https://www.openevidence.com/

https://www.gv.com/news/openevidence-ai-doctors

Medical knowledge doubles every 73 days. This means science is advancing faster than doctors can possibly keep up, creating a dangerous gap between breakthrough discoveries and actual patient treatment. When I first met Daniel Nadler, he told me something that stuck with me: ‘This isn’t just a technology problem—it’s a human problem.’ He was right, and OpenEvidence is the solution.

GV just led OpenEvidence’s Series B, and I’m excited to join the board and support Daniel and team.

OpenEvidence is an AI-powered application that allows physicians to ask natural language questions and get immediate answers grounded in peer-reviewed medical evidence from sources like the New England Journal of Medicine and JAMA. The mission is simple but profound: organize and expand global medical knowledge so that every clinician can access the latest breakthrough discoveries at the moment of care.

What Daniel has built since launching in 2023 is staggering. OpenEvidence has seen adoption by doctors faster than any technology in history aside from the iPhone. In July of last year, OpenEvidence had ~358,000 physician consultations in one month. Last week they hit that same number—in a single day. Today, it’s used by more than 40% of physicians in the United States, who log in daily, on average. The patient impact is extraordinary: 1 in 3 Americans this year were treated by a physician using the platform.

This explosive adoption speaks to the incredible product-market fit, but it’s also a result of Daniel’s unorthodox DTC (Direct to Clinician) approach. You don’t pay, you just start using it. Instead of trying to sell into huge medical institutions, he’s letting practitioners use it for free and building adoption from the ground up. Word of mouth is responsible for much of their growth, which has allowed them to be the most capital efficient of the breakout AI applications GV has been tracking.

医学知识每73天就会翻倍。这意味着科学发展速度远超医生跟进步伐，在突破性发现与患者实际治疗之间形成了危险鸿沟。当我初次见到丹尼尔·纳德勒时，他的一句话令我印象深刻："这不只是技术问题，更是人类问题。"他是对的，而OpenEvidence正是解决方案。

GV刚领投了OpenEvidence的B轮融资，我很荣幸加入董事会支持丹尼尔及其团队。

OpenEvidence是一款人工智能应用，医生可用自然语言提问，即刻获得基于《新英格兰医学杂志》《美国医学会杂志》等同行评审医学证据的答案。其使命简洁而深刻：整理并扩展全球医学知识，让每位临床医生都能在诊疗时刻获取最新突破性发现。

丹尼尔自2023年推出以来的成就令人震撼。OpenEvidence在医生中的普及速度史上仅次于iPhone。去年7月单月完成约35.8万次医生咨询，上周单日就达到同等规模。如今美国超40%医生使用该平台，日均登录。患者影响惊人：今年每3个美国人就有1人接受过使用该平台的医生诊疗。

这种爆发式普及印证了绝佳的产品市场契合度，也源于丹尼尔独创的"DTC（直达临床医生）模式"——无需付费即可使用。他不向大型医疗机构推销，而是让从业者免费使用，自下而上构建用户基础。口碑传播推动大部分增长，使其成为GV追踪的AI应用中资本效率最高的突破者。

During my first meeting with Daniel, I wanted to invest immediately. But what really sealed it for me was the shared enthusiasm from our life sciences team. My colleagues in life sciences are doctors and researchers by training, and they started hearing about OpenEvidence from colleagues at medical institutions nationwide. After trying it themselves, they realized OpenEvidence was nailing answers that other platforms couldn’t match. Krishna Yeshwant, a physician who co-leads our life sciences investment team, started using it daily, replacing tools he’d relied on for years.

The defining moment came in Daniel’s living room. Krishna and my colleagues from Cambridge arrived for what was supposed to be a 90-minute meeting. Six of us spent the entire day with Daniel, and by the end of the evening offered to invest on the spot. What drew us in wasn’t just the product—it was Daniel himself. He’s that rare founder who isn’t motivated by the same things as most people. His fear isn’t failure; it’s not achieving true greatness. Daniel is building something that compounds forever, and is already starting to be accessed by healthcare professionals around the globe, and that obsession with legendary outcomes is exactly what we look for.

Daniel’s history with GV goes back much further than mine—we were early investors in his first company, Kensho, in 2013. That relationship and track record made this decision even more compelling.

Daniel has secured partnerships with the American Medical Association, the New England Journal of Medicine, and the Mayo Clinic. He was also named to the TIME100 Health list as one of the most influential people in global health.

OpenEvidence is freeing physicians from drowning in exponentially growing medical knowledge, so they can focus on what matters most: caring for patients. This is the fastest-growing healthcare application ever built, and we’re just getting started.

GV is grateful to partner with Daniel and the entire OpenEvidence team as they build the future of medical information access.

第一次与丹尼尔会面时，我就想立即投资。但真正让我下定决心的，是来自我们生命科学团队的共同热情。我的生命科学同事们都是科班出身的医生和研究员，他们开始从全国医疗机构的同行那里听说OpenEvidence。亲自试用后，他们发现OpenEvidence给出的答案精准度远超其他平台。克里希纳·耶什万特医生是我们生命科学投资团队的联合负责人，他开始每天使用这个工具，取代了依赖多年的旧系统。

决定性时刻发生在丹尼尔的客厅里。克里希纳和我来自剑桥的同事们原计划进行90分钟的会议，结果我们六个人与丹尼尔讨论了一整天，当晚就当场决定投资。吸引我们的不仅是产品——更是丹尼尔本人。他是那种罕见的创始人，驱动力与常人不同。他恐惧的不是失败，而是无法成就真正的伟大。丹尼尔正在打造一个具有永恒复利效应的平台，全球医疗从业者已开始使用，这种对传奇成果的执着正是我们寻找的特质。

丹尼尔与GV的渊源远比我的参与更早——2013年我们就是他第一家公司Kensho的早期投资者。这段合作历史和成功记录让本次投资决策更具说服力。

丹尼尔已与美国医学会、《新英格兰医学杂志》和梅奥诊所建立合作，还被《时代》周刊评为全球健康领域最具影响力的TIME100健康人物之一。

OpenEvidence正在将医生们从指数级增长的医学知识海洋中解放出来，让他们能专注于最重要的事：救治患者。这是有史以来增长最快的医疗应用，而我们才刚刚启航。

GV很荣幸能与丹尼尔及整个OpenEvidence团队携手，共同构建医学信息获取的未来。

https://journals.stfm.org/familymedicine/2025/march/br-wu-0348/

Artificial intelligence (AI) may expand our options for resources to use as peripheral brains while we provide clinical care and teach. With the recent public explosion of large language models (LLM), to which we can turn for help with activities such as creating schedules, meals, and images in a conversational manner, potential exists for AI to also help us in our daily clinical and teaching activities. 1, 2 Some AI tools have also been shown to have high accuracy in choosing correct answers on medical licensing exams.3, 4 OpenEvidence (OE) is an LLM specifically trained for medicine with the aim “to aggregate, synthesize, and visualize clinically relevant evidence in understandable, accessible formats that can be used to make more evidence-based decisions and improve patient outcomes.” 5, 6 It was created by Daniel Nadler and developed with support from the Mayo Clinic Platform Accelerate program. 6, 7 It has also shown significant accuracy with answering board questions.8 It is now partnered with Elsevier’s ClinicalKey AI through paid subscription with the aim to deliver the “next-generation clinical decision support tool that combines the most recent and reputable evidence-based medical content with generative artificial intelligence (AI) to help physicians at the point of care.” 9

Clinicians and learners can set up an account for unlimited, free access to OE. OE is accessed via an internet browser. The clinical question is typed into the clearly labeled field on the middle of the screen. OE then provides a scholarly style response with citations provided within the text. The references are listed below the response with a “details” button that opens to show the text summarized for OE’s response. Clicking on the reference links directly to the PubMed abstract. OE also suggests relevant follow-up questions to further explore the topic.

For my review of OE, the same clinical questions were researched with OE, commonly used internet-based evidence-based clinical resources (DynaMed, UpToDate) and popular LLM’s (GPT4, Llama-3.1, CoPilot) during 2 weeks of direct patient care. All clinical resources and LLMs provided similar information. For broad questions, OE provided responses in less time than it takes to read through a clinical resource’s text. For very targeted questions, such as medication doses, OE took longer to provide a response. As with other LLMs, OE can analyze a deidentified patient history and provide a possible diagnosis and management plan. It can also suggest a response to patient messages. It does not craft board-style questions or create images.

Regarding the type of references provided by LLMs, OE cites recent articles from reputable journals and society guidelines. Copilot frequently cites other websites, GPT4 and Llama-3.1 do not provide citations within their responses. While a distinct advantage of OE over other LLMs is how it summarizes references, it can only access freely available information, such as abstracts, and not always the entire article. Reading the linked PubMed abstract helps the user quickly validate OE’s response.

The clinician-educator and learner will find OE useful for quickly finding a targeted answer to clinical questions while caring for patients. Learners can use it during clinical learning experiences to help them quickly formulate informed differential diagnoses and opinions for patient care. Whereas other point-of-care resources (eg, Amboss, UpToDate) are either subscription-based or too lengthy to sift through in the fast-paced clinic environment, one can quickly input patient-specific queries into OE and receive reliable responses in the time between seeing a patient and presenting to faculty.

While OE has strong utility as a targeted point-of-care clinical care resource, it may not be as useful as a comprehensive information tool. Due to the short length and concise focus of its responses, it does not readily provide expanded medical knowledge that is relevant to the topic, which may lead to early closure for the novice learner or tired clinician. Therefore, the clinician-educator and learner should be aware of unconscious gaps in knowledge and work together to strengthen their curiosity skills and how to ask high-yield clinical questions.

Overall, OE can provide responses to questions ranging from basic science knowledge to suggestions for patient evaluation and management. OE is not peer-reviewed and as emphasized in the terms of use, OE is not a substitute for clinical expertise and “does not provide medical advice, diagnosis or treatment.” 10 The clinician who uses OE still bears the responsibility of assessing the applicability and validity of the OE response to the clinical context. Nonetheless, as a free and reliable resource, OE is a welcome addition to the clinical toolbox to augment patient care and medical education.

人工智能（AI）或许能为我们提供更多"外脑"资源的选择，以辅助临床诊疗和教学工作。随着大型语言模型（LLM）的公众化爆发，这些能通过对话方式协助制定日程、规划膳食和生成图像的AI工具，也展现出助力日常临床与教学活动的潜力[1-2]。某些AI工具在医师资格考试中已展现出高准确度的答题能力[3-4]。

OpenEvidence（OE）是专为医学领域训练的LLM，其宗旨在于"以清晰易懂的形式聚合、整合并可视化临床证据，助力循证决策和改善患者预后"[5-6]。该平台由Daniel Nadler创建，并获梅奥诊所平台加速计划支持开发[6-7]，在专业考题应答方面表现出色[8]。目前通过与爱思唯尔ClinicalKey AI的付费合作，致力于打造"融合最新权威循证医学内容与生成式AI的新一代临床决策支持系统"[9]。

临床医师和医学生可注册获取OE的无限次免费使用权。通过浏览器访问平台后，在屏幕中央标注清晰的输入框键入临床问题，OE会生成带文献引用的学术式应答。参考文献列于回答下方，点击"详情"按钮可查看支撑应答的摘要文本，引用链接直接跳转至PubMed摘要。OE还会智能推荐相关延伸问题。

笔者在两周临床实践中，对比测试了OE与其他主流循证资源（DynaMed、UpToDate）及热门LLM（GPT4、Llama-3.1、CoPilot）对相同临床问题的应答表现。所有工具提供的信息大体相似：对于宽泛问题，OE的响应速度优于传统临床文献阅读；但在药物剂量等精准查询时响应较慢。与其他LLM类似，OE能分析匿名病历给出拟诊方案，也可草拟医患沟通回复，但不支持命题式问答或图像生成。

在文献引用方面，OE主要援引权威期刊最新论文和学会指南，Copilot多引用网页资源，GPT4和Llama-3.1则无引用功能。OE的核心优势在于文献整合能力，但仅能获取公开摘要而非全文。通过PubMed摘要可快速验证OE应答的可靠性。

临床教育者和学习者可将OE作为高效获取精准答案的床旁工具。医学生在轮转期间可用其快速形成鉴别诊断，而其他床旁资源（如Amboss、UpToDate）或因订阅门槛高、内容冗长难以适应快节奏临床环境，OE则能在接诊间隙快速提供可靠应答。

需注意的是，OE作为精准床旁工具虽强，却不适合深度知识拓展。其简明应答风格可能导致初学者或疲惫医师的认知闭环，因此使用者需保持批判思维，培养提出高价值临床问题的能力。

总体而言，OE能应答从基础科学到诊疗方案的全方位提问，但其未经同行评议，使用条款明确声明"不提供医疗建议、诊断或治疗"[10]。临床医师仍需自主评估OE应答的适用性。作为免费可靠的新工具，OE无疑为临床工作和医学教育增添了实用选择。