Health-care AI is here. We don’t know if it actually helps patients.

医疗人工智能时代已至，但它真的能改善患者健康吗？

I don’t need to tell you that AI is everywhere. Or that it is being used, increasingly, in hospitals. Doctors are using AI to help them with note-taking. AI-based tools are trawling through patient records, flagging people who may require certain support or treatments. They are also used to interpret medical exam results and x-rays. 无需多言，大家也知道人工智能（AI）无处不在，并且正越来越多地被应用于医院。医生们正在利用 AI 辅助记录笔记；基于 AI 的工具在患者病历中进行检索，标记出可能需要特定支持或治疗的患者；它们还被用于解读医学检查结果和 X 光片。

A growing number of studies suggest that many of these tools can deliver accurate results. But there’s a bigger question here: Does using them actually translate into better health outcomes for patients? We don’t yet have a good answer. That’s what Jenna Wiens, a computer scientist at the University of Michigan, and Anna Goldenberg of the University of Toronto argue in a paper published in the journal Nature Medicine this week. 越来越多的研究表明，其中许多工具能够提供准确的结果。但这里有一个更重要的问题：使用这些工具真的能转化为患者更好的健康结果吗？我们目前还没有确切的答案。这正是密歇根大学计算机科学家 Jenna Wiens 和多伦多大学的 Anna Goldenberg 在本周发表于《自然-医学》（Nature Medicine）杂志上的一篇论文中所探讨的观点。

Wiens tells me she has spent years investigating how AI might benefit health care. For the first decade of her career she tried to pitch the technology to clinicians. Over the last few years, she says, it’s as though “a switch flipped.” Health-care providers not only appear much more interested in the promise of these technologies but have also begun rapidly deploying them. The problem is that many providers aren’t rigorously assessing how well they actually work. Wiens 告诉我，她多年来一直致力于研究 AI 如何造福医疗保健。在她职业生涯的前十年里，她一直在向临床医生推介这项技术。她说，在过去几年里，情况仿佛“拨动了开关”。医疗服务提供者不仅对这些技术的潜力表现出浓厚兴趣，而且已经开始迅速部署它们。问题在于，许多机构并没有严格评估这些工具的实际效果。

Take “ambient AI” tools, for example. Also known as AI scribes, they “listen” to conversations between doctors and patients and go on to transcribe and summarize them. Multiple tools are available, and they are already being widely adopted by health-care providers. A few months ago, a staffer at a major New York medical center who develops AI tools for doctors told me that, anecdotally, medics are “overjoyed” by the technology—it allows them to focus all their attention on their patients during appointments, and it saves them from a lot of time-consuming paperwork. 以“环境 AI”（ambient AI）工具为例。它们也被称为 AI 记录员，能够“聆听”医生与患者之间的对话，并进行转录和总结。目前市面上已有多种此类工具，并正被医疗机构广泛采用。几个月前，纽约一家大型医疗中心负责开发医生 AI 工具的工作人员告诉我，从轶事反馈来看，医务人员对这项技术感到“欣喜若狂”——它使医生在就诊期间能将全部注意力集中在患者身上，并节省了大量耗时的文书工作。

Early studies support these anecdotes and suggest that the tools can reduce clinician burnout. That’s all well and good. But what about patient health outcomes? “[Researchers] have evaluated provider or clinician and patient satisfaction, but not really how these tools are affecting clinical decision-making,” says Wiens. “We just don’t know.” 早期研究支持了这些说法，并表明这些工具可以减轻临床医生的职业倦怠。这固然很好，但患者的健康结果又如何呢？Wiens 说：“（研究人员）评估了医疗提供者或临床医生以及患者的满意度，但并没有真正评估这些工具如何影响临床决策。我们对此一无所知。”

The same holds true for other AI-based technologies used in health-care settings. Some are used to predict patients’ health trajectories, others to recommend treatments. They are designed to make health care more effective and efficient. But even a tool that is “accurate” won’t necessarily improve health outcomes. AI might speed up the interpretation of a chest x-ray, for example. But how much will a doctor rely on its analysis? How will that tool affect the way a doctor interacts with patients or recommends treatment? And ultimately, what will this mean for those patients? 其他用于医疗环境的 AI 技术也是如此。有些被用于预测患者的健康轨迹，有些则用于推荐治疗方案。它们的设计初衷是让医疗服务更有效、更高效。但即使是一个“准确”的工具，也不一定能改善健康结果。例如，AI 可能会加快胸部 X 光片的解读速度，但医生会在多大程度上依赖其分析结果？该工具将如何影响医生与患者互动或推荐治疗的方式？最终，这对患者意味着什么？

The answers to those questions might vary between hospitals or departments and could depend on clinical workflows, says Wiens. They might also differ between doctors at various stages of their careers. Take the AI scribes, as another example. Some research on AI use in education suggests that such tools can impact the way people cognitively process information. Could they affect the way a doctor processes a patient’s information? Will the tools affect the way medical students think about patient data in a way that impacts care? These questions need to be explored, says Wiens. “We like things that save us time, but we have to think about the unintended consequences of this,” she says. Wiens 表示，这些问题的答案可能因医院或科室而异，并取决于临床工作流程。它们在处于不同职业阶段的医生之间也可能存在差异。以 AI 记录员为例，一些关于教育领域 AI 使用的研究表明，此类工具会影响人们认知处理信息的方式。它们是否会影响医生处理患者信息的方式？这些工具是否会以影响护理质量的方式改变医学生思考患者数据的方式？Wiens 认为，这些问题都需要深入探讨。“我们喜欢能节省时间的东西，但我们必须考虑其带来的意外后果，”她说。

In a study published in January 2025, Paige Nong at the University of Minnesota and her colleagues found that around 65% of US hospitals used AI-assisted predictive tools. Only two-thirds of those hospitals evaluated their accuracy. Even fewer assessed them for bias. The number of hospitals using these tools has probably increased since then, says Wiens. Those hospitals, or entities other than the companies developing the tools, need to evaluate how much they help in specific settings. There’s a possibility that they could leave patients worse off, although it’s more likely that AI tools just aren’t as beneficial as health-care providers might assume they are, says Wiens. 在 2025 年 1 月发表的一项研究中，明尼苏达大学的 Paige Nong 及其同事发现，约 65% 的美国医院使用了 AI 辅助预测工具。其中只有三分之二的医院评估了其准确性，评估其偏差（bias）的医院则更少。Wiens 表示，自那时以来，使用这些工具的医院数量可能已经增加。这些医院，或者除工具开发公司以外的实体，需要评估它们在特定环境下的实际帮助程度。Wiens 说，它们有可能导致患者状况恶化，尽管更有可能的情况是，AI 工具并不像医疗提供者所假设的那样有益。

“I do believe in the potential of AI to really improve clinical care,” says Wiens, who stresses that she doesn’t want to stop the adoption of AI tools in health care. She just wants more information about how they are affecting people. “I have to believe that in the future it’s not all AI or no AI,” she says. “It’s somewhere in between.” “我确实相信 AI 有潜力真正改善临床护理，”Wiens 说道。她强调，她并不想阻止 AI 工具在医疗保健领域的应用，她只是希望获得更多关于它们如何影响人们的信息。“我相信未来不会是‘全盘 AI’或‘完全不用 AI’，”她说，“答案应该介于两者之间。”