you bet on scale, what we found as OpenAI as an industry is that things will get better. and so what we're doing is kind of hitching a ride on that kind of scale frontier. and leveraging the fact that we have the ability to kind of integrate evals and training data into the models to make the models both specialize and also benefit from that general capability.

口袋里的医生A doctor in your pocket

主持人：我想先来畅想一下，你们正在打造的这项技术未来能实现什么。显然，它目前已经很出色了，但你们不会止步于此，还有很多工作要做。我觉得，很多人都被“口袋里的私人医生”这个概念吸引了。它无所不知，掌握所有医学专科的知识，这样你就不必为了不同的问题分别去看心脏科医生和皮肤科医生。它还能完全根据你的情况量身定制，了解你完整的病史，记得你说过的每件事，记得你的病史，也了解你的偏好；同时，它还能激励并提醒你，在恰当的时机提供信息，引导你养成更健康的行为习惯，或遵循医生为你制定的方案。Host: I want to almost start imagining what's possible with the technology that you're building. Obviously where it is today is amazing, but you're not stopping and there's a lot of work to go. I think many people are allured by the idea of a personal doctor in your pocket. Something that is perfectly knowledgeable, has knowledge across all medical specialties, so you don't necessarily need to go see a cardiologist and a dermatologist for different questions. something that is perfectly personalized to you, has all your medical history in context, remembers everything that you've said, remembers your medical history, but also your preferences. and is also able to motivate you and nudge you so that the information that it gives you is given to you at the right time in a way that can nudge you towards better health behaviors or adhering to the protocol that your doctor gave you.

从长远来看，这大致是你们在 OpenAI 想要打造的体验吗？我们离实现它还有多远？Is that sort of the experience you're building at OpenAI in the fullness of time or and how close are we to that?

嘉宾：是的，这是我们非常期待的一件事。我们非常看好这样一种愿景：打造一个主动式的个人健康助手。我不认为它能取代真正看医生或获得医生帮助。但我们确实在认真思考，如何让获取健康信息的体验更加贴合个人的具体情况。这是我们在健康领域以及 ChatGPT 上投入的重要组成部分。ChatGPT 是我们面向消费者推出的一项服务，用户可以连接自己的健康信息，让这些信息得到安全使用，不会被用于训练数据、定向广告或类似用途。嗯。Guest: Yeah, that's something that we're really excited about. We are very excited about this vision of kind of a proactive personal health assistant. I don't think that's any kind of replacement for actually having a doctor or having access to a doctor. But really thinking about the ways in which we can take that experience of getting health information and making it more personal to kind of your context is a really important part of kind of our investment in health and ChatGPT which is kind of consumer offering that we have that enables users to connect their health information for that information to be used kind of securely not to be used for training data or for targeting ads or anything like this. Mhm.

这也是我们思考如何迈向一个真正更加个性化、更加主动地提供服务的世界时，一个重要的出发点。这也影响了我们对产品部署的思考。实际上，从一开始，我们就一直尽可能广泛地向免费用户和专业版用户开放这项服务。目前每周活跃的健康用户约有 3 亿，其中绝大多数是免费用户，而且大多数用户也在美国以外。我们非常重视分发渠道这个问题，并将其置于核心位置。因此，在思考如何迈向个人健康助理这一新的前沿时，我们也会把这一点纳入考量。And that's a big part of how we think about how we're going to move towards this world of really like increasingly personal access and proactive access. It's also been how we've been thinking about deployment. So we've actually from the beginning been deploying this as widely as possible to our free users as well as our pro users. And so we have been of the kind of 300 million weekly active health users, the vast majority of them are free users. And we take that and most of them are outside the US as well. M and so we take that kind of question of like distribution here really important really centrally and so as we're thinking about this kind of next frontier of like a proactive personal health assistant we're kind of bringing that to bear as well

主持人：我认为，具体用例能帮助我们想象，也能帮助人们想象这一切究竟能实现什么，以及这些工作如何推动“口袋里的私人医生”这一理念。我觉得这很令人兴奋。Greg Brockman 上播客时，稍微谈到了他遇到的一件事：他有一位朋友的朋友当时住院了，她的医生正准备给她使用某种药物。她在 ChatGPT 里再次核查了这种药物，根据自己的病史意识到，这种药可能会与她正在使用的药物产生相互作用，或者导致某种不良事件。她把这件事告诉了医生，医生表示同意，并说：“我没有足够的时间查看你的病史，所以没能注意到这一点。”所以，非常感谢你把这件事提出来。我们是不是应该始终为每个人都做到这一点？Host: I think use cases help us imagine help people imagine what's actually possible what all this work is contributing to the idea of a personal doctor in your pocket I think is exciting when Greg Brockman came on the podcast he spoke a little bit about this situation he had with a friend of her friend of is was in the hospital and her physician was about to administer a certain medication. She double check double checked that medication in ChatGPT realized based on her medical history that she would have some drug interaction or some adverse event that may come from that. She showed that to her doctor agreed and said I didn't have enough time to look through your medical history to notice that. So thank you so much for raising that. Shouldn't we do that for everyone all the time?

如果成本足够低、使用足够方便，我们能不能针对每种临床情境再征求第二、第三、第四个意见？这就是我们未来的发展方向吗？If it's cheap enough and convenient enough, can't we do a second, third, fourth opinion for every clinical scenario? Is that where we're going?

嘉宾：是的。我很喜欢这个想法：在整个诊疗过程中，为患者和临床人员提供一张始终存在的安全网。你知道，它不会总是介入或充当中间人，而是真正思考如何增强现有体验，并在此基础上进一步提升。你知道，我们实际上做过这方面的工作，更直接地研究临床人员这一侧：我们与 Penda Health 合作开展了一系列实验，研究临床医生副驾驶这一概念。它的部署方式正是如此：在临床人员工作时，为他们提供一张安全网。它会主动介入并提出建议，比如：“嘿，这里可能有问题。你知道，可能存在某种你还没留意到的药物相互作用。”Guest: Yeah. I love this idea of a kind of safety net for both patients and clinicians that is just kind of there in the care journey. And you know, not always interfering or being the middleman, but like really thinking about the ways in which it can augment the existing experience and build on it. you know we did this work actually kind of more directly studying this on the clinician side in a kind of a series of experiments that we ran with Penda Health where we studied this idea of like a clinician co-pilot that was deployed and it was exactly deployed in this way where you know it was a safety net for the clinician as they were doing their work. It kind of came in and proactively suggested like hey this might be off like you know this there might be this medication interaction that you might not be tracking

我们能不能考虑调整这些之类的？我觉得患者方面也会出现同样的情况，而且我对这个想法非常兴奋：在你的医疗护理过程中，有一个主动型代理真正帮助你，并确保如果你的 A1C 升高，而你又患有糖尿病，医生就会检查你的用药情况，诸如此类；同时确保这些情况得到标记，以便进行适当的审查。can we you know potentially adjust that and things like this. I think we're going to see the same thing happen on the patient side and I'm very excited about this idea of like kind of like a proactive p proactive agent in your care journey really helping you and making sure that like you know if your A1C rises and you're diabetic like that you get like review of your medications by your doctor and things like this and making sure that's kind of flagged for kind of appropriate review.

主持人：你提到了某种个性化的主动式智能体。从受欢迎程度来看，过去几个月里，个人智能体总体上开始崭露头角。比如 Instinct、Muse、Grockbot、OpenAI 的 Dots。对于不熟悉这类应用的人来说，你可以给这些 AI 应用设定一个目标，它们就能执行一系列任务。所以，并不是说“嘿，帮我做这件事”，然后它只通过一个简单的循环来完成任务。它可以利用你的偏好，也可以访问你授权它使用的所有应用。如果你把信用卡信息交给它，它也能访问这些信息，并代表你完成目标。人们不禁会想，在我们这个美好却常常支离破碎的医疗保健体系中，这类智能体能发挥哪些作用。比如安排预约，并确保保险涵盖这次预约，或者支付这次预约的费用。Host: You mentioned sort of personalized proactive agents. personal agents in general have kind of emerged on the scene from a popularity perspective over the last few months. Instinct, Muse, Grockbot, OpenAI's dots. you can give these AI apps an objective for folks who are not familiar and it can execute a series of tasks. So it's not just hey do this task and then it executes the task with a single loop. It can use your preferences. It can access all of your applications that you give it permission to. it can access your credit card information if you give that to them and it can execute an objective on your behalf. One can't help but imagine the possibilities there in navigating our wonderful but often disjointed health care system. Scheduling a appointment and making sure your insurance is covered for that appointment or covering that appointment.

你知道，帮助你并指导你遵循医生在你出院后给你制定的护理方案，甚至就医疗账单错误与保险公司交涉。这些都是会给我们的医疗系统带来阻碍的各种事务。you know helping you and guiding you to follow a care protocol that your doctor gave you post discharge. Even negotiating with your insurance company on a medical billing error. all kinds of tasks that just create friction in our healthcare system.

客人：是的。Guest: Yeah.

主持人：这是不是你目前工作的方向？我们可以期待 OpenAI 推出这样的成果吗？Host: Is that where you're kind of going with your work? Should we look forward to that from OpenAI?

嘉宾：我对这一愿景非常兴奋。我觉得，如果从患者和临床医生双方的角度来看，医疗体系及其服务人们的方式，最棘手的问题之一就是生态系统的碎片化，以及各部分之间缺乏联系。而且，医疗行业也是那种不该指望明天所有东西都配备 MCP 连接器的行业，对吧？这里有很多遗留软件。医疗行业一开始就未必具备最好的 AI 或软件互通能力。很多人，你知道，在医疗生态系统中运行主要的 EHR 软件时，用的可能就是虚拟机之类的东西。所以……Guest: I'm very excited about that vision. I think if you look at from both a patient and clinician perspective, one of the most difficult things about the healthare system and how it serves people is this kind of question of like fragmentation and kind of lack of connection of the ecosystem. And healthcare is also one of those industries where you shouldn't expect everything to have an MCP connector tomorrow, right? It is a place where there's legacy software. There's not like necessarily the best connectivity with AI or software in the first place. And many people are, you know, running their primary EHR software in the healthcare ecosystem on like a VM for example. So like

主持人：他们在云端运行的虚拟机。对。Host: virtual machine that they're running in the cloud. Yeah.

嘉宾：没错。所以实际上有个挺有意思的方面：虚拟机在某些方面确实能让 AI 智能体更轻松地完成工作，而这绝对是智能体和计算机操作大有可为的领域。所以，我确实认为，患者与获得所需医疗服务之间、临床医生与提供所需医疗服务之间，以及医疗数据之间，传统上存在的许多障碍都会在未来几个月逐渐消失。医疗数据往往是整个系统无法顺畅地为患者运转的瓶颈。对此我们真的非常兴奋。Guest: Exactly. And so there's actually like an interesting component where like a virtual machine actually makes things easier in some ways for AI agents, but this is absolutely ripe for agents and computer use. so, so I definitely think that this is like a place where a lot of the barriers that have traditionally existed between patients and getting the care they need and clinicians and delivering the care that they need and healthcare data in general, which is often kind of a bottleneck to the whole system working well for patients. I think a lot of those will dissolve in the coming months and so we're really excited about that.

教 AI 了解它正在与谁交谈Teaching AI who it's talking to

主持人：好，我们现在谈的是应用场景，你在帮助我们畅想未来。在继续沿着这个方向讨论之前，咱们先谈谈信任。显然，信任在这里至关重要。同一个底层模型，既可以帮助我理解我的化验结果，或者理解医生告诉我的一些事情，也可以帮助一位训练有素的专科医生理清一个非常复杂的病例。你会如何调整回答，让这两类用户——或者任何其他使用同一个底层模型的用户——都能看得懂？Host: Okay, so we're talking about the use cases, you're helping us dream the dream. before we keep going down that road, let's talk about trust. Obviously trust is the most important here. The same underlying model can help me understand my lab results or something that I heard from a doctor but it can also help a highly trained specialized physician make sense of a very complicated case. What do you change in the answer so that it's legible to each of those two personas or any other persona that might be engaging with the same underlying model?

嘉宾：是啊，我的意思是，正如你所说，医疗健康领域的发展取决于信任建立的速度。因此，你不仅要能改进模型并对其进行评估，还得做好沟通，让全世界都跟上步伐。Guest: Yeah, I mean as you said, health is a place where, you know, it moves at the speed of trust. And so, you need to be able to not only make the models better and measure them, but also kind of communicate and bring the world along.

嘉宾：所以，这一直是我们思考问题和制定策略时非常重要的一部分，涉及我们如何与患者、终端用户以及临床医生合作。简单回答你的问题，我们基本上是通过与世界各地的专家合作，并以各种方式收集反馈，来评估和训练模型，从而针对不同类型的用户调整模型的行为：无论是普通用户、已经长期应对复杂诊疗过程的资深普通用户、临床医生，还是其他类型的用户角色。回到我们在 2025 年 5 月发布的最初版 HealthBench 评测，其中实际上有一个主题或评测部分，专门关注模型行为的这种差异，以及模型如何根据对方是普通用户还是临床医生，调整技术术语等内容的使用程度。Guest: And so, that's been a very important part of our thinking and our strategy for both how we work with patients and how we work with end users and how we work with clinicians. And so the brief answer to your question is basically we evaluate and train the models by working with experts across the world and getting feedback in various ways to kind of tailor their behavior to whether a person is a lay user or kind of an expert lay user that has been like navigating their care for a while a complex care journey or clinician or other kinds of user personas. And so back even for the original healthbench evaluation that we released back in May 2025, there was actually a theme or part of the evaluation that was really focused on this distinction of how the model behaved and tailored its level of kind of use of technical jargon and things like this to whether a person was a lay user or clinician.

从那以后，我们在衡量模型能否满足不同类型用户的需求，以及以此为目标训练模型方面，都做得更好了。因此，我们的方法很大程度上不仅着眼于如何让模型更好地服务这些不同的用户，也着眼于如何带着他们一起前进。其中一部分就是我们如何进行评估，以及如何围绕评估进行沟通。And since then we've both gotten better at measuring whether the models are able to cater to different kinds of users and also training the models towards that. and so a lot of our approach has really been like not only do how do we make the models better for these different users, but how do we bring them along? And part of that is how we do evaluations and communicate about them as well.

主持人：所以，信任由几个要素构成。我们刚才谈到了根据用户的情况调整回答。另一个重要要素是意见不一致。一个人可能在和 LLM 或聊天机器人交谈，分享自己所有的症状，但内心真正希望听到的是一切都没问题。从消费者用户的 NPS（净推荐值）角度来看，他们想听到安慰；但从医疗角度来看，他们可能需要听到截然不同的内容。Host: So there's a few components to trust. So we've talked about sort of tailoring the answer to the user. Another big component is disagreement. a person can be speaking to an LLM or speaking to chat and be sharing all of their symptoms but really hoping to hear that everything is okay. So from a consumer user NPS perspective, net promoter score perspective, they want to hear reassurance, but from a medical perspective, they need to hear perhaps something very different.

是的。Yeah.

主持人：你如何教 AI 识别这种差异，并管理不同的产品目标——一方面是消费者参与度和留存率，另一方面则是准确性和临床质量？Host: How do you teach an AI to recognize the difference and manage different product objectives there either from consumer engagement and retention versus sort of correctness and sort of clinical quality?

嘉宾：是的，这对我们如何进行评估和训练都至关重要。稍微退一步，谈谈我们如何回应以及如何评估时，大家意见一致或不一致的问题。我们可以采取的一种做法，也可以作为我们制定方案的依据，就是邀请几位临床医生加入，比如 OpenAI Health 团队，然后对他们说：“你们来想想该如何处理这类不确定的案例。我们会就这些案例征求你们的意见，你们也会帮助我们构建评估和训练数据等。”然后就交由这几位医生来想办法。Guest: Yeah, this is super central to how we do both evaluation and training. And so zooming out a little bit and the question of like agreement and disagreement around how we respond and how we evaluate like one thing you could do and one thing that we you could kind of base your approach on here is like we could bring in a few clinicians into let's say the opening high health team and say like hey you guys figure out how to navigate these kind of uncertain cases and you know we'll ask you about these uncertain cases and you'll help us build evaluations and training data and stuff like this. and leave it to those few physicians to figure it out.

这实际上并不是最好的方法。事实证明，如果你问五位医生之类的，随便什么问题都可能得到八种不同的答案。所以，我们最初在 OpenAI 开始关注健康领域时，做的第一件事之一，就是建立这样一个全球医生网络，汇集数百位专家。我们与他们合作，他们来自全球各地，涵盖数十个专科、不同语言和执业国家。this is actually not the best approach. So it turns out if you ask five physicians or something this any question you'll get like eight different answers. And so the one of the first things that we did when we first started focusing on health at OpenAI is really build this kind of global physician network of hundreds of experts that we work with kind of across the globe across you know dozens of specialties and languages and countries of practice

要考虑如何以恰当的方式汇总专家的智慧，尤其是在可能存在分歧的领域，避免依赖一两位或三位医生来做裁定。这是其中一部分：考虑如何利用不同专家提供的评估和训练数据，而不是依赖一两个人。但你问题的第二部分是，如何平衡彼此竞争的目标，对吧？比如说，医生们的意见以及汇总起来的医生智慧都指向一个方向。to think about the right ways to kind of aggregate the wisdom of experts so that we weren't relying especially in areas where there could be disagreement we weren't relying on one or two or three physicians to kind of adjudicate. So that's one part of it is kind of like thinking about evaluation and training data coming in from different experts rather than relying on one or two people. But then the second part of your question is like how do you balance kind of competing objectives, right? Let's say the physicians and the aggregate wisdom of the physicians is pointing in one direction

然后，用户可能其实希望你说一些安慰的话，即使当时的情况并不令人安心。这本质上是优先考虑安全。于是……and then maybe the user actually wants you to say something comforting even when the situation is not comforting there. It's basically a prioritization of safety. and so

这对我们来说非常重要：当有数以亿计的健康相关对话涌入时，我们会非常认真地履行自己的责任。因此，在整个评估和训练体系中，我们都会评估这类棘手情形：用户遇到这种情况时，模型应该站出来说：“嘿，这不安全，你应该寻求医疗帮助。”即使用户不想听，或者想尝试某种替代疗法之类的，也应该如此。这是我们进行评估时非常重要的一环。随着时间推移，我们不仅在与医生合作的过程中改进了评估方式，也看到模型在各方面都有了相当显著的提升。在这些领域里，还有另一个分歧的例子，或者说另一种情况：作为消费者的我。it's very important to us that like we take our obligation very seriously when there's hundreds of health hundreds of millions of health conversations coming in. And so across you know the evaluation stack and training stack we have evaluations that track these kind of difficult situations where the user should where the model should actually go and say like hey this is unsafe like you should seek care. even if the user doesn't want to hear it or you know wants to get some kind of alternative treatment or something like this. and that's, you know, a very important part of how we do evaluation. And over time, we've not only improved how we've done the evaluation with working with physicians, but also seen pretty dramatic model improvements across the board. in those areas, there's another example or another instance of disagreement, which is me as the consumer.

主持人：我向 ChatGPT 提出一个医疗问题，然后在就诊时把它的回答给医生看。顺便说一句，我的医生很爱我。然后医生不同意，他们不认同这个回答。我该如何处理这种情况？Host: I ask a medical question of ChatGPT. I then show that response to my doctor during a visit. My doctor loves me, by the way. and then they disagree. they disagree with the response. How should I handle that situation?

嘉宾：是的，我的意思是，我觉得你应该以类似的方式处理，就像是在其他情况下也出现了分歧一样。人们确实会在不同事情上意见不一，而我认为，作为患者，你有权要求另一位医生看看，了解一下情况，也可以去找另一位医生征求第二意见。Guest: Yeah, I mean I think you should handle it similarly as if you know disagreement was happening in some other way as well. Like people do disagree about different things and I think like you have a right as a patient to get you know ask for another doctor to take a look and get a sense and go to another doctor to get a second opinion

是的，这就是我会倡导的做法，无论你是否也在使用 ChatGPT 获取健康信息。and yeah that's what I would advocate for whether or not you're also using ChatGPT to get health information as well.

医学共识及其5至17年的滞后期Medical consensus and its 5–17 year lag

主持人：嗯，非常有道理。信任还有另一个要素，那就是绝对正确。你们做的很多工作，至少从我听你们说的来看，似乎都是为了让答案以全球医学文献和证据为依据。医学有些领域几乎没有什么争议或分歧，但医学中仍有很多内容有待发现或研究。即使在医学界内部也存在分歧。对于这类问题，当我们还在不断探索真相时——你知道，科学本身就是一个过程，等等——就很难让答案以真相为依据。你们如何看待这类情况下的正确性？Host: Yeah, makes a ton of sense. There's another element of trust which is just absolute correctness and a lot of the work that you all have done it seems to be at least I'm hearing from you seems to be about grounding answers in the world's medical literature and evidence. Some parts of medicine there's not a lot of agreement or disagreement about, but there is much of medicine that's still being discovered or being studied. And there is disagreement even in the medical field. And for that you know for that for those kinds of questions it's difficult to ground it in truth when we don't we're still discovering you know truth science as a process etc. How do you think about correctness in those kinds of instances?

嘉宾：是的，这真是个很好的问题。我的意思是，无论有没有 AI，这都很重要。我们不妨把视角拉远一点，看看如今医学共识是如何形成和传播的。现在这个过程是怎样的？基本上，有很多人撰写各种类型的生物医学论文，提出可能改善医疗质量的改进措施、治疗方法之类的建议。然后，每年不同专科都会开展制定临床指南的工作，这些指南大致会说明：比如，在某种情况下，如果你是一名心脏病医生，遇到一位有 X、Y 和 Z 情况的患者，你大概应该怎么做。这些临床指南可以大致看作是流程图，每年由世界各地不同的机构发布，依据最新研究说明在不同情境下大致应该采取什么做法。Guest: Yeah it's a really great question. I mean it's important with or without AI. So if you kind of zoom out and look at like how is medical consensus distributed today, right? Like what is the process of that today? You basically have a bunch of people writing papers, biomedical papers of various kinds suggesting kind of improvements or treatments or things like this that could improve the quality of care. And then you have this process, yearly process across different specialties of building clinical guidelines which kind of describe like hey in this situation if you're a cardiologist and you see a patient with X Y and Z here's what you should probably do. and so these clinical guidelines are roughly you can consider them kind of flowcharts that are put out each year by the different kinds of bodies across the different across the world that describe like roughly what you should do in different scenarios based on the latest research.

嗯。然后最后一步是，你有生物医学文献、临床指南，还有人们在实践中实际上是怎么做的。结果发现，最后这一步是一个巨大的瓶颈。有一些研究探讨了临床指南需要多长时间才能落实到世界各地医生的日常实践中。Mhm. And then the final step is so you have you biomedical literature and then you have the clinical guidelines and then you have like what do people actually do in practice and so the final step it turns out is a ma massive bottleneck. So there are these studies on like how long does it take for clinical guidelines to penetrate the practice of everyday doctors across the world.

过去的研究发现，具体时间因研究而异，这种渗透真正发生大约需要5到17年。And in the past people have found that this takes depending on the study like 5 to 17 years for that penetration to actually happen

这差距非常大。你肯定不希望自己在医疗实践中落后五到十七年。因此，我们思考的一个问题是：如何把这段仿佛一直在沿用过去做法的时期，压缩到几周、几天，甚至几小时？这正是我们在 ChatGPT for healthcare 和 ChatGPT for clinicians 项目背后的一项重要原则。这两款产品面向临床医生和医疗专业人士，能够在诊疗过程中引入最新的生物医学文献，让人们在诊疗现场快速找到问题的答案，比如在一次 15 分钟的就诊过程中。which is massive. Like you don't want to be practicing medicine 5 to 17 years behind. And so one of the things that we think about is how do you compress that kind of period of kind of practicing in the past down to weeks or days or even hours, right? And so this is one very important principle behind our work in ChatGPT for healthcare and ChatGPT for clinicians which are clinician and healthcare professional facing offerings where we're bringing in the latest biomedical literature in the moment of care and allowing people to very quickly in the course of like let's say a 15-minute visit at the point of care get answers to their questions.

所以，这是回答这个问题的一种方式。我的意思是，实际情况是，虽然很多领域都缺乏医学共识，但有些情况下其实存在医学共识，只是没有落实到实践中。因此，你可以做的一件事，就是大幅减少这种情况的发生。这是回答你问题的一种方式。 回答你问题的第二种方式，是探讨如何处理那些仍然存在真正医学不确定性的领域。我觉得这里有几件事可以做。首先，你要训练模型识别自身的不确定性。比如，在浏览并获取信息之后，你希望模型能够意识到：“嘿，现有信息确实不够”，并坦诚地说明这一点。So that's one way of answering the question which is like you know in practice you know while there is medical a lack of medical consensus in many areas in some cases there is medical consensus but it's not being practiced and so one thing you can do is like really dramatically reduce the amount that's happening and so that's one way of answering your question a second way of answering your question is like how to handle the places where there's still like genuine medical uncertainty and there I think I mean there's a couple things so one is you want to train the models to be able to recognize their own uncertainty like after browsing and getting information and things like this you want the models to be able to know, hey, like there just isn't enough information and to be upfront about that.

所以，你知道，这一直是我们开展评估的重要组成部分，也是 HealthBench 的一个公开方面，其中包括对措辞保留的评估，比如在存在这类不确定性时如何表达。所以，这是一个重要方面。第二，我是说，这就涉及这样一个问题：如何提高人类健康的上限。关键在于认真思考模型如何真正加速科学研究进程，推动医学前沿的发展。也许我们稍后可以聊聊这个话题。And so, you know, that's been an important part of how we've done evaluation that's actually like a public aspect of healthbench, which includes kind of evaluations on hedging, for example, when there is this kind of uncertainty. So, that's one important thing. The second thing, I mean, this kind of goes to this question of like how do you raise the ceiling of human health is like really thinking about the ways that models can actually accelerate the process of doing science and actually pushing the medical frontier. And so, maybe we'll chat about that later.

主持人：其中一些领域属于灰色地带，也颇具争议。我想知道，你认为这些模型——或者说，你们在设计模型时——会不会对这些事情表明立场？比如未经批准的肽类、疫苗，以及所有那些经常在媒体上被政治化的话题。Host: Some of these areas are gray areas and controversial. And I'm curious if you think the models or you're designing the models in a way to take a stance on these things, unapproved peptides, vaccines, all things that just get politicized oftent times in the media.

主持人：像聊天这样的产品，在一些仍存在争议的话题上应该如何参与讨论？Host: How should a product like chat engage on some of those topics where there's still debate?

嘉宾：是的，我的意思是，对于那些存在争议或整体上缺乏共识的问题，无论它们是否被政治化，我们都希望确保依靠专家来帮助制定模型应如何回应的标准。这就是我们总体上采取的方法。Guest: Yeah, I mean where there's debate or a lack of consensus across the board whether it's politicized or not, we want to make sure that we are relying on the experts to help define kind of standards for how the model should respond. And so that's kind of the approach that we take across the board.

主持人：听你这么说，感觉你总是在考虑三类对象之一：患者、注册使用服务的临床医生个人，或者企业——无论是医疗服务提供方企业，还是其他类型的企业。你是如何从架构层面，以及比如说 harness 层面来思考产品设计，以满足这些不同用户的需求的？Host: Hearing you talk, it feels like you're always thinking about one of three constituents. It's either the patient, it might be the individual clinician that signed up, or it might be the enterprise, whether it's a healthcare provider enterprise or some other enterprise. How do you think about designing the product at the architectural level and at the let's say the harness level to meet the needs of all these different users?

嘉宾：嗯，我的意思是，我觉得这件事可能有三个部分。一个是让模型真正适合这些用户；第二是围绕模型构建合适的工具框架或产品；第三是部署或合作推进方式，也就是重新触达合适的用户。通常来说，我觉得现在很多时候模型本身已经相当不错了。所以我们谈到了我们的评估方法，比如与临床医生合作等等。我们看到模型的性能和可靠性都有了相当显著的提升。你知道，几个月前我们谈到过，我们在免费服务层级上所做的改进，以及如何让免费层级的性能达到前沿水平。所以，对于每周使用 ChatGPT 的 12 亿人来说，我们可以免费提供一个处于前沿水平、极其有用的健康领域模型。Guest: Yeah, I mean I think maybe there's three parts to that. One is making the models really good for these users. The second is building the right harness or product around it and the third is really like the deployment or partnership motion to kind of re reach out to the right reach out to the users. And like by default I think a lot of the time the models are often pretty good these days. And so we talked about our approach of evaluation working with clinicians and things like this. We are seeing pretty dramatic improvements in kind of performance and reliability. You know we talked a few months ago about the improvements that we've been making on kind of the free tier and bringing free tier performance to the frontier. So you know of the 1.2 billion people who use ChatGPT weekly giving them a model for health that is just like incredibly useful at the frontier for free.

所以，显然，这一直是我们战略中很重要的一部分：让医疗健康智能变得触手可及。第二部分是我们的产品组合。我们的思路是，你可以拥有一个非常出色的模型，但如果使用这个模型来改善健康的门槛太高，人们就不会去用，对吧？因此，我们在产品方面做的很多工作，实际上都是在思考你提到的这些群体，以及如何降低人们使用算力和智能来改善人类健康的门槛。这也体现在我们的相关工作中，比如 ChatGPT Health、面向企业的 ChatGPT for Healthcare，以及 ChatGPT for Clinicians。我们在设计这些产品时，很大程度上是在思考：人们使用这些模型时会遇到哪些障碍和瓶颈，使用门槛又有多高。so that's been a big part of our strategy obviously for making healthcare health intelligence abundant. The second thing is our kind of product offerings. And so the way we think about this is you can have a model that's really good but if the activation energy to use the model to improve health is too high then people won't do it right. So a lot of our product efforts are really like thinking about these constituents that you're talking about and thinking about the ways in which we can reduce the activation energy for people to use compute and intelligence to improve human health. and so that goes you know along the lines of you know our work in ChatGPT health and ChatGPT ChatGPT for healthcare on the enterprise side and ChatGPT for clinicians. A lot of the way we design these offerings is basically to think about like what are the barriers and bottlenecks and activation energies for people to be able to use the models.

比如，向医疗系统提供 ChatGPT 企业版时，安全和合规方面的瓶颈是什么？这是我们两年前经常思考的一个问题。于是我们想，好吧，我们需要在安全和合规方面进行投入。我们需要确保能够提供 HIPAA 合规保障，并确保能够提供医疗机构所需的隐私保护，这样我们才能处理他们所需的医疗数据，并提供值得他们信赖的服务。and so for example you know what are the security and compliance bottlenecks in providing an enterprise offering of ChatGPT to healthcare systems. So that was like one question that we were thinking about a lot 2 years ago. And so we thought, okay, we need to kind of invest in security and compliance. We need to make sure we're able to offer HIPO compliance. We need to make sure that we're able to offer the kinds of privacy protections that healthcare organizations need so that we're able to handle the healthcare data that they need and be able to offer services that they trust.

所以，这大概是我们从两年前左右到一年前左右所做工作中非常重要的一部分。之后，我们基本上就是根据看到的、阻碍普及的瓶颈继续推进。第三件事是合作伙伴关系和部署。还有，几年前我觉得 OpenAI 很早就开始用医疗健康领域的语言交流了。我们和医疗健康领域的合作伙伴接触时，我觉得我们在这些对话中也算是先行者。从那以后，我们做的一件事就是认真思考你提到的每一类相关方，思考如何用他们的语言交流，以及如何让他们一起踏上这段旅程。因为即使模型变得非常聪明，如果没有人真正信任或使用它们，那也就谈不上实现我们的使命。And so that was like a very important part of the work we were doing for like roughly two years ago to like roughly a year ago. and we kind of just go after there from there like based on the bottlenecks that we see to kind of adoption. The third thing is kind of partnerships and deployment. And again, it's really like, you know, a few years ago, I think OpenAI was very early to speaking the language of healthcare. and when we interacted with healthcare partners, I think we were, you know, early in those conversations. So, I think one of the things that we've brought to bear since then is really, you know, thinking about each of those constituents that you're talking about and really thinking about how to speak their language and how to kind of bring them along this journey because even if the models get really smart, if nobody's like really trusting them or using them, that's not really achieving our mission.

主持人：你刚才是不是稍微提到了你们与 Epic 合作开展的一些工作，也就是将产品直接集成到 Epic 中？能不能再多分享一点这方面的情况？因为很多长期在医疗健康领域工作的人，都很想了解你们与 Epic 进行如此深入合作的情况。Host: And is that you're alluding to a little bit some of the work you've done with Epic to kind of integrate the product directly into Epic. Maybe share a little bit more about that because a lot of the folks who spent a lot of time in healthcare were very interested to see that level of collaboration with Epic.

嘉宾：是的，我的意思是，这确实是我们方法中非常重要的一部分。正如我们之前谈到的，医疗领域的碎片化一直是个问题，而数据和工作流程彼此割裂，我认为这是阻碍 AI 在医疗领域发挥实际作用的一大障碍。所以，我们与 Epic 以及其他合作伙伴开展的工作，你知道，最近我们发布了一项公告：现在，如果你是医疗服务提供者或医疗专业人员，就可以在 ChatGPT 中使用 Epic；同时，ChatGPT 也可以在 Epic 本身发挥作用。也就是说，在 Epic 的工作流程中，你可以使用一个小侧边栏，在那里也能用 ChatGPT。Guest: Yeah, I mean this is a very important part of our approach. You like we talked about fragmentation in healthcare has been one of the issues and the fact that there is kind of data and workflows that are disconnected from each other I think is a big obstacle to the use of AI productively in healthcare. and so you know our work with Epic or work with other partners we had a recent announcement where we announced that there's now a way to use Epic in ChatGPT if you're a provider or healthcare professional and also for ChatGPT to be useful in Epic itself. And so on in the Epic workflow itself you can have like a little sidebar where you can actually use ChatGPT as well.

所以这是我们正在做的一件事。我们也在开发其他类型的连接器，最近已经宣布了一些，后续还会推出更多。我们正在做大量工作，确保打破这些障碍，让医疗保健生态系统实现互联。我认为，Epic 只是众多例子之一：我们会找出这些瓶颈，并努力解决它们。and so that's one thing we've we're also working on like different other kinds of connectors that we've also announced recently and we have more coming. And so there is quite a bit of work that we're doing to make sure that we are kind of breaking down these barriers to kind of the healthcare ecosystem being connected. And I think you know Epic is just one of many examples where we're just identifying those bottlenecks and trying to fix them.

心理健康问题The mental health problem

主持人：关于信任，我最后想谈谈你们在心理健康方面做的一些工作。这是另一个复杂性很高的领域，除了我们刚才谈到的临床复杂性之外，你们可能还需要兼顾其他目标。如果我问我的 OpenAI：“嘿，我想在接下来的几周里感觉好一些。”你们会如何理解这个问题？又会如何帮助用户采取行动？考虑到这句话可能有很多种含义，你们会如何引导用户？Host: The last thing I want to hit on with respect to trust is some of the work that you've done around mental health. And this is another area where there's a lot of complexity and there may be other objectives that you need to solve for beyond the clinical complexity that we talked about. If I ask my openai dot, hey, I want to feel better over the next few weeks. How do you even interpret that question? How do you help act on that? How do you help guide a user on that given that could mean so many things?

嘉宾：是的，我的意思是，无论用户使用的是哪种界面，我们都非常重视在用户遇到困难或陷入危机时为他们提供帮助。比如说，对于 DOTS，我们也在这个产品界面中投入了与其他产品界面同等的安全防护措施。所以，我们的做法中一个重要的部分，就是设定一个安全底线。然后，在 mental health bench 方面，我们采取的做法不仅是衡量安全底线，还会思考模型如何主动发挥作用、提高上限，并认真探索模型如何帮助带来积极的心理健康体验，从只关注安全底线，转向衡量这类积极成效。Guest: Yeah, I mean and regardless of the surface, we are super committed to like helping users when they're having a difficult time or when they're in crisis. And so, for example, for DOTS, we've been investing the same kinds of safeguards that we've been putting in all of our product surfaces in that product surface as well. So, that's an important part of our approach is kind of like a baseline safety floor. And then you know with mental health bench we've really taken this approach of not just measuring the safety floor but thinking about the ways in which the models can actually like proactively help kind of raise the ceiling and really think about the ways in which models can you know help provide positive mental health experiences and move from just worrying about the safety floor to measuring those kinds of upsides.

因此，这种做法实际上与我们在 HealthBench 上的工作非常相似。我们一直在与世界各地的一批持证心理健康专家合作，探讨如何恰当地定义模型行为、如何应对一系列棘手情境，并确保能够达成一定程度的共识。在心理健康领域，人们的分歧肯定比在躯体健康领域更大。我们希望能在一些棘手情境中达成一定程度的共识，这样就能实际衡量模型表现，并打造一个行业共享的基准。我们最近刚刚发布了这个基准。与 HealthBench 和 HealthBench Professional 类似，我们的目标一直是创建一个能为所有人提升上限的基准，并在所有模型开发者、医疗行业和心理健康专家之间建立一把真正共享的标尺，以便我们确保自己正朝着正确的方向前进。And so again, the approach is actually very much mirroring our work on Healthbench where we've just been working with a bunch of licensed meth mental health experts across the world to think about the right ways to define model behavior and a bunch of tricky scenarios and ensure that we can get to a level of consensus. people definitely disagree even more here than physical health. Get to a level of consensus in some of these tricky situations where we can actually measure performance and create this kind of like shared benchmark from the industry. And so we released that benchmark very recently and similar to healthbench and healthbench professional the goal has really been can we create this benchmark that kind of raises the ceiling for everybody and kind of creates across all model developers and the healthcare industry and mental health experts like this really shared ruler or measuring stick that we can use to kind of make sure that we're moving in the right direction.

主持人：好的。我们已经谈过智能，以及随着智能水平不断提升而出现的各种应用场景。我们也谈过至关重要的信任。接下来聊聊医疗照护。我想区分一下医疗信息和医疗照护。我认为，对于医疗信息来说，如果你有健康方面的担忧，或者我对自己的健康或正在经历的某件事有所担忧，那么聊天工具和其他大型语言模型正越来越成为获取答案、解决问题更好、更快、更便宜的途径。但如果在某些情况下，仅凭医疗信息无法帮我解决问题，那么至少在今天，我就不得不进入传统的医疗照护体系。所以，如果我需要开处方，如果我知道自己需要去看线下专科医生，如果我需要做一些后续血液检查、核磁共振或其他类型的扫描，或者需要临床医生以某种方式为我做检查，我仍然得在传统医疗体系中奔波。Host: Okay. So we've spoken about intelligence and the use cases that are coming around intelligence approving. We've spoken about trust which is critical. Let's talk about medical care. And I want to draw the distinction between medical information and medical care. I think for medical information, I think if you have a health concern, if I have a health concern about my myself or something that I'm dealing with, chat and other LLMs are increasingly the better, faster, cheaper way to get an answer and a resolution to that question. But if in the scenarios where medical information alone doesn't get me to resolution, I then at least today am thrusted into the traditional medical care system. And so if I need to go get a prescription, if I know need to go see an in-person specialist, if I need to get some follow-up blood work or some MRI or some other form of scan done, if I need a clinician to examine me in some way, I still need to navigate the traditional health care system.

信息与实际照护Information vs actual care

主持人：而这类工作有很大一部分受到美国行医执照以及我们医疗体系中其他基础设施的治理和监管。这是一种多轮对话，而不是只解决一个问题。我得去和几个人谈谈，还得去办几件事。我们需要收集一些信息。从很多方面来说，医疗服务是一种打包产品。你知道，我们既能得到诊断、分诊和护理方案，也能得到临床医生给予的共情、保证和支持。我们还能获得与此相关的法律责任保障，以及他们所承担的责任。AI 能提供这一切吗？它会在哪些方面止步？你觉得这一切会走向何方？Host: And a lot of that work is sort of governed and regulated by the US medical license and sort of other infrastructure that we have in our healthcare system. it's the multi-turn conversation where it's not just a single question resolution. It's I got to go talk to a few people. I need to go get a few things done. We need to collect some information. In many ways, medical care is a it's a bundled product. you know, we're getting the diagnosis and triage and the care plan. We're getting the empathy from the clinician and the assurance and the support. We're getting the legal liability associated with that and what they're taking on. Will AI provide all of that? where will it stop short? where do you think this is all going?

嘉宾：是的。不，我不认为 AI 能够或将会提供这一切。我也认同，你说得对，健康信息和医疗服务之间确实存在区别。所以，我认为我们看待自身角色的方式，实际上是赋能整个生态系统，因为未来不可能由某一家公司提供所有医疗服务。我们的角色，实际上是思考医疗服务的“最后一公里”对人们来说究竟是什么样的。我们能否真正理解这一点，并让每个人都能更轻松地走完这段路？这样一来，真正对患者产生影响的最后一公里，对我们的所有合作伙伴来说就像最后一百米一样简单。这就是我们看待这件事的方式：让合作伙伴能够非常轻松地运用 AI，在整个护理过程中为每个人提供支持。Guest: Yeah. No, I don't think AI can or will provide all of that. And I think you're right that there's like this distinction between health information and medical care. And so I think the way we see our role here is really to enable the whole ecosystem because there's no future where you know any one company is delivering all the medical care right and so our role here is really to think about what is the last mile of medical care really look like for people. Can we really understand that and can we make that easier and easier for everybody so that last mile of actually having impact on patients just looks like the last 100 meters for all of our partners. And so that's kind of how we think about it is like really like making it very easy for our partners to use AI to support everybody in the care journey.

我认为，OpenAI 在这方面有一些相对独特的优势，因为它拥有通往医疗健康领域的入口。比如，它可以帮你处理很多这类问题，或陪你走过这段历程；随着个人智能体越来越有用，它还可以帮你安排日程、处理事务等，充当你健康管理的“领航员”。话虽如此，我不认为它能解决我们面临的所有这些挑战，至少短期内还做不到。我认为在很大程度上，我们希望赋能许多提供医疗服务的合作伙伴；他们目前在各种场景中都广泛使用我们的模型。I think there is stuff that OpenAI can do relatively uniquely here because of the front door to health that it has. And so for example, it can help you navigate a lot of these kinds of questions or help you navigate this journey or you know as we get increasingly helpful personal agents help you with some of the scheduling and logistics and things like this and just act as a quarterback for your health. that said, I don't think it'll resolve all these challenges that we'll have, at least not in the short term. And I think to a large extent, we're looking to enable a lot of our partners that are delivering care that are, you know, overwhelmingly using our models in various ways

以便让这些流程进行得更顺畅、更轻松，这样患者就不会再有这种支离破碎的体验。to like make these operations happen smoother and easier so that patients are not left in this kind of fragmented experience.

主持人：那么，作为消费者，我应该期待从 OpenAI 获得怎样的体验？会是你所描述的那种统筹协调式医疗服务吗？我会在某个环节通过界面与临床医生交流吗？还是你们会帮我转介给医生，并协助我找到合适的医生？毕竟，光是预约并确保就诊顺利进行，本身就是个很大的挑战。我应该期待些什么？Host: So should the as a consumer should the experience that I look forward to from OpenAI be sort of the quarterback care that you described? Will I be talking to a clinician in the interface at any point or would you perhaps route me to a doctor and help me find that given that's a whole challenge just scheduling and making those visits happen. What should I look forward to?

嘉宾：我想你会发现，我们希望能在帮助你找到医疗服务的过程中提供很大帮助。我不认为我们会去做你提到的所有那些事。Guest: I think you'll see like we will be hopefully very helpful in helping you navigate the process of finding care. I don't think we'll be in the business of doing all the things that you mentioned.

但这正是我们真正依靠所有初创企业、医疗服务机构、合作伙伴等的地方。but and that's where we're really relying on all of our startup and care provider organizations and partners and things like this.

主持人：所以，回到我之前提到的一个问题：你们正在构建那个未来，但你们是与合作伙伴共同构建的，是为消费者和患者、临床医生以及企业打造的。对于临床医生和企业，能不能具体介绍一下你们正在推出的一些工具，以及一些真正奏效的合作？Host: So, so coming back to a question I mentioned earlier, which is you are building that future, but you're building it with partners, you're building it for consumers and patients, you're building it for clinicians, you're building it for enterprises. For clinicians and enterprises, like give us some texture on some of the tools that you're launching and some of the collaborations that have really worked.

是的。我们开展了一些非常令人振奋的合作，也有一些非常出色的 ChatGPT 医疗版首发合作伙伴。很多用户一直在使用面向临床医生的 Catch，而且非常喜欢。这个产品的用户留存表现着实让我们惊喜，大家似乎真的很喜欢它。是的，我们一直在与各类医疗系统合作。我想，值得一提的有几家，其中一家是 UCSF。我们一直在与他们合作，也聊过相关情况。他们是最早采用 Epic 与电子病历系统（EHR）和 ChatGPT 之间这项连接功能的机构之一，一直走在前沿探索各种用法，比如临床医生用它来查看病历等。他们也在思考，如何让患者护理体验不再如此割裂，不必频繁切换标签页或使用不同的界面。因此，在这方面，他们是我们的核心用户之一。Yeah. we've had a few really exciting partnerships and we've had some really amazing launch partners for ChatGPT for healthcare. We've had lots of users using catch for clinicians and really enjoying it. It's been the retention on the offering has been like a very pleasant surprise. People seem to be really enjoying it. yeah, we've been working with a variety of health systems. I think one a couple that stand out you know one is we've been working with UCSF and we've been chatting about that and they've been you know one of our earliest adopters for this connection between Epic and the EHR and ChatGPT and they've been really on the front lines of thinking about for example clinicians using it for chart review or things like this and thinking about the right ways in which like the experience of caring for a patient doesn't need to be this super fragmented you need to switch tabs or switch experiences and things like this and so they've been some of our power users on that front.

我觉得另一个例子是 Advent Health。他们一直是我们的重度用户之一，也一直在思考如何在整个工作空间，实际上是在整个 OpenAI 范围内部署智能体。他们一直走在前列，而且取得了非常出色的成果。据他们报告，通过以这些方式使用 AI，许多行政工作流程的成本降低了约 80%。我认为很多医疗系统都应该向他们学习。他们确实令人印象深刻。I think another one is Advent Health and they've actually been some of our power users in think and thinking about deploying agents across the workspace actually across all of OpenAI. They've been a leader and they've been really they've had amazing results. They've reported roughly 80% reduction a lot of their administrative workflow costs using AI in these kinds of ways. And I think a lot of health systems I think should look to them. I think they're really impressive.

主持人：如果我走进医生的诊室，看到他们的屏幕上开着 ChatGPT，我该生气吗？还是该高兴？是吧。Host: If I go into my doctor's office and I see ChatGPT open up on their screen, should I be upset? Should I be happy? Yeah.

主持人：你觉得人们会对此作何反应？Host: How do you think people will react to that?

嘉宾：是的。我听到的说法各不相同，而且我觉得情况确实在不断变化。所以……Guest: Yeah. I hear differing things and I think it's actually changing over time. And so

我觉得这是一件很有意思的事，因为奥弗顿窗口正在实时变化。我想，你知道，之前可能确实有过一定程度的担忧。I think it's this interesting thing whereas as the Overton window is shifting in real time. I think you know there might have been some level of concern before

但我觉得，越来越多的人发现，双方都有这么一个问题：临床医生是否会反对患者使用 AI，以及患者是否会反对临床医生使用 AI，就像你描述的那样。不过我认为，双方其实都发现 AI 对各自的个人用途非常有帮助。Mbut I think increasingly a lot of you know on both sides you know there's this question of like you know whether clinicians would push back against patient use of AI and whether patients would push back on clinician use of AI in the way that you're describing but I think both parties are actually finding incredibly useful for their own personal purposes. M

所以我认为，从实际情况来看，尤其是随着时间推移，我们发现双方在接受治疗的过程中，可能都会因为 AI 的参与而感到更安心——无论是临床医生在使用 AI，还是患者把它当作一种核实判断的方式。我认为这种态度确实在逐渐改变，但我不会说我们已经完全做到这一点了。不过，我预测事情会朝这个方向发展。and so I think what we're finding in practice especially over time is that both parties might actually feel safer with AI in their care journey whether it's you know their clinician using it or the patient using it as kind of like a sanity check and I think that attitude is definitely changing over time but I wouldn't say that we're fully there but that's my prediction of how things go.

主持人：好的。那么再进一步说，如果患者在就诊前已经做了所有功课，整合了自己的所有健康数据，并在 ChatGPT 上问了一大堆问题；而医生也在就诊前通过各种自动化流程，把整个病例都交给 ChatGPT 处理过了，那么就诊时他们应该谈些什么？Host: Okay. So then taking that one step further, if a patient has done all their research before a visit and they have all their health data integrated and they've asked a bunch of questions in ChatGPT and then the clinician before the visit has all automated through loops has run the entire patient case through ChatGPT and then the visit happens. What should they talk about?

嘉宾：我希望他们能谈谈医疗保健中人性化的一面。我的意思是，目前仍有很多不确定性，也需要很多共情和床边关怀，这些都是医疗保健人性化的一部分。此外，医疗保健还涉及大量身体接触。我不认为你知道……我不认为人工智能已经掌握了所有信息——也就是说，理论上，人工智能掌握了所有信息，就能自行判断应该做什么。而就诊的部分目的，正是获取更多信息，并进行病史采集、检查等流程。因此，我认为这些不会消失，尤其是其中需要身体接触的部分。而且，即便从认知层面来看，共情、获得安慰以及得到第二次确认，我认为人们仍然会有所需求。Guest: my hope is that they talk about the human aspect of healthcare. I mean there's still like a lot of uncertainty. There's a lot of empathy. There's a lot of bedside manner that goes into the human aspects of healthcare. There's also a lot of physical interaction that goes into it. I don't think you know I don't think all the information exists for the AI just information the theoretically all the information exists for the AI to just figure out what all should be done and part of the reason for a visit is to get more information and do the process of history taking investigation and things like this. And so I think that will not go away especially the physical elements of that. And I think even from a cognitive perspective, the elements of kind of empathy and getting comfort and getting that second check I think will still be something that people demand.

主持人：你认为 AI 总体上，或者说 Dots，会在两次就诊之间做些什么？你觉得它会不会越界，在两次就诊之间提供医疗护理，然后再把相关信息反馈给医生？Host: Can what do you think AI in general or let's say dots would do in between visits and will you think you'll cross the line into providing care in between visits and then share that back with the doctor?

嘉宾：是啊，不。我觉得我们肯定会聚焦于健康信息。不过，无论是 Dots 还是其他产品界面，我觉得我们都非常期待这样的未来：朝着你刚才描绘的方向，变得更加主动。比如，想象一下，有人正在经历慢性病护理的过程。再比如，你知道，有人拿到一份化验结果，发现自己的 A1C 指标偏高，属于糖尿病前期或已经患有糖尿病。你能不能提醒他们：“嘿，也许你该检查一下用药情况，再去看一下心脏病专家什么的。”Guest: Yeah. No, I don't think we'll I think we'll definitely be focused on health information, but I think the whether it's dots or any other product surface, I think we're really excited about this kind of future of heading more in this proactive direction that you were outlining. Like imagine if somebody is kind of dealing with a chronic care journey and you know they're again like this example of like someone's A1 someone gets a lab test back where their A1C is higher and they're pre-diabetic or diabetic you know can you flag for them like hey maybe you should get a medication check and go to your you know cardiologist or whatever

并思考如何以恰当的方式提醒用户注意用药依从性问题或其他类型的问题。所以我认为，这类问题是 AI 有可能对健康产生重大影响的领域，尤其是在这类慢性病护理过程中。但我不认为我们会亲自负责管理所有这些事情。and think about the right ways in which you can you know flag to users medication adherence issues or other kinds of issues. So I think those kinds of issues are places where you know AI can have potentially a really high health impact especially like these kind of chronic care journeys but I don't think it'll be we'll be in the business of like managing all these things by ourselves.

主持人：好的。Host: Sure.

是的。Yeah.

健康领域的“第37手”A "move 37" for health

主持人：我还很好奇，人工智能应用于其他领域和场景时，比如国际象棋和围棋，会出现什么情况。人工智能在这些棋类游戏中变得非常厉害后，也揭示出了一些新的策略和下棋方式，甚至连这些领域最顶尖的专家都没有运用或尝试过。这帮助我们更深入地了解这些游戏，也帮助我们更深入地了解人工智能。在你们的研究工作中，你是否也看到过类似的情况，出现在人工智能应用于医疗健康领域时？目前是否已经有一些发现令你感到惊讶，甚至带来了新的医疗服务方式，或对临床情境有了新的理解，让我们得以抱有一些乐观期待？Host: Another thing I'm curious about is in the fields of AI applied to other domains and situations like let's say games like chess and go. AI as AI got really good at chess and go they also revealed other strategies and ways of playing the game that even the best experts in those domains were not exploiting or not pursuing and that helped us learn more about these games and also helped us learn more about AI. Are there equivalents like that you've seen in AI applied to health from the work that you've done in research? Are there already findings that are surprising you and perhaps introducing new ways of delivering care or understanding a clinical scenario that can give us some optimism?

嘉宾：是的，我的意思是，我觉得我们在较小的规模上见过这种情况。不过让我把视角放大一点，也就是说，最近有个想法一直在激励我们团队，推动我们去提升人类健康的上限：为健康领域带来一种类似“第37手棋”的突破。Guest: Yeah, I mean I think we've seen this at smaller scales, but let me zoom out and say like, you know, one thing that's been motivating our team in general recently around raising the ceiling of human health is this notion of bringing to bear like kind of a move 37 for health.

嘉宾：那么，“第37手”这个说法源自 AlphaGo。AlphaGo 系统下了一场这种复杂棋盘游戏的对局，对手是李世石。Guest: And this idea of a move 37 is it comes from Alph Go. and a game that the Alpha Go system which plays this kind of complicated board game played with Lisa Dole

当时有一步棋，在那个瞬间看起来简直愚蠢至极，也让所有观看这场比赛的人类专家都感到意外。where there was a specific move that just kind of in the moment was seemed like a really dumb move and a surprising move for all the human experts watching this game

在这个 AI 系统和这位世界级专家之间，然后……不过事后看来，这是不是一种非常聪明、富有洞察力的做法，而人类现在才刚刚开始理解并从这些模式之类的东西中学习？是的。between this AI system and this world expert and go but in hindsight is this kind of like brilliant and insightful move that humans are now just u beginning to understand and learn from the patterns of and things like this. Yeah.

这是一个非常有趣的例子。我认为，这个想法很正确、出色、富有洞见且影响深远：如何在医疗健康领域做出类似“第37手”的妙着，或者作出这样的预测——让模型预测出某种人类事后看来可能都无法预测的东西。And this is very interesting example. I think so this idea of like how do you get to a move 37 in health of a move or prediction where the models predict something that a human might not have been able to predict in hindsight seems correct and brilliant and insightful and impactful.

所以我认为我们现在还处于非常早期的阶段，未来你会看到这类应用比过去多得多。不过，我想其中一个例子是，再次回想这些系统被部署为临床医生的安全保障的方式：临床医生实际上正在提供护理，而人工智能会在当下标记说：“嘿，这里可能有些不对劲。”比如我们与 Panda Health 合作开展的研究，以及我们在那里看到的结果模式。So I think we're very early and I think you're going to see much more of this in the future than in the past. But I think you know I mean one kind of example is again thinking about these the ways in which these systems have been deployed as safety nets to clinicians where clinicians are actually delivering care and in the moment the AI actually flags kind of like hey something might be off here like our study with Panda Health and thinking about the patterns of results that we saw there

举个例子，在那种情况下，临床医生有点倾向于过度使用抗生素。as an example you know in that context the clinicians were a little bit prone to overuse of antibiotics

模型标记出的一种非常常见的模式，就是避免过度使用抗生素，并且少关注个别病例，多考虑抗生素耐药性这一全球性问题。这算是一个具体例子，但我想你还会看到更多这样的例子：模型会综合用户长达十年的纵向历史记录，包括他们的所有数据、人力资源数据、聊天数据以及其他各种数据，并据此做出以前看似不可能的预测，因为人类甚至无法在脑中整合所有这些数据。and a very common pattern that the models flagged is just kind of like avoiding this overuse of antibiotics and thinking less about the local case and more about the kind of global issue of resistance to antibiotics. So that's like one, you know, specific example, but again, I think you're going to see more of these examples of models taking in like a longitunal history of a user over 10 years across all their data, across their HR data, across their chat data, all the all these things and making predictions that seemed impossible before cuz a human can't even integrate all that data in their head.

并对他们的健康状况、风险之类的事情做出预测，而这些似乎是不可能做到的。我认为，这足以称得上是“第37手”。And making predictions about their health and their risks and things like this that seemed like they were impossible. And I think that would rise to the occasion of being called the Move 37.

主持人：是的，是的，我的意思是，消费者好奇的事情太多了。至少曾经有一段时间，我们不确定某些东西对你是否健康；而正如你所说，奥弗顿窗口发生了变化，出现了更多证据，而且这些证据似乎明确指向某一方。你知道，曾经有一段时间，人们不确定吸烟是否有害。咖啡对你有益还是有害？糖对你有益还是有害？有时，人们对其中一些事情的看法会发生变化。如今，消费者想知道：肌酸对你有益吗？褪黑素对你有益吗？你知道，所有这些新出现的现象，可能都还缺乏充分研究；或者至少，人们对某些补充剂或药物的安全性和有效性还没有广泛达成共识。Host: Yeah. Yeah, I mean there are there's so many things that consumers are curious about that at least at one point in time we weren't sure if it was healthy for you and then the to your point the Overton window changed and there was sort of more evidence that was sort of conclusive in one direction. You know, at one point people weren't sure if smoking was bad for you. Is coffee good or bad for you? Is sugar good or bad for you? sometimes the views on some of these things have changed and today consumers are they want to know is creatine good for you? Is melatonin good for you? you know all these sort of new emerging phenomenons that perhaps are underststudied or at least you know not widely sort of agreed upon this safety and effectiveness of certain supplements or certain medications and things like that.

那么，你是否预期，随着人们把这些信息输入 ChatGPT 或其他大语言模型，我们会获得更多可以用来研究某些问题的证据？大家对这些问题很好奇，但我们其实并不了解，或者说还没有确凿证据来判断它们究竟如何。Can do you anticipate as people are sort of entering this information into ChatGPT or other LLMs that we'll have more evidence that we can generate to study some of these things that folks are curious about but we don't really know or we don't have conclusive evidence one way or the other on where they stand.

嘉宾：是的。我觉得，智能体最有意思的一点在于，它们能够解决分析现有数据时遇到的许多瓶颈。没错。医疗生态系统中的各个环节都在不断产生数据，比如电子健康记录数据、纵向病史、理赔数据等等。Guest: Yeah. I mean I think one of the most interesting things about agents is they kind of solve a lot of the bottlenecks in analyzing data that already exists. Right. There's various places across the healthcare ecosystem where data is being generated. EHR data, longitudinal histories, claims, whatever

数据在这里被生成后，只是暂时存储起来，其中蕴含的洞察并未得到充分挖掘，也没有用于生成新的知识。where data is being generated and it is kind of just stored there and the insights from it are not fully mined or used to generate new knowledge.

是的。所以我认为，未来健康水平可能提升、健康的上限可能提高的一个前沿方向，是思考如何利用那些散落在各处、存放在各种设备中的数据，并让模型基于这些数据产生新的洞见。因此，我对这样的未来也很乐观。我也对另一种未来很乐观：你刚才提到了两个群体——用户，也就是患者，以及临床医生。我认为还有第三个群体，那就是科学家，他们帮助推动这一领域的知识前沿不断发展。我也非常看好这样一种未来：人工智能能够帮助加快许多验证环节的进展，疏通其中的瓶颈。Yeah. And so I think one of the frontiers for how health might improve in the future and how the ceiling of health might raise will be thinking about the ways in which we can leverage the data that is just kind of out there in boxes in various places and have models generate new insights based on this data. So I'm also optimistic about that future. I'm also optimistic about a future where you know you mentioned these two constituencies the user the patient and the clinician. I think there is a third constituency which is the scientists right which was helping kind of advance the frontier of knowledge in the space and I'm also very optimistic about a future in which you know AI helps accelerate a lot of the bottlenecks in validation

我认为最终成为瓶颈的会是这些，而不是提出新想法、新疗法之类的事情，所以我对此也非常乐观。which I think will end up being the bottlenecks as opposed to kind of proposing new ideas or treatments or things like this and so very optimistic about that as well.

降低护理成本Driving down the cost of care

主持人：稍微换个话题，你也谈过，作为医疗智能投入要素的智能成本正在大幅下降，而且你知道，到了某个时候，它会便宜到无法计量。我花了很多时间，我们整个团队也花了很多时间，试图理解这些技术将如何影响我们的医疗体系——医疗支出的增长速度远远超过通胀。我们在医疗上花费超过5.3万亿美元。要是把它当成一个独立国家，它的GDP将位居世界第三。它是我们经济中占比最大的部分，也是我们经济中最大的就业行业。2300万美国人在医疗行业工作。我们在医疗上花的钱实在太多了。你怎么看你正在做的工作，以及它会对医疗成本产生什么影响？Host: Switching gears slightly, you've also shared thoughts around just the cost of intelligence as an input to medical intelligence coming way down and you know at some point it'll be too cheap to meter. I spent a lot of time and we as a team spent a lot of time trying to understand how these technologies will make an impact on our healthcare system that is inflating well ahead of inflation. 5.3 plus trillion we spend on healthcare. If it were its own country, it'd be the third largest GDP. it's the biggest part of our economy. It's also the biggest employer in our economy. 23 million Americans work in healthcare. There's so much there's just so much we spend on healthcare. What do you sort of make of the work that you're doing and the impact that it'll have on the cost of care?

嘉宾：我认为，这是我们践行使命最重要的方式之一：不仅要让我们的模型在前沿领域表现得更好，还要让所有人都能广泛使用它们。显然，这也是我们能够免费向全球许多人提供健康智能服务的重要原因之一。我觉得，这也很大程度上体现了我们对 API 的看法，以及我们的模型在健康领域的表现、相关定价等等。最近的一项更新是，我们刚刚发布了 GPD6 Luna。Guest: I think this is like one of the most important ways that we can pursue our mission is to not only make our models better at the frontier, but make that widely accessible to everybody. So that's, you know, obviously a big part of how we've been able to deploy health intelligence to a lot of people across the world for free. I think it's also a big part of how we think about, you know, API and how good our models are there for health and pricing there and stuff like this. You know, one recent update was we recently released GPD6 Luna

而 GP6 Luna 是我们目前在 API 上最便宜的模型，或者说，是 API 上最便宜的前沿模型。我们发现，GP6 Luna 在我们的基准测试 Healthbench Professional 上表现确实更好。这是我们目前最新的基准测试，涵盖了各种临床工作流程；这些工作流程都经过挑选，难度很高，涉及临床医生使用我们模型的各种方式，而这些模型仍有改进空间。and GP6 Luna's is our current cheapest model on the API or cheapest, you know, frontier model on the API. And what we found with GP6 Luna was that it was actually doing better on our benchmark Healthbench Professional, our current latest benchmark of kind of clinician workflows that are like really difficult selected for difficulty for our current models across all the different ways in which clinicians are using our models and the models still could be could use some improvement.

主持人：嗯。Host: Mh.

嘉宾：它的表现优于 5.6 Soul，而就在一个月前，它还确实是我们最好的模型。尽管成本只有它的三十四分之一，它的表现却更好。过去一年里，我们也经历过几次这样的时刻。你知道，许多趋势都表明，与过去推出的其他技术相比，人工智能在达到一定能力或实用性水平时，成本下降得更快。因此，OpenAI 的一个判断是：在达到一定健康智能水平时，健康智能的成本将大幅下降。Guest: it performed better than 5.6 Soul, which was literally our best model like a month ago. And so and it's it performs better despite being 34 times cheaper. and we've had a couple of those moments even in the past year. And you know, there's been many trends showing that AI compared to other technologies that have been introduced in the past has been reducing a cost at a given level of kind of capability or usefulness faster than many of those other technologies. And so one bet we've had at OpenAI is that the cost of health intelligence will plummet at a given level of health intelligence.

而且，医疗智能达到某个水平后，临床医生和患者在整个诊疗过程中会觉得自己能够信赖它——我们正开始接近这个水平，对吧？从某种意义上说，这就像是一个固定的智能水平。希望随着时间推移，这个标准也会不断提高。我的预期是，一旦我们接近这个智能水平，达到它的成本也会大幅降低，就像一直以来的趋势那样。我们希望的是，医疗智能能够在某种程度上成为一种普惠资源，对吧？我们不想把医疗智能据为己有，而是希望每个人都能使用它。And the level of health intelligence where you know clinicians and patients feel like they can trust in the care journey is starting to get there, right? So that's like a fixed level of intelligence in some sense. Hopefully the bar for that raises over time as well. my expectation is once we get close to that level of intelligence the cost for that level of intelligence also dramatically reduces like it has been as the hope there is that we get like a kind of this commoditization of health intelligence right we don't want to hoard that health intelligence we wanted to everybody to use it

所以，我希望这能有所帮助。不过，回答你关于整个医疗支出的问题，我认为这并不全是健康智能的事。我觉得健康智能迄今还没有成为一个宏观因素，或者说，还没有对你描述的问题产生宏观影响。so my hope that contributes but you know to answer your question about the whole of healthcare spending I don't think all of that is about health intelligence I don't think like health intelligence has had yet a macro factor on the a macro effect on the problem that you're describing

如果你看医疗领域的整体支出，会发现有不少领域——比如慢性病管理、文档处理、行政事务，以及各种行政工作流和后台工作流——目前有大量资金花在一些可能并不需要如此花费的地方。因此，我们非常看好未来几年整体支出会有所压缩。我认为这种情况目前还没有发生，也不认为仅仅让医疗智能变得普及就会带来这种变化。我认为，这需要通过集成、工作流优化、部署和规模化来实现，并确保这些模型在用户使用它们处理的、具有经济价值的任务中真正发挥价值。但我认为，这是实现这一未来的前提。if you look at like the whole spend of health. There is quite a bit of areas though where if you look at for example chronic care management or documentation or administration or a lot of kind of administrative workflows of various kinds, back office workflows, there's a lot of money spent in ways that probably don't need to be spent that way. and so we are very optimistic about kind of an overall compression of spend over the coming years. I don't think that's happened yet and I don't think that just happens from making health intelligence abundant. I think that happens from integration and workflows and deployment and scaling that up and really making sure these models are valuable in the economically valuable tasks that people are using them for. But I think it's a prerequisite to making that future happen.

主持人：我们大致正朝着“更多医疗关怀，更少文书工作”的方向前进。至少，这能节省不少成本。我想你也看到了过去几周的一些头条：有篇《纽约时报》的文章，大意是医疗服务提供方的 AI 和付款方的 AI 正在相互对抗；还有蓝十字蓝盾协会的一项分析显示，双方的计费机器人和文书处理机器人实际上让支出增加了大约 10 亿美元之类的。当你在医疗领域开展前沿研究，努力将医疗智能的成本降到接近于零，却又看到这样的头条时，你怎么看？Host: More care, less paperwork is sort of where we're heading. At least there's a lot of the cost savings. I'm sure you saw some of the headlines over the last few weeks of there's a New York Times article that basically provider AIS and payer AIs are fighting and there was some Blue Cross Blue Shield Association analysis that showed that the respective billing bots and paperwork bots effectively increased spend by a billion dollars or something like that. when you are doing cutting edge research on health and trying to bring down the cost of medical intelligence to near zero and then you see headlines like that, what do you make of that?

嘉宾：我认为，这可能是这样一个领域：围绕医疗服务提供方与支付方之间的军备竞赛，短期内可能会有一些阵痛，但从长远来看，这种情况没有理由非得存在，对吧？所以……Guest: I think this might be one of those areas where there might be some kind of short-term pain around this kind of provider payer arms race, but in the long run there's no reason this has to exist, right? And so

真的，我觉得你知道，你正在进入这样一个世界：很多就诊过程中，诊室里都有某种环境智能在聆听并转录这些内容。这里有笔记、临床记录，还有 AI 对临床记录细节进行裁定之类的东西。所以，你掌握了很多裁定就诊情况所需的细节，对吧？在我看来，理想的情况是，医疗服务提供方和支付方能够达成共识，比如说：“这是我们对这次就诊实际情况的准确记录。这是我们掌握的所有信息。”除此之外，就没有其他信息来源了。re really I think you know you're entering a world where a lot of visits actually have some kind of ambient intelligence in the room listening, transcribing things like this. there is notes, there's clinical notes, there's AI adjudicate AI details of clinical notes and things like this. And so you have a lot of the details you need to adjudicate visits, right? And so the ideal world in my view is a world in which providers and payers kind of have a handshake like, hey, here's our ground truth of what happened in this visit. Like here's all the information we have. There's just like no other source of information that there is,

对吧？right?

然后由 AI 协助裁定这种情况。也许会有一些有限的申诉流程之类的，但我认为没必要搞出一整套流程，让支付方和服务提供方之间陷入 AI 军备竞赛。这些完全可以避免。所以我认为这只是短期内有点分散注意力的事情。And then an AI helps adjudicate that situation. And maybe there's like some limited appeals process or things like this, but I don't think you need to have all this process where you have like an AI arms race between payers and providers. I think they can just be completely avoided. So I view that as a bit of a short-term distraction.

主持人：不，很多创业者正在打造医疗健康领域的技术，他们很可能正在观看这段访谈，也许正试图弄清楚：随着你不断推进这项工作，把医疗智能的成本降到接近于零，它会如何变成一种无处不在的基础服务，贯穿消费者、临床医生以及医疗技术或医疗体系中行政管理环节的所有互动。Host: No, there's a lot of entrepreneurs that are building technology and healthcare that are likely watching this and they're probably trying to figure out you know as you continue to advance your work and you bring down the cost of medical intelligence to near zero and this becomes this sort of ubiquitous utility that flows through all of our healthcare interactions for consumers, for clinicians, for the administrative part of the healthcare technology or the healthcare stack.

主持人：大家应该基于此构建什么？假设它会变得比现在好 100 倍以上，专业应用应该做些什么？Host: what should folks be building on top? What should specialist applications be doing assuming that this gets 100x plus better?

嘉宾：是的，这是个很好的问题。正如我提到的，我们认为，OpenAI 对健康领域产生的很多影响，并非直接来自我们提供的产品，而是通过合作伙伴，以及医疗服务交付的最后一公里来实现的。Guest: Yeah. it's a great question. Like I mentioned, like we think about a lot of our health impact through OpenAI as not being directly through the products that we offer, but actually through our partners and that last mile of delivering care.

所以，你知道，我想对我们的合作伙伴说的是，要基于真正强大的模型来构建。最重要的是，要相信这些模型会变得更强大。所以，要着眼未来。我认为，医疗领域的很多人都处在某种光谱上：有人押注模型会失败，有人持中立态度，也有人选择与模型同行，或看好模型。我想说的是，要相信模型会越来越好。这大概是最重要的一点。还要思考正在开发的许多技术——无论是智能体、计算机操控，还是其他前沿技术——将如何推动医疗领域的发展，并押注于此，为这样的未来做好准备。我还认为，借助 AI，很多医疗服务实际上会变得更容易被患者获得。And so, you know, what I'd say to our partners is, you know, build on top of models that are really capable. And the most important thing is bet that those models will become more capable. So bet on the future. I think many people in healthcare are on some kind of spectrum between betting against the models, betting neutrally for the models and betting with the models or betting for the models. And I think I would say you know bet on the models getting better. That's probably the most important thing. And think about the ways in which a lot of the technology that's being developed whether that's you know agents or computer use or other kind of frontier tech will accelerate healthcare and bet on that and build towards that future. I also think a lot of the medical care will actually like become more accessible to patients with AI.

你知道，我们之前谈到过一个想法：由一个类似“总协调人”的角色来统筹，并消除许多障碍。因此，专注于提供真正优质的护理，并让 AI 参与监测整个护理历程，我认为这也是其中非常重要的一部分。You know, we talked about this idea of kind of a quarterback and dissolving a lot of these barriers. And so just being focused on delivering really good quality care and having AI be part of, you know, monitoring that care journey, I think is a very important part of that as well.

程序性护理及后续安排Procedural care and what's next

主持人：把视角拉回整个医疗体系，放眼来看，你知道，我们谈到了这5.3万亿美元的支出。其中很大一部分是医疗服务提供方、支付方以及医疗领域“五个P”中的其他利益相关方之间的文书工作。还有很大一部分是认知型医疗服务，而你认为这类服务可以通过技术来增强和提升；某种程度上，我们可以让更多认知型医疗服务能力上线，这令人振奋，尤其是从改善服务可及性的角度来看。医疗支出中还有很大一部分属于操作性医疗，要么需要进行一些影像检查、血液检测或后续检查，要么需要某种外科干预。你认为你们在医疗领域开展的智能技术工作会如何影响操作性医疗？这最终会成为一个——会成为新的瓶颈，或是加剧现有瓶颈吗？还是说你们预计也会在这方面开展大量工作？Host: Zooming out to the overall healthare system and now just looking at it at large, you know, we talked about this 5.3 trillion. A lot of that spend is paperwork between providers and payers and other stakeholders within the five Ps of healthcare. A lot of that is cognitive care that you're arguing can be augmented and enhanced and sort of we can have bring on a lot more cognitive care capacity online which is exciting and exciting from an access perspective. And then there's a lot of spend in healthcare that is procedural either requiring some imaging, some blood work, some follow-on examination or it's some surgical intervention. where do you see sort of the intelligence work that you're doing in the medical space impacting procedural care? And is that going to be the ultimate is that going to be the new sort of or an enhanced or perhaps an exacerbated bottleneck or do you expect to do a lot of work there as well?

嘉宾：机器人学等。Guest: Robotics etc.

嘉宾：是的，我的意思是，我认为短期来看，在程序性护理方面，需要时应该更精准地进行干预，对吧？我也认为，人工智能可以提供有用的信息，帮助人们判断何时需要寻求这类护理。Guest: Yeah, I mean I think in the short term with procedural care, I think you want a better targeting when that's needed, right? And I think AI can help, you know, provide information that is helpful for knowing when to seek that kind of care as well.

我猜，未来一两年里最大的宏观因素，可能就是更好的主动预防措施，对吧？这样你之后就不必真的去做那项治疗了，因为如果你能早点采取正确的行动，再加上 AI 帮你规划整个过程，就有望避免后续很多最昂贵治疗所带来的开支。所以我猜，这可能就是值得关注的一个宏观因素。Probably the biggest macro factor in the next, you know, year or two will just be better kind of proactive preventative stuff, right? So you don't have to actually go in later and get that procedure right because if you make the right moves sooner and AI helps you navigate that kind of journey then you can hopefully avoid a lot of the downstream cost of the most expensive procedures. So that might be like one macro factor to kind of look out for is my guess.

我认为，机器人技术与护理相关的问题，从技术角度，以及报销和行业整合的角度来看，都是一个更长期的问题。我预计这不是未来几年内就能实现的事。I think the question of kind of robotics and care I think is a bit of a longer term one both from a technology perspective and kind of like a reimbursement and kind of industry integration perspective. My expectation is that it's not like a next few years kind of thing.

主持人：非常棒。好，我们已经谈了很多内容。我们谈到了智能的提升，谈到了信任，也谈到了从医疗信息到医疗服务的转变。随着事态逐步发展，智能体、个人智能体以及底层模型的性能不断提升，我们可以期待 ChatGPT Health 带来什么？Host: Very cool. Okay, we've covered a lot of ground. We've talked about intelligence improving. We've talked about trust. We've talked about moving from medical information to medical care. Help us understand what we can look forward to as things as sort of things continue to unfold. as agents and personal agents and the underlying models become increasingly performant, what can we look forward to from ChatGPT health?

是的，我的意思是，我觉得我们真正专注于实现的，主要有两件高层次的事情。首先，再次思考我们可以通过哪些方式提升人类健康的底线，让所有临床医生、用户和患者都能广泛获得可信、可靠的健康智能。我们的目标是达到这样一种程度：正如我们所说，所有患者和临床医生都会因为 AI 融入就医过程而感到更安心——比如看到 ChatGPT 时会更安心，医生使用 ChatGPT 时也是如此。Yeah, I mean I think there's like really two highle things that we're really focused on making happen. One is again thinking about like the ways in which we can raise the floor of human health and make trustworthy reliable health intelligence abundant for all clinicians and all users and patients. And so getting to the point where you know like we talked about like all patients and clinicians just feel safer by having AI in the care journey like when you see the chatpt you actually when the doctor's using chach you actually feel safer and vice versa.

要达到那个阶段，再说一次，这不仅取决于模型是否足够好、评测是否到位，还取决于我们如何部署、部署所需的启动门槛，以及产品等方面。我认为第二件事，是认真思考如何提高上限：AI 有哪些方式能够真正做到以前做不到的事？我认为其中一个例子，就是这种积极主动的健康管理助手，它是你整个健康护理过程的一部分，帮助你应对这些时刻，充当你的统筹者，帮你安排日程等等；同时也能指出你今天可以采取哪些措施来改善健康状况，从而避免未来出现许多代价高昂的健康恶化问题。getting to that point and again that's a question of not just you know the models being good and eval being good but also how we deployed and the activation energy around that and the products and things like this I think a second thing is really thinking about raising the ceiling so what are the ways in which AI can actually do things that were not possible before so one of them actually I think is this kind of like you know proactive health proactive personal health assistant that is part of your care journey is helping you navigate these moments being your quarterback you know helping you schedule and all these things but also flagging the places where you know you can intervene in your health today and prevent a lot of know expensive deterioration later in the future.

我认为，这将是我们思考如何改善未来人类健康、让尽可能多的人受益时一个很重要的部分。第二点就是你刚才提到的：认真思考我们如何释放现有数据的价值——这些数据有时被存放在各种各样的“盒子”里——并思考如何利用这些数据产生新的见解，让患者受益。and I think that's going to be an important part of how we think about you know improving the future of health for people as many people as possible. And I think the second thing is just what you mentioned before which is like really thinking about the ways in which we can unlock the value of existing data that is sometimes stored in boxes of various kinds and thinking about the ways in which that can be leveraged to produce new insights that benefit patients.

主持人：想到 ChatGPT 大概是三年前推出的，真是令人震撼、不可思议，也让人心生敬畏。在这么短的时间里，我们已经取得了巨大的进步。所以，哪怕只是谈谈未来五年或十年，都感觉有点像纸上谈兵。不过，你能给我们一些预测吗？我是说，今天听起来还像科幻小说里的功能，有没有可能在未来三年、五年或十年后成为现实？Host: It is striking and incredible and humbling to realize that ChatGPT I think came out three years ago roughly. and so it's been a lot of progress in a very short period of time. So to even talk on a five or a 10-year basis feels academic but can you give us some predictions? I mean is there a feature that we might have that feels science fiction today but we could have one day 3 5 10 years from now?

是的，我的意思是，如今预测未来真的非常困难。所以这个问题不太好回答，但我想说的是，在未来5到10年这个时间范围内，可能对人类健康产生最大影响的，应该是一些新疗法，而这些疗法或许来自两个方面。一个是利用AI引导生成新的靶点或分子，这些靶点或分子可能适用于开发新疗法；另一个是利用AI引导优先级排序和验证。我们已经到了这样一个阶段：人们希望推荐用于临床试验等研究的分子和药物数量，远远超过临床试验可容纳的名额。所以，我们需要思考模型如何帮助确定优先顺序、开展这些临床试验，让试验推进得更顺畅、更快速。Yeah, I mean is it is super hard to predict the future these days. So this is a difficult one, but what I would say is like I think the thing that might have the most impact on human health in that kind of time frame in the next 5 to 10 years is probably going to be newer treatments that come out of maybe two things. One is AIG guided generation of new targets or molecules that could be interesting for you know for new treatments and the second is AI guided prioritization and validation. So thinking about the right ways, we're already at a point where the number of molecules and drugs that people want to suggest to be used in clinical trials and things like this is way more than the number of slots available in clinical trials. So thinking about the ways in which models can help prioritize and run these clinical trials so that they just run smoother and faster.

我猜在那样的时间范围内，你说的可能是更好的药物、为人们量身定制的药物之类的东西。而这只有在你投资了那两大支柱之后才有可能。My guess is in that kind of time frame, you're talking about kind of like improved medicines, more personalized medicines for people, things like this. And that will only be possible because you invested in those two pillars.

主持人：太棒了。好了，我们聊了很多内容。结束这次对话时，我比之前更加乐观了。Karen，非常感谢你来做客 A16Z 播客。Host: That is awesome. well, we've covered a lot of ground. I'm leaving this conversation even more optimistic than I was before. so Karen, thank you so much for coming on the A16Z podcast.

感谢邀请我。Thank you for having me.