OpenAI 健康 AI 负责人：你该信任 ChatGPT 吗？

大家好，欢迎回到播客。我是 Jay Rugani，a16z 的合伙人，负责医疗健康和生物科技公司的投资。今天和我一起的是 OpenAI 的 Karan Singhal。他在 OpenAI 领导健康 AI 研究，我们有很多要聊的。感谢你的到来。

Hey everyone, welcome back to the podcast. I'm Jay Rugani, a partner at a16z, leading investments in health care and biotechnology companies. I'm joined by Karan Singhal of OpenAI. He leads health AI research at OpenAI, and we've got a lot to talk about. Thanks for being here.

感谢邀请。

Thanks for having me.

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 询问健康相关问题。他们在问什么？他们问的哪些问题是从我们主流医疗系统得不到的？

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-related questions. What are they asking? What are the kinds of questions they're asking that they're not getting from our main health care system?

好的。让我先宏观地谈谈我们为什么投资健康领域。OpenAI 的使命是确保 AGI 惠及全人类。在高层次上，我们通过三种方式实现这一点。第一是构建和部署 AGI，即构建强大的模型、围绕它们构建产品、部署它们、建立合作伙伴关系。第二是防止负面影响，即各种类型的安全。第三是创造积极影响。我们认为健康是一个非常具体的方式来实现积极影响，同时也能解决医疗保健中出现的一些安全问题。因此，我们真正致力于确保两件事。一是提高人类健康的下限，让人们能够免费或几乎免费地获得可信赖、丰富、可靠的医疗智能，同时也提高上限，使以前不可能的事情成为可能。要看到提高下限的最佳方式，就是思考我们在消费者领域的工作。正如我们报道的，每周有超过 3 亿人使用 ChatGPT 询问健康和保健类问题，我们认为这是一项极其重要的责任，必须做好。而且这个数字一直在快速增长。我想你会发现，即使在美国，即使在湾区，如果你想预约专科医生，有时可能需要数周或数月。你可能会花很多时间弄清楚如何预约、安排时间等等。经过所有这些时间，也许只能和医生交谈 15 分钟，你感觉没有充分准备，离开时可能想：“哦，我还有六个问题想问。”我们希望 AI 能成为你护理旅程的一部分，健康不应仅限于这 15 分钟的间隔。AI 实际上可以帮助你导航你的护理旅程。这反映了人们今天在消费者端使用 AI 的方式。人们带着问题和担忧来，想要获得信息。他们想弄清楚如何导航就诊、提问，或者弄清楚如何跟进医生告诉他们的事情等等。我们发现，在就诊之间，AI 在消费者端能最大程度地帮助人们。当然，我们也在临床医生端和企业端进行类似的投资。

Yeah. Let me zoom out and talk a little bit about why we're investing in health in the first place. So, you know, OpenAI'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, 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. And so the best way to see the kind of the raising the floor is really to think about how we've been working in the consumer space. And so as we've 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." 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 the doctor told them or things like this. And we're finding that in between these visits is when AI can help people most in the consumer side. And you know, we're making similar investments on the clinician side and on the enterprise side as well.

是的，太棒了。我们有一个非凡的医疗系统。如果我病得很重，没有哪个国家比美国更让我愿意待着，然而我们却有大量的稀缺。你知道，你见过一些统计数据，超过 1 亿人无法获得常规初级保健医生。即使有，也经常需要等待 40 多天才能见到那位医生。有时你见到医生，正如你所说，是快速的 15 分钟就诊，而你还有更多问题。我上周因为某事看了皮肤科医生。我事先在 Apple 备忘录里写下了所有问题，但还是忘了问一些事情，现在我在预约复诊，又要等 4 个月。这发生在我们所有人身上。我们都感受过。有没有一个具体的患者故事或最近的案例，能帮助我们想象世界将走向何方，以及你的工作将带我们走向何方？

Yeah, it's amazing. We have an extraordinary healthcare 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 have 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 15-minute 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 4 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?

我们团队的工作令人欣慰的一个方式是看到数字，看到有多少人在使用这项技术，以及它对人们似乎有多有用。另一个方式是听到人们的反馈，对吧？我们有人进来，给我们发邮件，或者以各种方式联系我们，每周多次，说这如何帮助了我。我想我最受触动的一个故事是 Nathan Labenz 和他儿子的故事。他主持 Cognitive Revolution 播客，他在那个播客中讲述了这个故事，但他基本上和他儿子经历了一段漫长的历程，他的儿子非常年幼，在某个时候遭受了很多疲劳和易怒等症状。AI 帮助他们在度假时意识到这是一件相当严重的事情，并决定带他去检查。

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 got 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 is helping me. I think one of the stories that I was most touched by was the story of Nathan Labenz and his son. So he hosts the Cognitive Revolution podcast, and he's kind of told the 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 irritability 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.

你们在心理健康方面做的一些工作，有很多复杂性。鉴于这可能意味着很多事情，你如何帮助引导用户？

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?

我们一直在与世界各地的一些持证心理健康专家合作，思考在一系列棘手场景中定义模型行为的正确方式，并确保我们能够达成一定程度的共识，从而实际衡量性能。

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 in a bunch of tricky scenarios, and ensure that we can get to a level of consensus, where we can actually measure performance.

让我们谈谈信任。

Let's talk about trust.

一个人可能在与 LLM 交谈时真的希望听到一切都好。但从医学角度来看，他们可能需要听到非常不同的东西。你如何教 AI 识别这种差异？

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?

结果他被诊断出患有一种侵袭性白血病，在整个治疗过程中，Nathan 说他当时大量使用 GPT-5 Pro 来帮助梳理流程、理解化验报告等，甚至在专家有时间帮忙解读之前。我觉得这给他们带来了很多安心。Nathan 实际上在他的播客里讲述了那个故事，我推荐大家去听听。

It turns out he was diagnosed with an aggressive form of leukemia, and throughout his care journey, Nathan reports using GPT-5 Pro at that time quite a bit for helping navigate the process, helping understand lab reports and things like this even before a specialist had time to help interpret. And I think it brought them a lot of peace of mind. Nathan actually tells that story in his podcast, and I would recommend that for folks.

从很多方面来说，OpenAI 在每一步都将健康智能构建到你的模型中，但人们也用同一个模型来写代码或规划假期。你能从架构角度帮助大家理解为什么你要这样构建吗？为什么通用人工智能是医学这样复杂领域的正确基础？

In many ways, OpenAI is building 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?

是的，人们经常问我们关于通用模型与专用模型的争论。我之前的工作中曾研究过专用模型，所以我在这问题上确实有双方的经验。我认为这里最重要的是，我们谈论的实际上是在通用模型中进行专门训练，这其实是两全其美。对于 ChatGPT 以及我们在 ChatGPT 中发布的所有模型，在模型训练的每个阶段，我们实际上在去年所有模型的每个训练阶段都加入了专门的医疗数据。因此，你得到一个既受益于大型前沿模型在智能和推理方面的所有规模和通用改进，又受益于专门训练的模型，例如通过整合我们与全球众多医生合作获得的数据。所以我认为这种方法能让你两全其美。如果你分叉说，‘嘿，我们要训练一个只针对医疗保健的较小专用模型’，就像我以前和许多人做过的那样，你会发现那个模型会相对较快地落后于前沿，因为前沿和通用模型改进的速度太快了。如果你押注于规模，我们 OpenAI 作为一个行业发现，事情会变得更好。所以我们所做的是搭上规模前沿的顺风车，并利用我们能够将数据和训练数据整合到模型中的能力，使模型既专业化又受益于通用能力。

Yeah, people often ask us about this general versus specialized model debate. I used to work on specialized models in a previous role, so I definitely have experience on both sides of this question. I think the most important thing here is that what we're talking about is actually specialized training in a general model, which is the best of both worlds. For ChatGPT and all the models we ship in ChatGPT, across every stage of model training, we actually have specialized healthcare data added to each phase of training since all of our models in the last year. As a result, you have a model that benefits from all the scale and general improvements in intelligence and reasoning of the big frontier models, and also benefits from the specialized training we get from, for example, integrating data from working with a bunch of physicians across the globe. So I think you get the best of both worlds with this approach. If you forked and said, 'Hey, we're going to train a smaller specialized model that is just for healthcare,' like I used to and like many people have, what you'd find is that model would fall behind the frontier relatively quickly because the pace of the frontier and general model improvements is so fast. If you bet on scale, what we found as OpenAI as an industry is that things will get better. So what we're doing is hitching a ride on that scale frontier and leveraging the fact that we can integrate data and training data into the models to make the models both specialized and benefit from general capability.

我几乎想开始想象你们正在构建的技术能实现什么。显然，今天已经很惊人了，但你们没有停下，还有很多工作要做。我认为很多人被口袋里的私人医生的想法所吸引。一个知识渊博、拥有所有医学专科知识的东西，这样你就不必为不同的问题去看心脏病专家和皮肤科专家。一个完全个性化为你服务的东西，拥有你所有的病史背景，记住你说过的一切，记住你的病史和偏好，还能激励你、提醒你，以便在正确的时间以能促使你采取更健康行为或遵守医生方案的方式向你提供信息。这是你们在 OpenAI 最终要构建的体验吗？我们离那有多近？

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 lured 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. Is that sort of the experience you're building at OpenAI in the fullness of time, and how close are we to that?

是的，这是我们非常兴奋的事情。我们对这种主动式个人健康助手的愿景非常兴奋。我不认为这能替代实际拥有医生、能接触到医生，但真正思考如何将获取健康信息的体验变得更个性化于你的背景，是我们对健康和 ChatGPT 投资的重要部分。ChatGPT 是我们拥有的消费者产品，它使用户能够连接他们的健康信息，以便信息安全使用，不用于训练数据或定向广告等。这是我们如何迈向越来越个性化访问和主动访问世界的重要部分。这也是我们一直思考部署的方式。所以我们从一开始就尽可能广泛地部署给免费用户和付费用户。我们有大约 3 亿每周活跃健康用户，其中绝大多数是免费用户，而且大多数在美国以外。

Yeah, that's something we're really excited about. We are very excited about this vision of a proactive personal health assistant. I don't think that's any replacement for actually having a doctor, 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 your context is a really important part of our investment in health and ChatGPT, which is a consumer offering that we have that enables users to connect their health information for that information to be used securely, not to be used for training data or for targeting ads or anything like this. And that's a big part of how we think about how we're going to move towards this world of 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.

嗯。

Mhm.

所以我们非常重视这里的分布问题，将其作为核心。因此，当我们思考主动式个人健康助手的下一个前沿时，我们也会将这一点考虑进去。

And so we take that question of distribution here really important, really centrally. And so as we're thinking about this next frontier of a proactive personal health assistant, we're going to bring that to bear as well.

我认为用例能帮助人们想象什么是真正可能的，所有这些工作正在贡献什么。口袋里的私人医生的想法我认为很令人兴奋。当 Greg Brockman 来播客时，他谈到了他朋友的一个情况：他朋友在医院，她的医生正要给她用某种药。她在 ChatGPT 中再次核对了那种药，根据她的病史意识到她可能会有药物相互作用或由此产生的不良事件。她把这一点给她的医生看。她的医生同意并说：‘我没有足够的时间查看你的病史来注意到这一点。非常感谢你提出这一点。’我们不应该一直为每个人这样做吗？如果足够便宜和方便，我们难道不能为每个临床场景做第二、第三、第四意见吗？这就是我们要去的方向吗？

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 his was in the hospital and her physician was about to administer a certain medication. She 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. 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? 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?

是的，我喜欢这个为患者和临床医生提供安全网的想法，它就在护理旅程中，并不总是干扰或充当中间人，而是真正思考如何增强现有体验并在此基础上构建。

Yeah, I love this idea of a safety net for both patients and clinicians that is just there in the care journey and not always interfering or being the middleman, but really thinking about the ways in which you can augment the existing experience and build on it.

我们实际上更直接地在临床医生方面进行了这项研究，在与 Panda Health 合作的一系列实验中，我们研究了临床医生副驾驶这一概念，它正是以这种方式部署的——作为临床医生工作时的安全网。它会主动介入并建议，比如：‘嘿，这可能有问题；可能存在您未注意到的药物相互作用。’

We did this work actually more directly studying this on the clinician side in a series of experiments that we ran with Panda Health, where we studied this idea of a clinician co-pilot that was deployed exactly in this way—as a safety net for the clinician as they were doing their work. It came in and proactively suggested, like, 'Hey, this might be off; there might be this medication interaction that you might not be tracking.'

嗯。

Mhm.

我们能否调整一下这个以及类似的事情？我认为在患者方面也会发生同样的情况，我对在您的护理旅程中有一个主动的智能体真正帮助您感到非常兴奋，并确保比如如果您是糖尿病患者且糖化血红蛋白升高，您会得到医生对您药物的复查等等，并确保标记出来进行适当审查。

Can we 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 a proactive agent in your care journey really helping you and making sure that, like, if your A1C rises and you're diabetic, you get a review of your medications by your doctor and things like this, and making sure that's flagged for appropriate review.

你提到了个性化主动智能体。个人智能体总体上在过去几个月里从受欢迎程度来看已经涌现——Instinct、Muse、Grok Bot、OpenAI 的 dots。对于不熟悉的人来说，你可以给这些 AI 应用一个目标，它就能执行一系列任务。所以不仅仅是‘嘿，做这个任务’然后它用单次循环执行任务。它可以使用你的偏好，它可以访问你授予权限的所有应用，如果你提供信用卡信息，它也可以访问，并且它可以代表你执行目标。人们不禁会想象在导航我们美妙但往往支离破碎的医疗保健系统时的可能性：预约并确保你的保险覆盖那次预约或支付那次预约，帮助和指导你遵循医生在出院后给你的护理方案，甚至与保险公司就医疗账单错误进行谈判。所有这些任务都在我们的医疗保健系统中制造摩擦。

You mentioned personalized proactive agents. Personal agents in general have emerged on the scene from a popularity perspective over the last few months—Instinct, Muse, Grok Bot, OpenAI's dots. For folks who are not familiar, you can give these AI apps an objective, 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 healthcare system: scheduling an appointment and making sure your insurance is covered for that appointment or covering that appointment, 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.

是的。

Yeah.

这是你的工作方向吗？我们应该期待 OpenAI 在这方面的发展吗？

Is that where you're kind of going with your work? Should we look forward to that from OpenAI?

我对这个愿景非常兴奋。我认为如果你从患者和临床医生的角度来看，医疗保健系统及其服务人们的最困难的事情之一就是生态系统的碎片化和缺乏连接。医疗保健也是那些你不应该期望明天所有东西都有 MCP 连接器的行业之一，对吧？这是一个有遗留软件的地方。首先，与 AI 或软件的连接性不一定是最好的，而且许多人在医疗保健生态系统中在虚拟机上运行他们的主要电子健康记录软件，例如。所以，就像——

I'm very excited about that vision. I think if you look at from both the patient and clinician perspective, one of the most difficult things about the healthcare system and how it serves people is this question of fragmentation and 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 necessarily the best connectivity with AI or software in the first place, and many people are running their primary EHR software in the healthcare ecosystem on a VM, for example. So, like—

他们在云中运行的虚拟机，是的。

Virtual machine that they're running in the cloud, yeah.

没错。所以实际上有一个有趣的组成部分，虚拟机在某些方面实际上使 AI 智能体更容易，但这绝对是智能体和计算机使用的成熟领域。所以我绝对认为这是一个地方，传统上存在于患者获得所需护理、临床医生提供所需护理以及医疗保健数据之间的许多障碍，这些数据往往是整个系统为患者良好运作的瓶颈。

Exactly. And so, there's actually an interesting component where a virtual machine actually makes things easier in some ways for AI agents, but this is absolutely ripe for agents and computer use. So I definitely think that this is 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 a bottleneck to the whole system working well for patients.

好的，所以我们正在讨论用例。你帮助我们梦想梦想。在我们继续沿着这条路走之前，让我们谈谈信任。显然，信任在这里是最重要的。同一个底层模型可以帮助我理解我的实验室结果或我从医生那里听到的一些东西，但它也可以帮助一位训练有素的专科医生理解一个非常复杂的病例。你在答案中改变了什么，以便对这两个角色或任何其他可能使用同一底层模型的角色来说都是可读的？

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?

是的。我的意思是，正如你所说，健康是一个以信任的速度发展的领域。所以，你不仅需要能够使模型更好并衡量它们，还需要沟通并带动世界前进。因此，这一直是我们思考和战略中非常重要的一部分，无论是我们如何与患者合作，如何与最终用户合作，还是如何与临床医生合作。所以，对你问题的简短回答基本上是：我们通过与世界各地的专家合作并以各种方式获得反馈来评估和训练模型，以调整行为，使其适应一个人是普通用户还是已经在一段时间内导航其护理、复杂护理旅程的专家型普通用户，或是临床医生或其他类型的用户角色。因此，甚至对于我们早在 2025 年 5 月发布的原始 Health Bench 评估，实际上有一个主题或评估的一部分真正专注于模型如何表现和调整其使用技术术语的程度等，以区分一个人是普通用户还是临床医生。从那时起，我们在衡量模型是否能够迎合不同种类的用户方面都变得更好，并且也在朝着这个方向训练模型。所以，我们的很多方法实际上不仅是如何为这些不同的用户使模型更好，而且是如何带动他们。其中一部分就是我们如何进行评估并沟通它们。

Yeah. I mean, as you said, health is a place where 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 communicate and bring the world along. 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 tailor the behavior to whether a person is a lay user or an expert lay user that has been 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 Health Bench 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 use of technical jargon and things like this to whether a person was a lay user or clinician. 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 not only 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.

所以，信任有几个组成部分。我们已经讨论了根据用户定制答案。另一个重要组成部分是分歧。一个人可能与 LLM 或聊天交流并分享他们所有的症状，但真的希望听到一切都没问题。所以，从消费者用户 NPS（净推荐值）的角度来看，他们想听到安慰，但从医学角度来看，他们可能需要听到非常不同的东西。

So, there's a few components to trust. So we've talked about 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.

你如何教 AI 识别差异并管理那里不同的产品目标，无论是从消费者参与度和保留率，还是从正确性和临床质量方面？

How do you teach an AI to recognize the difference and manage different product objectives there, either from consumer engagement and retention versus correctness and clinical quality?

是的，这对我们如何进行评估和训练都非常核心。

Yeah, this is super central to how we do both evaluation and training.

那么，稍微拉远一点看，关于我们如何回应、如何评估的一致与分歧问题——你可以做的一件事，也是你可以作为方法基础的一件事，是我们可以请几位临床医生加入，比如说 OpenAI 健康团队，然后说：‘嘿，你们来想办法处理这些不确定的病例，我们会向你咨询这些不确定病例，你们帮我们构建评估和训练数据等等。’然后让那几位医生去解决。这其实并不是最好的方法。

And so, zooming out a little bit, the question of agreement and disagreement around how we respond and how we evaluate—one thing you could do, and one thing you could base your approach on here, is we could bring in a few clinicians into, let's say, the OpenAI Health team and say, 'Hey, you guys figure out how to navigate these uncertain cases, and 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. This is actually not the best approach.

所以，事实证明，如果你问五位医生任何问题，你可能会得到八个不同的答案。因此，我们在 OpenAI 刚开始关注健康领域时做的第一件事，就是真正建立这个全球医生网络，有数百名专家，我们在全球范围内合作，跨越数十个专科、语言和执业国家，思考如何正确聚合专家的智慧，这样我们就不会依赖——尤其是在可能存在分歧的领域——我们不会依赖一两位或三位医生来裁决。所以这是一部分：考虑评估和训练数据来自不同的专家，而不是依赖一两个人。

So, it turns out if you ask five physicians any question, you'll get like eight different answers. And so, one of the first things that we did when we first started focusing on health at OpenAI was really build this kind of global physician network of hundreds of experts that we work with across the globe, across dozens of specialties and languages and countries of practice, to think about the right ways to 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 adjudicate. So that's one part of it: thinking about evaluation and training data coming in from different experts rather than relying on one or two people.

但你问题的第二部分是：你如何平衡相互竞争的目标？假设医生和医生的集体智慧指向一个方向，而用户可能实际上希望你说些安慰的话，即使情况并不令人安慰。这基本上是安全性的优先级问题。因此，对我们来说非常重要的是，当有数亿次健康对话涌入时，我们非常认真地对待我们的义务。所以，在整个评估栈和训练栈中，我们有评估来跟踪这些困难情况，在这些情况下，模型实际上应该去说：‘嘿，这不安全。你应该寻求医疗帮助。’即使使用者不想听，或者想获得某种替代疗法之类的。这是我们进行评估的重要部分，随着时间的推移，我们不仅通过与医生合作改进了评估方式，而且在那些领域看到了模型整体的显著改进。

But then the second part of your question is: how do you balance competing objectives, right? Let's say the physicians and the aggregate wisdom of the physicians is pointing in one direction, and then maybe the user actually wants you to say something comforting even when the situation's not comforting. It's basically a prioritization of safety. And so, it's very important to us that we take our obligation very seriously when there's hundreds of millions of health conversations coming in. And so, across the evaluation stack and training stack, we have evaluations that track these difficult situations where the model should actually go and say, 'Hey, this is unsafe. You should seek care.' Even if the user doesn't want to hear it or wants to get some kind of alternative treatment or something like this. And that's a very important part of how we do evaluation, and over time we've not only improved how we've done that evaluation with working with physicians, but also seen pretty dramatic model improvements across the board in those areas.

还有另一个例子或另一种分歧的情况，就是我作为消费者：我向 ChatGPT 问一个医学问题，然后在就诊时把那个回答给我的医生看。顺便说一句，我的医生很喜欢我。然后他们不同意。他们不同意那个回答。我应该如何处理这种情况？

There's another example or another instance of disagreement, which is me as the consumer: 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?

是的，我的意思是，我认为你应该像处理其他方式发生的分歧一样处理它。就像人们确实会在不同事情上产生分歧，我认为作为患者，你有权要求另一位医生看一看、了解一下，并去找另一位医生寻求第二意见。是的，无论你是否同时使用 ChatGPT 获取健康信息，我都会提倡这样做。

Yeah, I mean, I think you should handle it similarly as if disagreement was happening in some other way as well. Like people do disagree about different things, and I think you have a right as a patient to ask for another doctor to take a look and get a sense, and go to another doctor to get a second opinion. And yeah, that's what I would advocate for whether or not you're also using ChatGPT to get health information as well.

是的，非常有道理。信任的另一个要素就是绝对正确性。而你们所做的很多工作——至少从听你所说来看——似乎都是关于将答案建立在世界医学文献和证据之上。医学的某些部分没有太多一致或不一致，但医学的很多部分仍在被发现或研究中。甚至在医学领域也存在分歧。对于这类问题，当我们仍在发现真理时，很难将其建立在真理之上。科学是一个过程，等等。在这些情况下，你如何考虑正确性？

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—at least from 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 those kinds of questions it's difficult to ground it in truth when we're still discovering truth. Science is a process, etc. How do you think about correctness in those kinds of instances?

是的，这是一个非常好的问题。我的意思是，无论有没有 AI，这都很重要。所以，如果你拉远来看当今医学共识是如何分布的，今天的过程是什么？基本上有一群人撰写论文，各种生物医学论文，提出改进或治疗方法之类的东西，可以提高护理质量。然后你有这个过程，不同专科每年建立临床指南的过程，这些指南描述：‘嘿，在这种情况下，如果你是一名心脏病专家，你看到一位患有 X、Y 和 Z 的患者，以下是你可以做的事情。’所以这些临床指南大致上——你可以把它们看作流程图，每年由世界各地的不同机构发布，大致描述基于最新研究在不同情况下你应该做什么。

Yeah, it's a really great question. I mean, it's important with or without AI. So, if you zoom out and look at how medical consensus is distributed today, what is the process of that today? You basically have a bunch of people writing papers, biomedical papers of various kinds, suggesting 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 describe, '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.' So these clinical guidelines are roughly—you can consider them flowcharts that are put out each year by the different bodies across the world that describe roughly what you should do in different scenarios based on the latest research.

嗯。

Mhm.

然后最后一步是：你有生物医学文献，然后你有临床指南，然后人们在实际中到底怎么做？所以最后一步，事实证明，是一个巨大的瓶颈。有研究关注临床指南渗透到世界各地日常医生实践需要多长时间？过去人们发现，根据研究不同，这种渗透实际发生需要 5 到 17 年。这是巨大的。你不想在医学实践中落后 5 到 17 年。所以我们思考的一件事是，如何将那种过去实践的时间压缩到几周、几天甚至几小时，对吧？所以这是我们 ChatGPT for healthcare 和 ChatGPT for clinicians 工作的一个非常重要的原则，这些是面向临床医生和医疗专业人员的服务，我们在护理时刻引入最新的生物医学文献，让人们能够在就诊时，比如 15 分钟的就诊过程中，非常快速地得到他们问题的答案。所以这是回答问题的一种方式，即：在实践中，虽然许多领域缺乏医学共识，但在某些情况下存在医学共识，但没有被实践。所以你可以做的一件事就是真正大幅减少这种情况的发生。这是回答你问题的一种方式。

And then the final step is: you have the biomedical literature, and then you have the clinical guidelines, and then you have what do people actually do in practice? And so the final step, it turns out, is a massive bottleneck. So there are these studies on how long does it take for clinical guidelines to penetrate the practice of everyday doctors across the world? 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. 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 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 a 15-minute visit at the point of care, get answers to their questions. So that's one way of answering a question, which is: in practice, while there is 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 really dramatically reduce the amount that that's happening. This is one way of answering your question.

回答你的问题的第二种方式，是如何处理那些仍然存在真正医学不确定性的快速场景。我认为有几点。第一，你要训练模型能够识别自身的不确定性。在浏览并获取信息之后，你要让模型知道：嘿，这里的信息就是不够，并且要坦诚这一点。这一直是我们做评估的重要部分。这实际上是 HealthBench 的一个公开方面，其中包括对冲淡表述的评估，比如当存在这种不确定性时。所以这是很重要的一点。第二点涉及如何提高人类健康的上限——真正思考模型如何能够改进科学研究的流程，并推动医学前沿。也许我们稍后会聊到这一点。

A second way of answering your question is how to handle the quick places where there's still genuine medical uncertainty. I think there are a couple of things. One is you want to train the models to be able to recognize their own uncertainty. After browsing and getting information, you want the models to know, hey, there just isn't enough information, and to be up front about that. That's been an important part of how we've done evaluation. That's actually a public aspect of HealthBench, which includes evaluations on hedging, for example, when there is this kind of uncertainty. So that's one important thing. The second thing goes to this question of how you raise the ceiling of human health—really thinking about the ways that models can actually improve the process of doing science and push the medical frontier. Maybe we'll chat about that later.

听你说话，感觉你总是在考虑三个群体之一：患者、注册的个人临床医生，或者企业，无论是医疗服务提供方企业还是其他企业。你如何从架构层面以及从 harness 层面来设计产品，以满足所有这些不同用户的需求？

Hearing you talk, it feels like you're always thinking about one of three constituents: the patient, the individual clinician that signed up, or 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 harness level to meet the needs of all these different users?

是的，我认为这大概有三部分。第一是让模型对这些用户来说真正好用。第二是围绕模型构建合适的 harness 或产品。第三是部署或合作动作，以触达合适的用户。默认情况下，我认为如今很多时候模型已经相当不错了。我们谈过我们的评估方法、与临床医生合作等等。我们看到了性能和可靠性方面相当显著的提升。几个月前我们谈过我们在免费层级上所做的改进，把免费层级的性能带到前沿水平。在每周使用 ChatGPT 的 12 亿人中，我给他们提供了一个在健康领域处于前沿、极其有用的模型，而且是免费的。所以这是我们让医疗健康和健康智能变得充裕的战略的重要部分。第二是我们的产品 offerings。你可以有一个非常好的模型，但如果使用模型来改善健康的激活能太高，人们就不会去用，对吧？所以我们很多产品工作实际上是在思考你提到的这些群体，并思考如何降低人们使用算力和智能来改善人类健康的激活能。这沿着我们在 ChatGPT、ChatGPT 中的健康功能、企业侧的 ChatGPT for healthcare 以及面向临床医生的 ChatGPT 方面的工作展开。我们设计这些 offerings 的很多方式，基本上是思考人们能够使用模型的障碍、瓶颈和激活能是什么。例如，向医疗系统提供企业版 ChatGPT 的安全和合规瓶颈是什么？这是我们两年前思考很多的一个问题。于是我们想，好吧，我们需要投资于安全和合规，我们需要确保能够提供 HIPAA 合规，我们需要确保能够提供医疗机构所需的那种隐私保护，以便我们能够处理他们需要的医疗数据，并提供他们信任的服务。这是我们大约两年前到大约一年前所做工作中非常重要的一部分。然后我们就根据我们看到的采用瓶颈，从那里继续推进。第三是合作与部署。再说一次，几年前我认为 OpenAI 在讲医疗健康的语言方面非常早，当我们与医疗合作伙伴互动时，我认为我们在那些对话中是很早的。所以我认为从那以后我们带来的一个东西，就是真正思考你提到的每一个群体，真正思考如何讲他们的语言，如何带他们走上这段旅程，因为即使模型变得非常聪明，如果没有人真正信任它们或使用它们，那并没有真正实现我们的使命。

Yeah, I think maybe there are 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 the deployment or partnership motion to reach out to the right users. By default, I think a lot of the time the models are often pretty good these days. We talked about our approach of evaluation, working with clinicians and things like this. We are seeing pretty dramatic improvements in performance and reliability. We talked a few months ago about the improvements that we've been making on the free tier and bringing free tier performance to the frontier. Of the 1.2 billion people who use ChatGPT weekly, I'm giving them a model for health that is just incredibly useful at the frontier for free. So that's been a big part of our strategy for making healthcare and health intelligence abundant. The second thing is our product offerings. You can have a model that's really good, but if the activation energy to use a model to improve health is too high, then people won't do it, right? So a lot of our product efforts are really 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. That goes along the lines of our work in ChatGPT, health in 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 what are the barriers and bottlenecks and activation energies for people to be able to use the models. For example, what are the security and compliance bottlenecks in providing an enterprise offering of ChatGPT to healthcare systems? That's one question that we were thinking about a lot 2 years ago. And so we thought, okay, we need to invest in security and compliance, we need to make sure we're able to offer HIPAA 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. That was a very important part of the work we were doing roughly 2 years ago to roughly a year ago. And we kind of just go after there from there based on the bottlenecks that we see to adoption. The third thing is partnerships and deployment. Again, 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 early in those conversations. So I think one of the things that we've brought to bear since then is really thinking about each of those constituents that you're talking about and really thinking about how to speak their language and how to bring them along this journey, because even if the models get really smart, if nobody's really trusting them or using them, that's not really achieving our mission.

这是否在暗示你们与 Epic 所做的一些工作，将产品直接集成到 Epic 中？也许可以多分享一点，因为很多在医疗健康领域深耕的人对看到与 Epic 这种程度的合作非常感兴趣。

And is that alluding a little bit to some of the work you've done with Epic to integrate the product directly into Epic? Maybe share a little bit more about that, because a lot of the folks who have spent a lot of time in healthcare were very interested to see that level of collaboration with Epic.

是的，这是我们方法中非常重要的一部分。我们谈过医疗健康中的碎片化是问题之一，数据和工作流彼此脱节是 AI 在医疗健康中高效使用的一大障碍。我们与 Epic 的合作、与其他合作伙伴的合作——我们最近有一个公告，宣布如果你是提供方或医疗专业人员，现在可以在 ChatGPT 中使用 Epic，同时 ChatGPT 也能在 Epic 本身、在 Epic 工作流本身中发挥作用。你可以有一个小侧边栏，在那里也能使用 ChatGPT。这是一件事。我们还在做其他类型的连接器，最近也宣布了，还有更多即将推出。我们做了相当多的工作来确保我们正在打破这些障碍，让医疗健康生态系统连接起来。我认为 Epic 只是众多例子之一，我们在识别那些瓶颈并试图修复它们。

Yeah, this is a very important part of our approach. We talked about fragmentation in healthcare being one of the issues, and the fact that there is data and workflows that are disconnected from each other is a big obstacle to the productive use of AI in healthcare. Our work with Epic, our 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, in the Epic workflow itself. You can have a little sidebar where you can actually use ChatGPT as well. That's one thing. We're also working on different other kinds of connectors that we've also announced recently, and we have more coming. There is quite a bit of work that we're doing to make sure that we are breaking down these barriers to the healthcare ecosystem being connected. I think Epic is just one of many examples where we're identifying those bottlenecks and trying to fix them.

关于信任，我想谈的最后一点是你们在心理健康方面所做的一些工作。这是另一个非常复杂的领域，除了我们谈到的临床复杂性之外，可能还有其他你需要解决的目标。

The last thing I want to hit on with respect to trust is some of the work that you've done around mental health. 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.

如果我问我的 OpenAI dot：“嘿，我想在接下来几周里感觉好一些。”你们会如何理解这个问题？你们如何帮助采取行动？考虑到这可能意味着很多不同的事情，你们如何帮助引导用户？

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 that could mean so many things?

是的，我的意思是，无论是什么服务，我们都非常致力于在用户遇到困难或处于危机时帮助他们。因此，例如对于 dots，我们一直在该产品服务中投入与所有产品服务相同的保障措施。所以，这是我们方法的重要部分，即一种基线安全底线。然后，你知道，通过心理健康基准，我们真的采取了这种方法，不仅衡量安全底线，还思考模型如何能够主动帮助提高上限，并真正思考模型如何帮助提供积极的心理健康体验，从仅仅担心安全底线转向衡量那些积极方面。所以再次，这种方法实际上非常类似于我们在健康基准上的工作，我们一直在与世界各地许多持牌心理健康专家合作，思考在棘手场景中定义模型行为的正确方式，并确保我们能够达成共识。人们在心理健康方面的分歧甚至比身体健康更大。在一些棘手情况下达成共识，以便我们能够实际衡量性能，并为行业创建这种共享基准。所以我们最近发布了那个基准，类似于 Health Bench 和 Health Bench Professional，目标真的是我们能否创建这个基准，提高所有人的上限，并在所有模型开发者、医疗行业和心理健康专家之间创建这个真正共享的尺子或衡量标准，我们可以用来确保我们朝着正确的方向前进。

Yeah, I mean and regardless of the service, 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 services in that product service as well. So, that's an important part of our approach is a 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 help provide positive mental health experiences and move from just worrying about the safety floor to measuring those kinds of upsides. And so again, the approach is actually very much mirroring our work on health bench where 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 in 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 shared benchmark for the industry. And so we released that benchmark very recently and similar to Health Bench and Health Bench 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.

好的，我们已经谈到了智能以及围绕智能提升而出现的用例。我们谈到了信任，这至关重要。让我们谈谈医疗护理。我想区分医疗信息和医疗护理。我认为对于医疗信息，如果你有健康问题，如果我对自己或正在处理的事情有健康问题，聊天和其他 LLM 越来越成为获得答案和解决问题的更好、更快、更便宜的方式。但如果在仅靠医疗信息无法解决问题的情况下，至少在今天，我就会被推入传统的医疗护理系统。所以如果我需要去拿处方，如果我需要去看面对面的专家，如果我需要做后续血液检查或一些 MRI 或其他形式的扫描，如果我需要临床医生以某种方式检查我，我仍然需要导航传统的医疗保健系统，而很多工作受到美国医疗执照和我们医疗保健系统中其他基础设施的监管。这是多轮对话，不仅仅是单个问题的解决，而是我得去和几个人谈谈。我需要去完成几件事。我们需要收集一些信息。在许多方面，医疗护理是一个捆绑产品。你知道，我们得到诊断、分诊和护理计划。我们从临床医生那里得到同理心、保证和支持。我们得到与此相关的法律责任以及他们承担的责任。AI 会提供所有这些吗？它会在哪里止步？你认为这一切会走向何方？

Okay, so we've spoken about intelligence and the use cases that are coming around intelligence improving. We spoke 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 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 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 healthcare system 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 where do you think this is all going?

是的，不，我不认为 AI 能够或将会提供所有这些，我认为你说得对，健康信息和医疗护理之间存在区别。所以，我认为我们在这里的角色真的是赋能整个生态系统，因为未来不会有任何一家公司提供所有医疗护理，对吧？因此，我们在这里的角色真的是思考医疗护理的最后一英里对人们来说到底是什么样子。我们能否真正理解这一点，并让每个人都能更容易地做到，以便对患者产生实际影响的最后一英里对我们的所有合作伙伴来说就像最后 100 米。所以，这就是我们的思考方式，真正让我们的合作伙伴很容易地使用 AI 来支持护理旅程中的每个人。我认为 OpenAI 在这里可以相对独特地做一些事情，因为它拥有健康的前门。因此，例如，它可以帮助你导航许多这类问题，或帮助你导航这个旅程，或者，你知道，随着我们获得越来越有用的个人智能体，帮助你处理一些日程安排物流等事情，并充当你健康的四分卫。话虽如此，我不认为它能解决我们将面临的所有这些挑战，至少短期内不会。我认为在很大程度上，我们正在赋能许多提供护理的合作伙伴，他们绝大多数以各种方式使用我们的模型，使这些操作更顺畅、更容易，以便患者不会陷入这种碎片化的体验。

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 that last mile of actually having impact on patients just looks like the last 100 m 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. 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 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 to like make these operations happen smoother and easier so that patients are not left in this kind of fragmented experience.

那么，作为消费者，我应该期待从 OpenAI 获得的体验是你描述的那种四分卫护理吗？我会在某个时候在界面中与临床医生交谈吗？或者你可能会将我路由到医生并帮助我找到，因为这是一个巨大的挑战，仅仅是安排和让那些就诊发生。我应该期待什么？

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 cuz given that's a whole challenge, just scheduling and making those visits happen. What should I look forward to?

我想我想我想你会看到，我们希望非常有助于帮助你导航寻找护理的过程。我不认为我们会从事你提到的所有事情。

I think I think I think you'll see like we 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.

嗯。

Mhm.

嗯，但那就是我们真正依赖所有初创公司和护理提供组织、合作伙伴等的地方。

Um but and that and that's where we're really relying on all of our startup and care provider organizations and partners and things like this.

那么那么回到我之前提到的一个问题，那就是你正在构建那个未来，但你是与合作伙伴一起构建，你为消费者和患者构建，你为临床医生构建，你为企业构建。对于临床医生和企业，请给我们一些关于你正在推出的一些工具以及一些真正有效的合作的细节。

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 uh on some of the tools that you're launching and and some of the collaborations that have really worked.

是的。嗯，我们有一些非常令人兴奋的合作关系。嗯，我们有一些非常棒的 ChatGPT 医疗保健启动合作伙伴。我们有很多用户使用 ChatGPT 临床医生版，并且非常喜欢它。

Yeah. Um we've had a few really exciting partnerships. Um we've had some really amazing launch partners for ChatGPT for healthcare. We've had lots of users using ChatGPT for clinicians and really enjoying it.

嗯，这项服务的留存率一直是个非常大的惊喜。人们似乎真的很喜欢它。是的，我们一直在与各种医疗系统合作。有几个特别突出：一个是 UCSF，我们一直在讨论这个。他们是 Epic 和电子健康记录（EHR）与 ChatGPT 连接的最早采用者之一，他们真的站在最前线，思考比如临床医生用它来做病历审查之类的事情，并思考如何让照顾患者的体验不必如此碎片化——不需要切换标签页或切换体验等等。所以他们在这一块是我们的重度用户。另一个是 AdventHealth，他们实际上也是我们的重度用户，并考虑在整个工作空间、实际上在整个 OpenAI 部署智能体。他们一直是领导者。而且他们取得了惊人的成果。他们报告说，通过这类方式使用 AI，许多行政工作流程成本大约降低了 80%。我认为很多医疗系统都应该向他们学习。他们真的令人印象深刻。

It's been, um, the retention on the offering has been a very pleasant surprise. People seem to be really enjoying it. Yeah, we've been working with a variety of health systems. A couple that stand out: one is UCSF, and we've been chatting about that. They've been one of the 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 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. So they've been some of our power users on that front. Another one is AdventHealth, and they've actually been some of our power users and think about deploying agents across the workspace, actually across all of OpenAI. They've been a leader. And they've had amazing results. They reported roughly 80% reduction in a lot of their administrative workflow costs using AI in these kinds of ways. And I think a lot of health systems should look to them. They're really impressive.

如果我走进医生的诊室，看到他们的屏幕上开着 ChatGPT，我应该感到不安吗？还是应该高兴？你觉得人们会对此作何反应？

If I go into my doctor's office and I see ChatGPT open on their screen, should I be upset? Should I be happy? How do you think people will react to that?

是的，我听到不同的声音，而且我认为这实际上正在随时间变化。这是一件有趣的事情，奥弗顿窗口正在实时移动。我认为以前可能有一定程度的担忧，但越来越多地，双方都有这个问题：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以你描述的方式使用 AI。但我认为双方实际上都发现 AI 对他们个人用途非常有用。

Yeah, I hear differing things, and I think it's actually changing over time. This is an interesting thing where the Overton window is shifting in real time. I think there might have been some level of concern before, but increasingly, on both sides, there's this question of whether clinicians would push back against patient use of AI and whether patients would push back on clinician use of AI in the way you're describing. But I think both parties are actually finding it incredibly useful for their own personal purposes.

嗯。

Mhm.

所以我认为我们在实践中发现，尤其是随着时间推移，双方在就医过程中可能实际上会因为 AI 而感到更安全，无论是临床医生使用它，还是患者用它来做合理性检查。我认为这种态度肯定在随时间改变。我不会说我们已经完全达到那一步，但这是我的预测。

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 their clinician using it or the patient using it as a sanity check. And I think that attitude is definitely changing over time. I wouldn't say that we're fully there, but that's my prediction of how things go.

好的，那么再进一步，如果患者在就诊前已经做了所有研究，整合了所有健康数据，并在 ChatGPT 中问了一堆问题，而临床医生在就诊前已经通过自动化循环将整个病例跑了一遍 ChatGPT，然后就诊发生了，他们应该谈些什么？

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?

我希望他们谈论医疗保健中人性的一面。仍然有很多不确定性。有很多同理心。有很多床边态度是医疗保健人性方面的一部分。也有很多身体互动。我不认为所有信息都存在，让 AI 就能弄清楚应该做什么——从信息论上讲，所有信息都存在，让 AI 就能弄清楚应该做什么，而就诊的部分原因是为了获取更多信息，进行病史采集、检查等过程。所以我认为这不会消失，尤其是其中的身体元素。而且我认为即使从认知角度，同理心、获得安慰和获得二次检查的元素仍将是人们所需要的。

My hope is that they talk about the human aspect of health care. There's still a lot of uncertainty. There's a lot of empathy. There's a lot of bedside manner that goes into the human aspects of health care. There's also a lot of physical interaction that goes into it. I don't think all the information exists for the AI to just figure out what all should be done—information theoretically, all the information exists for the AI to just figure out what 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. 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 empathy and getting comfort and getting that second check will still be something that people demand.

你认为 AI 总体上，或者说 dots，在就诊间隔期会做什么？你认为你们会跨越界限，在就诊间隔期提供护理，然后与医生分享吗？

What do you think AI in general, or let's say dots, would do in between visits? And do you think you'll cross the line into providing care in between visits and then share that back with the doctor?

是的，我不知道。我不认为我们会——我认为我们肯定会专注于健康信息，但无论是 dots 还是任何其他产品界面，我们对这个更 proactive 方向的未来感到非常兴奋，就像你概述的那样。比如想象一下，如果某人正在应对慢性病护理旅程，你知道，再次像这个例子：某人拿到实验室测试结果，他们的 A1C 较高，处于糖尿病前期或糖尿病。你能提醒他们吗，比如‘嘿，也许你应该去检查一下药物，并去看你的心脏病专家之类的。’

Yeah, I don't know. I don't think we'll—I think we'll definitely be focused on health information, but I think whether it's dots or any other product surface, we're really excited about this future of heading more in this proactive direction that you were outlining. Like imagine if somebody is dealing with a chronic care journey and, you know, again like this example of someone gets a lab test back where their A1C is higher and they're pre-diabetic or diabetic. Can you flag for them like, 'Hey, maybe you should get a medication check and go to your cardiologist or whatever.'

嗯。

Mhm.

并思考如何以正确的方式向用户标记药物依从性问题或其他类型的问题。所以我认为这类问题是 AI 可能产生真正高健康影响的地方，尤其是在这些慢性病护理旅程中。但我不认为我们会——我们会自己管理所有这些事情。

And think about the right ways in which you can flag to users medication adherence issues or other kinds of issues. So I think those kinds of issues are places where AI can have potentially a really high health impact, especially in these chronic care journeys. But I don't think it'll be—we'll be in the business of managing all these things by ourselves.

当然。

Sure.

是的。

Yeah.

我好奇的另一件事是，在 AI 应用于其他领域和情境的领域中，比如国际象棋和围棋等游戏。当 AI 在国际象棋和围棋上变得非常擅长时，它们也揭示了其他策略和游戏方式，甚至是那些领域的最优秀专家也没有利用或追求的，这帮助我们更多地了解这些游戏，也帮助我们更多地了解 AI。从你在研究中做的工作来看，你在 AI 应用于健康方面见过类似的情况吗？是否已经有令你惊讶的发现，或许引入了新的提供护理的方式或理解临床场景的方式，能给我们一些乐观情绪？

Another thing I'm curious about is in the fields of AI applied to other domains and situations like games like chess and Go. 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 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?

是的，我的意思是，我认为我们在较小的规模上见过这种情况，但让我拉远一点说，最近激励我们团队整体提高人类健康上限的一件事是，将一种‘第 37 手’应用于健康的理念。这个‘第 37 手’的想法来自 AlphaGo，以及 AlphaGo 系统——它玩这个复杂的棋盘游戏——与李世石对弈的一局棋。

Yeah, I mean I think we've seen this at smaller scales, but let me zoom out and say like, 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 a kind of a move 37 for health. And this idea of a move 37 comes from AlphaGo and a game that the AlphaGo system, which plays this complicated board game, played with Lee Sedol.

嗯。

Mhm.

其中有一个特定的落子，在当时被所有观看这场 AI 系统与世界围棋专家对弈的人类专家视为非常愚蠢的一步，也是非常令人惊讶的一步。但事后看来，这是某种精彩而有洞察力的一步，人类现在才刚刚开始理解并从中学习模式等等。

Where there was a specific move that just kind of in the moment was seen as a really dumb move and a really surprising move for all the human experts watching this game between this AI system and this world expert in Go. But in hindsight is this kind of brilliant and insightful move that humans are now just beginning to understand and learn from the patterns of and things like this.

是的。

Yeah.

这是一个非常有趣的例子。我觉得——

And this is a very interesting example. I think—

文本。

Text.

所以有一个想法：如何在健康领域达到“第 37 步”——一步棋或一个预测，模型预测出人类可能无法预测的东西。

So there's an idea of how do you get to a move 37 in health—a move or a prediction where the models predict something that a human might not have been able to predict.

嗯。

Mhm.

事后看来似乎正确、精彩、有洞见且有影响力。所以我认为我们处于非常早期的阶段，未来你会看到比过去多得多的这类例子。

Hindsight seems correct and brilliant and insightful and impactful. 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.

但我想，一个例子是再次思考这些系统如何被部署为临床医生的安全网——当临床医生实际提供护理时，AI 会实时标记：“嘿，这里可能有问题。”比如我们与 Panda Health 的研究，思考我们在那里看到的结果模式。举个例子，在那种情境下，临床医生有点倾向于过度使用抗生素。

But I think, you know, one kind of example is again thinking about 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, like, "Hey, something might be off here." Like our study with Panda Health and thinking about the patterns of results that we saw there. As an example, in that context the clinicians were a little bit prone to overuse of antibiotics.

嗯。

Mhm.

模型标记的一个非常常见的模式就是避免这种抗生素过度使用，少考虑局部病例，多考虑抗生素耐药性的全球问题。所以这是一个具体的例子，但再次，我认为你会看到更多这样的例子：模型接收用户 10 年来的纵向历史，跨越所有数据——跨越他们的 HR 数据、聊天数据，所有这些——并做出以前看似不可能的预测，因为人类甚至无法在头脑中整合所有这些数据。

And a very common pattern that the models flagged is just avoiding this overuse of antibiotics and thinking less about the local case and more about the global issue of resistance to antibiotics. So that's one specific example, but again, I think you're going to see more of these examples of models taking in a longitudinal history of a user over 10 years across all their data—across their HR data, across their chat data, all these things—and making predictions that seemed impossible before because a human can't even integrate all that data in their head.

嗯。

Mhm.

并做出关于他们的健康、风险等看似不可能的预测，我认为那将足以被称为“第 37 步”。

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 a move 37.

是的。我的意思是，消费者好奇的事情太多了，至少曾经我们不确定某件事是否对健康有益，然后如你所说，奥弗顿窗口改变了，有更多证据在某个方向上具有决定性。你知道，曾经人们不确定吸烟是否有害。咖啡对你是好是坏？糖对你是好是坏？有时对这些事情的看法已经改变。而今天消费者想知道：肌酸对你有好处吗？褪黑素对你有好处吗？所有这些新出现的现象，也许研究不足，或者至少没有广泛共识——某些补充剂或某些药物的安全性和有效性等等。你是否预计，随着人们将这些信息输入 ChatGPT 或其他 LLM，我们将有更多证据可以生成，来研究人们好奇但我们并不真正知道或没有决定性证据表明其立场的一些事情？

Yeah. I mean there are 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 to your point the Overton window changed and there was more evidence that was 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 want to know: is creatine good for you? Is melatonin good for you? All these new emerging phenomena that perhaps are understudied or at least not widely agreed upon—the safety and effectiveness of certain supplements or certain medications and things like that. Do you anticipate as people are 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?

是的，我认为智能体最有趣的一点是它们解决了分析现有数据中的一堆瓶颈，对吧？医疗生态系统中各个地方都在生成数据——EHR 数据、纵向病史、理赔数据等等。

Yeah, I mean I think one of the most interesting things about agents is they kind of solve a lump of 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.

嗯。

Mhm.

数据在那里生成，就只是存储在那里，从中获得的洞见没有被充分挖掘或用于产生新知识。

Where data is being generated and it is just stored there and the insights from it are not fully mined or used to generate new knowledge.

是的。

Yeah.

所以我认为，未来健康如何改善以及健康天花板如何提高的前沿之一，将是思考如何利用那些散落在各地盒子里的数据，让模型基于这些数据产生新洞见。所以我对那个未来也很乐观。我也对一个未来感到乐观——你提到了两个群体：用户/患者和临床医生。我认为还有第三个群体，那就是科学家，对吧？他们帮助推进该领域的知识前沿。我也非常乐观地认为，未来 AI 将帮助加速验证中的许多瓶颈，我认为验证最终会成为瓶颈，而不是提出新想法或治疗方法之类的事情。所以对此也非常乐观。

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 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 mentioned these two constituencies, the user, the patient, and the clinician. I think there is a third constituency, which is the scientist, right? Who is helping advance the frontier of knowledge in the space. And I'm also very optimistic about a future in which AI helps accelerate a lot of the bottlenecks in validation, which I think will end up being the bottlenecks as opposed to proposing new ideas or treatments or things like this. And so very optimistic about that as well.

稍微换个话题，你也分享过关于智能成本作为医疗智能输入大幅下降的想法，你知道，在某个时候它会便宜到无需计量。我花了很多时间，我们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系统——其膨胀速度远超通胀。我们在医疗上花费 5.3 万亿美元以上。如果它是一个国家，将是第三大 GDP。它是我们经济中最大的部分。它也是我们经济中最大的雇主。2300 万美国人在医疗行业工作。我们在医疗上的花费实在太多了。你如何看待你所做的工作及其对医疗成本的影响？

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 spend a lot of time and we as a team spend a lot of time trying to understand how these technologies will make an impact on our health care system that is inflating well ahead of inflation. 5.3 plus trillion we spend on health care. 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 health care. There's just so much we spend on health care. 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?

我认为这是我们追求使命的最重要方式之一——不仅让我们的模型在前沿变得更好，而且让所有人都能广泛使用。所以这显然是我们能够向全世界许多人免费部署医疗智能的重要部分。我认为这也是我们思考 API 的重要部分——我们的模型在健康方面有多好，以及那里的定价等等。最近的一个更新是我们最近发布了 GPT-6 Luna。

I think this is 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 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 API and how good our models are there for health and pricing there and stuff like this. One recent update was we recently released GPT-6 Luna.

嗯。

Mhm.

GPT-6 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 GPT-6 Luna 实际上在我们的基准 HealthBench Professional 上表现更好——这是我们当前最新的临床医生工作流基准，这些工作流非常困难，是根据我们当前模型在临床医生使用我们模型的所有不同方式中的难度挑选出来的——模型仍然还有改进空间。

And GPT-6 Luna is our current cheapest model on the API—or cheapest frontier model on the API. And what we found with GPT-6 Luna was that it was actually doing better on our benchmark HealthBench Professional—our current latest benchmark of clinician workflows that are 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 use some improvement.

嗯。

Mhm.

它的表现比 5.6 Soul 更好，后者 literally 是我们一个月前最好的模型。尽管便宜 34 倍，它表现更好。在过去一年里我们有过几次这样的时刻，而且有许多趋势表明，与过去引入的其他技术相比，AI 一直在以更快的速度降低给定能力水平或有用性的成本。

It performed better than 5.6 Soul, which was literally our best model like a month ago. And it performs better despite being 34 times cheaper. And we've had a couple of those moments even in the past year, and there's been many trends showing that AI compared to other technologies that have been introduced in the past has been reducing the cost of a given level of capability or usefulness faster than many of these other technologies.

所以我们在 OpenAI 讨论过的一件事是，在给定的健康智能水平下，健康智能的成本将会骤降。

And so one of the things we've had at OpenAI is that the cost of health intelligence will plummet at a given level of health intelligence.

嗯。

Mhm.

而临床医生和患者觉得可以信任的健康智能水平，已经开始达到了，对吧？所以这在某种意义上是固定的智能水平。希望这个门槛也会随着时间的推移而提高。我的预期是，一旦我们接近那个智能水平，那个智能水平的成本也会像之前一样大幅降低。所以那里的希望是，我们实现健康智能的商品化，对吧？我们不想囤积健康智能。我们希望每个人都能使用它。所以我希望这能有所贡献，但回答你关于整个医疗保健支出问题，我不认为所有这些都与健康智能有关。我不认为健康智能已经对你所描述的问题产生了宏观影响。如果你看看整个医疗支出，有很多领域，比如慢性病管理、文档记录、行政管理或各种行政工作流程、后台工作流程，有很多钱花在那里，可能不需要那样花。所以我们对未来几年整体支出的压缩非常乐观。我认为这还没有发生，而且我认为这不仅仅是让健康智能变得丰富就能发生的。我认为这需要通过工作流程的整合、部署和扩展，并真正确保这些模型在人们使用它们的经济有价值任务中是有价值的，但我认为这是实现那个未来的先决条件。

And that the level of health intelligence where 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. So the hope there is that we get a kind of commoditization of health intelligence, right? We don't want to hoard that health intelligence. We want everybody to use it. So my hope is that that contributes, but to answer your question about the whole of health care spending, I don't think all of that is about health intelligence. I don't think health intelligence has had yet a macro effect on the problem that you're describing. If you look at the whole of spend of health, there are quite a bit of areas though where, if you look at for example chronic care management or documentation or administration or a lot of administrative workflows of various kinds, back office workflows, there's a lot of money spent in that probably don't need to be spent that way. And so we are really optimistic about 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 in workflows and deployment and scaling that up and really making sure that 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.

更多护理，更少文书工作，至少这是我们前进的方向，有很多成本节约。

More care, less paperwork is sort of where we're heading at least there's a lot of the cost savings.

是的。

Yeah.

我相信你看到了过去几周的一些头条新闻，有一篇《纽约时报》的文章基本上是说提供方 AI 和支付方 AI 在打架，还有蓝十字蓝盾协会的一些分析显示，各自的账单机器人和文书机器人实际上增加了十亿美元左右的支出。当你在做前沿的健康研究，试图将医疗智能的成本降至接近零，然后看到这样的头条新闻时，你怎么看？

I'm sure you saw some of the headlines of 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?

我认为这可能是那些领域之一，围绕这种提供方-支付方军备竞赛可能会有一些短期的痛苦，但从长远来看，没有理由存在这种情况，对吧？所以我认为，你正在进入一个世界，很多就诊实际上有某种环境智能在房间里倾听、转录等等。有笔记，有临床笔记，有 AI 对临床笔记的裁决等等。所以你有很多细节来裁决就诊，对吧？所以在我看来，理想的世界是提供方和支付方达成握手，就像“嘿，这是我们对这次就诊的真实情况。这是我们所有的信息。没有其他信息来源了。”

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 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's notes, there's clinical notes, there's AI adjudication 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. Here's all the information we have. There's just no other source of information that there is."

对。

Right.

然后 AI 帮助裁决这种情况，也许有一些有限的上诉程序之类的，但我不认为你需要所有这些流程，比如支付方和提供方之间的 AI 军备竞赛。我认为这完全可以避免。所以我认为这有点像是短期的干扰。是的。

And then an AI helps adjudicate that situation and maybe there's 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 that can just be completely avoided. So I view that as a bit of a short-term distraction. Yeah.

有很多在医疗保健领域构建技术的企业家可能在观看这个。他们可能试图弄清楚，随着你继续推进你的工作，将医疗智能的成本降至接近零，这成为一种无处不在的公用事业，贯穿我们所有的医疗保健互动，对于消费者、临床医生、医疗保健技术或医疗保健栈的行政部分。人们应该在上面构建什么？假设这变得好 100 倍以上，专业应用应该做什么？

There's a lot of entrepreneurs that are building technology in 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 a 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. What should folks be building on top? What should specialist applications be doing assuming that this gets 100x plus better?

是的。这是个好问题。就像我提到的，我们通过 OpenAI 思考我们的健康影响时，很多不是直接通过我们提供的产品，而是通过我们的合作伙伴和提供护理的最后一英里。所以，我要对我们的合作伙伴说的是，建立在真正有能力的模型之上。最重要的是赌这些模型会变得更有能力。所以，赌未来。我认为医疗保健领域的许多人在某种程度上介于赌模型不行、中立赌模型和赌模型行或赌模型会更好之间。我想说，赌模型会变得更好。这可能是最重要的，并思考正在开发的许多技术，无论是智能体还是计算机使用或其他前沿技术，将加速医疗保健，并赌这一点，朝着那个未来构建。我还认为，很多医疗护理实际上会通过 AI 变得更易于患者获得。你知道，我们谈到了这个四分卫的想法，消除很多这些障碍。所以，专注于提供真正高质量的护理，并让 AI 成为监测护理旅程的一部分，我认为这也是非常重要的一部分。

Yeah. It's a great question. Like I mentioned, 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 the last mile of delivering care. And so, what I'd say to our partners is, 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, 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 agents or computer use or other frontier tech will accelerate healthcare and bet on that and build towards that future. I also think a lot of medical care will actually become more accessible to patients with AI. You know, we talked about this idea 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 monitoring that care journey, I think is a very important part of that as well.

放眼整个医疗保健系统，现在从整体来看，我们谈到了这个 5.3 万亿美元。很多支出是提供方、支付方和医疗保健五个 P 中其他利益相关者之间的文书工作。很多是认知护理，你主张可以通过增强和提升，我们可以在线增加更多的认知护理能力，这令人兴奋，从可及性的角度来看也令人兴奋。然后医疗保健中很多支出是程序性的，要么需要一些影像、一些血液检查、一些后续检查，要么是一些手术干预。

Zooming out to the overall healthcare 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 P's of healthcare. A lot of that is cognitive care that you're arguing can be augmented and enhanced and sort of we can bring up 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.

你觉得你们在医疗领域做的智能相关工作，会如何影响手术类诊疗？那会成为最终的——会成为新的、或被强化的、甚至可能被加剧的瓶颈吗？还是你预计你们也会在那方面做很多工作，比如机器人等等？

Where do you see 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, or an enhanced, or perhaps an exacerbated bottleneck? Or do you expect to do a lot of work there as well, robotics, et cetera?

是的，我觉得短期内，在手术类诊疗上，你希望在需要的时候有更好的靶向定位，对吧？我认为 AI 可以帮助提供有用的信息，让人知道什么时候该去寻求这类诊疗。未来一两年最大的宏观因素，大概会是更好的主动预防性措施，对吧？这样你就不必以后真的去做那个手术。因为如果你更早做出正确的选择，而 AI 帮你在这段旅程中导航，那你就有望避免最昂贵手术带来的大量下游成本。所以这可能是值得关注的一个宏观因素，这是我的猜测。

Yeah, I think in the short term with procedural care, you want better targeting when that's needed, right? And I think AI can help provide information that is helpful for knowing when to seek that kind of care as well. Probably the biggest macro factor in the next year or two will just be better proactive, preventative stuff, right? So you don't have to actually go in later and get that procedure. 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 costs of the most expensive procedures. So that might be one macro factor to look out for, is my guess.

嗯。

Mhm.

我认为机器人与诊疗这个问题更偏长期，无论从技术角度，还是从报销和行业整合的角度来看都是如此。我的预期是，这不是未来几年就能实现的事。

I think the question of robotics and care is a bit of a longer-term one, both from a technology perspective and from a reimbursement and industry integration perspective. My expectation is that it's not a next-few-years kind of thing.

太酷了。好，我们聊了很多。我们谈到了智能的提升，谈到了信任，谈到了从医疗信息走向医疗照护。请帮我们理解一下，随着事态继续发展，随着智能体、个人智能体以及底层模型越来越强大，我们可以期待什么？从 ChatGPT Health 我们能期待什么？

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 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，我认为我们真正专注于实现的有两件高层级的事。第一件，还是思考如何抬高人类健康的下限，让可信、可靠的健康智能惠及所有临床医生、所有用户和患者。也就是达到这样一种状态——就像我们谈到的——所有患者和临床医生都因为 AI 参与诊疗过程而感到更安全。比如当你看到 ChatGPT——当医生在使用 ChatGPT 时，你确实会感到更安全，反过来也一样。要达到那一步，再说一次，这不仅是模型要好、评估要好的问题，还涉及我们如何部署它、围绕它的启动阻力，以及产品等等。第二件事，是真正思考如何抬高上限。也就是 AI 能以哪些方式做到以前不可能做到的事？其中之一，我认为就是这种主动式健康、主动式个人健康助手，成为你诊疗旅程的一部分。它帮你应对这些时刻，做你的场上指挥官，帮你安排日程等等，同时也会标出你今天就可以在健康上介入、从而避免未来大量昂贵恶化的地方。我认为这将是我们在思考如何为尽可能多的人改善健康未来时的重要一环。第二件事就是你之前提到的，真正思考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——并思考如何利用它们产生有益于患者的新洞见。

Yeah, I think there are really two high-level things that we're really focused on making happen. One is, again, thinking about 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, like we talked about, all patients and clinicians just feel safer by having AI in the care journey. Like when you see the ChatGPT — when the doctor is using ChatGPT, you actually feel safer, and vice versa. Getting to that point — and again, that's a question not just of the models being good and evals being good, but also how we deploy it 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 proactive health, proactive personal health assistant that is part of your care journey. It's helping you navigate these moments, being your quarterback, helping you schedule and all these things, but also flagging the places where you can intervene in your health today and prevent a lot of expensive deterioration later in the future. And I think that's going to be an important part of how we think about improving the future of health for as many people as possible. And I think the second thing is just what you mentioned before, which is 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.

想到 ChatGPT 大概是 3 年前问世的，这令人震撼、难以置信，也让人谦卑。所以在非常非常短的时间里取得了巨大进展。因此，哪怕只是谈 5 年或 10 年后的情况，都显得像纸上谈兵。但你能给我们一些预测吗？我的意思是，有没有某个功能今天感觉像科幻，但有一天，3 年、5 年、10 年后，我们可能就会拥有？

It is striking and incredible and humbling to realize that ChatGPT, I think, came out 3 years ago, roughly. And so it's been a lot of progress in a very, very short period of time. So to even talk on a 5-year or 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，如今预测未来超级超级难。所以这是个难题，但我想说的是，我认为在那个时间尺度上，也就是未来 5 到 10 年，对人类健康影响最大的，很可能是更新的疗法，它们可能来自两件事。一是 AI 引导生成新的靶点或分子，这些靶点或分子可能对新的疗法有价值。二是 AI 引导的优先级排序与验证。也就是思考正确的方式——我们已经到了这样一个阶段：人们想提议用于临床试验的分子和药物数量，远远超过临床试验可用的名额。所以要思考模型能以哪些方式帮助排定优先级并运行这些临床试验，让它们运行得更顺畅、更快速。我的猜测是，在那个时间尺度上，你谈论的会是改进的药物、更个性化的药物等等。而这只有在你投资于那两大支柱的前提下才可能实现。

Yeah, it is super, super hard to predict the future these days. So this is a difficult one, but what I would say is, 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 AI-guided generation of new targets or molecules that could be interesting 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. My guess is in that kind of time frame, you're talking about improved medicines, more personalized medicines for people, things like this. And that will only be possible because you invested in those two pillars.

太棒了。好，我们聊了很多。这场对话让我离开时比之前更加乐观。那么，Karan，非常感谢你来参加 a16z 播客。

That is awesome. Well, we've covered a lot of ground. I'm leaving this conversation even more optimistic than I was before. So, Karan, thank you so much for coming on the a16z podcast.

谢谢你的邀请。

Thank you for having me.