Learning medical triage from clinicians using Deep Q-Learning
Babylon Health（巴比伦健康）
Albert Buchard, Baptiste Bouvier, Giulia Prando, Rory Beard, Michail Livieratos, Dan Busbridge, Daniel Thompson, Jonathan Richens, Yuanzhao Zhang, Adam Baker, Y… 展开作者
Albert Buchard, Baptiste Bouvier, Giulia Prando, Rory Beard, Michail Livieratos, Dan Busbridge, Daniel Thompson, Jonathan Richens, Yuanzhao Zhang, Adam Baker, Yura Perov, Kostis Gourgoulias, Saurabh Johri
Medical Triage is of paramount importance to healthcare systems, allowing for the correct orientation of patients and allocation of the necessary resources to treat them adequately. While reliable decision-tree methods exist to triage patients based on their presentation, those trees implicitly require human inference and are not immediately applicable in a fully automated setting. On the other hand, learning triage policies directly from experts may correct for some of the limitations of hard-coded decision-trees. In this work, we present a Deep Reinforcement Learning approach (a variant of DeepQ-Learning) to triage patients using curated clinical vignettes. The dataset, consisting of 1374 clinical vignettes, was created by medical doctors to represent real-life cases. Each vignette is associated with an average of 3.8 expert triage decisions given by medical doctors relying solely on medical history. We show that this approach is on a par with human performance, yielding safe triage decisions in 94% of cases, and matching expert decisions in 85% of cases. The trained agent learns when to stop asking questions, acquires optimized decision policies requiring less evidence than supervised approaches, and adapts to the novelty of a situation by asking for more information. Overall, we demonstrate that a Deep Reinforcement Learning approach can learn effective medical triage policies directly from expert decisions, without requiring expert knowledge engineering. This approach is scalable and can be deployed in healthcare settings or geographical regions with distinct triage specifications, or where trained experts are scarce, to improve decision making in the early stage of care.
Our aim is to establish a framework where reinforcement learning (RL) of optimizing interventions retrospectively allows us a regulatory compliant pathway to prospective clinical testing of the learned policies in a clinical deployment. We focus on infections in intensive care units which are one of the major causes of death and difficult to treat because of the complex and opaque patient dynamics, and the clinically debated, highly-divergent set of intervention policies required by each individual patient, yet intensive care units are naturally data rich. In our work, we build on RL approache…
Do LLMs Triage Like Clinicians? A Dynamic Study of Outpatient Referral
Chinese University of Hong Kong, Shenzhen（香港中文大学（深圳））；Bournemouth University（伯恩茅斯大学）；National Health Data Institute, Shenzhen（深圳国家健康数据研究院）；Shenzhen Research Institute of Big Data（深圳大数据研究院）
Single-turn emergency psychiatric triage across 15 frontier AI chatbots
Max Planck UCL Centre for Computational Psychiatry and Ageing Research（马克斯·普朗克大学伦敦计算精神病学与衰老研究中心）；UK AI Security Institute（英国人工智能安全研究所）；Department of Experimental Psychology, University of Oxford（牛津大学实验心理学系）；Microsoft AI（微软人工智能）；Department of Psychiatry, University of Oxford（牛津大学精神病学系）
Beijing Institute of Technology（北京理工大学）；Tsinghua University（清华大学）
Sepsis is a leading cause of death in the ICU. It is a disease requiring complex interventions in a short period of time, but its optimal treatment strategy remains uncertain. Evidence suggests that the practices of currently used treatment strategies are problematic and may cause harm to patients. To address this decision problem, we propose a new medical decision model based on historical data to help clinicians recommend the best reference option for real-time treatment. Our model combines offline reinforcement learning and deep reinforcement learning to solve the problem of traditional rei…
Bespoke Large Language Models for Digital Triage Assistance in Mental Health Care
University of Oxford（牛津大学）；Oxford Precision Psychiatry Lab（牛津精准精神病学实验室）；NIHR Oxford Health Biomedical Research Centre（NIHR牛津健康生物医学研究中心）；Oxford Health NHS Foundation Trust（牛津健康NHS基金会信托）；University of Liverpool（利物浦大学）；Civic Health Innovation Labs（城市健康创新实验室）；Mental health Research for Innovation Centre (M-RIC)（心理健康创新研究中心）；Mersey Care NHS Foundation Trust（默西护理NHS基金会信托）
Applying state-of-the-art machine learning and natural language processing on approximately one million of teleconsultation records, we developed a triage system, now certified and in use at the largest European telemedicine provider. The system evaluates care alternatives through interactions with patients via a mobile application. Reasoning on an initial set of provided symptoms, the triage application generates AI-powered, personalized questions to better characterize the problem and recommends the most appropriate point of care and time frame for a consultation. The underlying technology w…
Prior to seeking professional medical care it is increasingly common for patients to use online resources such as automated symptom checkers. Many such systems attempt to provide a differential diagnosis based on the symptoms elucidated from the user, which may lead to anxiety if life or limb-threatening conditions are part of the list, a phenomenon termed 'cyberchondria' [1]. Systems that provide advice on where to seek help, rather than a diagnosis, are equally popular, and in our view provide the most useful information. In this technical report we describe how such a triage system can be m…
SafeMed-R1: Clinician-Audited Safety and Ethics Alignment for Medical Large Language Models
Shanghai Artificial Intelligence Laboratory（上海人工智能实验室）；Joint Laboratory of Biomedical Artificial Intelligence（生物医学人工智能联合实验室）；Shanghai Institute of Infectious Disease and Biosecurity（上海传染病与生物安全研究院）；Shanghai Health Development Research Center (Shanghai Medical Information Center)（上海健康发展战略研究中心（上海医疗信息中心））；University of Washington（华盛顿大学）；Department of Eye and Vision Sciences, University of Liverpool（利物浦大学眼科与视觉科学系）；Liverpool Centre for Cardiovascular Science, University of Liverpool（利物浦大学心血管科学中心）；School of Computer Science and Technology, Tongji University（同济大学计算机科学与技术学院）
Multi-agent Self-triage System with Medical Flowcharts
Department of Electrical and Computer Engineering, University of California San Diego（加州大学圣地亚哥分校电气与计算机工程系）；The Design Lab, University of California San Diego（加州大学圣地亚哥分校设计实验室）；Department of Computer Science and Engineering, University of California San Diego（加州大学圣地亚哥分校计算机科学与工程系）；Department of Medicine, Kaiser Permanente（Kaiser 医疗集团医学部）；Department of Radiation Oncology, University of California San Francisco（加州大学旧金山分校放射肿瘤学部）；Department of Emergency Medicine, University of California San Francisco（加州大学旧金山分校急诊医学部）；Division of Cardiology, Department of Medicine, University of California San Diego（加州大学圣地亚哥分校医学部心内科分会）；Department of Orthopaedic Surgery, Korea University Ansan Hospital（韩国大学安山医院骨科部）；Google Research, Seattle, WA, USA（谷歌研究（西雅图，华盛顿州））；Paul G. Allen School of Computer Science & Engineering, University of Washington（华盛顿大学保罗·G·阿伦计算机科学与工程学校）
Prompt Triage: Structured Optimization Enhances Vision-Language Model Performance on Medical Imaging Benchmarks
Stanford University, Department of Computer Science, Stanford, CA, USA（斯坦福大学计算机科学系）；Stanford University, Department of Biomedical Data Science, Stanford, CA, USA（斯坦福大学生物医学数据科学系）；Stanford University, Stanford Institute for Human-Centered Artificial Intelligence, Stanford, CA, USA（斯坦福大学人类中心人工智能研究所）；Stanford University, School of Medicine, Department of Dermatology, Stanford, CA, USA（斯坦福大学医学院皮肤科系）；Stanford University, Department of Radiology, Stanford, CA, USA（斯坦福大学放射科）
RIKEN Center for Integrative Medical Sciences（日本理化学研究所整合医学研究中心）；RIKEN Tokyo Japan（日本理化学研究所东京日本）；RIKEN Center for Interdisciplinary Theoretical and Mathematical Sciences（日本理化学研究所跨学科理论与数学科学中心）；Faculty of Medicine Institute of Science Tokyo Tokyo Japan（医学系科学研究所东京东京日本）
MedObvious: Exposing the Medical Moravec's Paradox in VLMs via Clinical Triage
Mohamed bin Zayed University of Artificial Intelligence, UAE（马尔代夫布扎伊德人工智能大学，阿联酋）；National Institute of Technology, Silchar（西尔CHAR国家理工学院）；Birmingham City University, UK（伯明翰城市大学，英国）
Although machine learning (ML) models of AI achieve high performances in medicine, they are not free of errors. Empowering clinicians to identify incorrect model recommendations is crucial for engendering trust in medical AI. Explainable AI (XAI) aims to address this requirement by clarifying AI reasoning to support the end users. Several studies on biomedical imaging achieved promising results recently. Nevertheless, solutions for models using tabular data are not sufficient to meet the requirements of clinicians yet. This paper proposes a methodology to support clinicians in identifying fail…
MedAlign: A Clinician-Generated Dataset for Instruction Following with Electronic Medical Records
Stanford School of Medicine（斯坦福大学医学院）；Stanford School of Engineering（斯坦福大学工程学院）；Stanford University（斯坦福大学）；Stanford Health Care（斯坦福医疗保健）；Vanderbilt University School of Medicine（范德比尔特大学医学院）；Vanderbilt University Medical Center（范德比尔特大学医学中心）；Hospital Israelita Albert Einstein（以色列阿尔伯特·爱因斯坦医院）
Clinical Validation of Medical-based Large Language Model Chatbots on Ophthalmic Patient Queries with LLM-based Evaluation
Singapore National Eye Centre（新加坡国家眼科中心）；Singapore Eye Research Institute（新加坡眼科研究所）；Singapore Health Services（新加坡保健服务集团）；Medical University of Vienna（维也纳医科大学）；The Hospital at Maayo（马约医院）；Duke-NUS Medical School（杜克-新加坡国立大学医学院）；London School of Hygiene and Tropical Medicine（伦敦卫生与热带医学院）；Chang Gung Memorial Hospital（长庚纪念医院）；Taipei Medical University（台北医学大学）；National Health Research Institutes（国家卫生研究院）；Nanyang Technological University（南洋理工大学）
Development of a Large Language Model-based Multi-Agent Clinical Decision Support System for Korean Triage and Acuity Scale (KTAS)-Based Triage and Treatment Planning in Emergency Departments
John Bapst Memorial High School（约翰·巴普斯特纪念高中）；Minerva University（密涅瓦大学）
Pohang University of Science and Technology（浦项科技大学）；Basic Science Research Institute, Pohang University of Science and Technology（浦项科技大学基础科学研究所）；Seoul National University Bundang Hospital（首尔大学盆唐医院）
A prompt severity assessment model of patients confirmed for having infectious diseases could enable efficient diagnosis while alleviating burden on the medical system. This study aims to develop a SARS-CoV-2 severity assessment model and establish a medical system that allows patients to check the severity of their cases and informs them to visit the appropriate clinic center based on past treatment data of other patients with similar severity levels. This paper provides the development processes of a severity assessment model using machine learning techniques and its application on SARS-CoV-…
A Clinician-Centered Pipeline for Annotation and Evaluation in Ultrasound AI Studies
Research Ireland Centre for Research Training in Machine Learning（爱尔兰研究机器学习研究中心）；School of Medicine, University College Dublin, Dublin, Ireland（都柏林大学医学院）；The Third People's Hospital of Zhenjiang City, Zhenjiang, China（镇江市第三人民医院）；Zhenjiang Maternal and Child Health Hospital, Zhenjiang, China（镇江 maternal and child health hospital）；The Fifth People's Hospital of Zhenjiang City, Zhenjiang, China（镇江市第五人民医院）；School of Computer Science, University College Dublin, Dublin, Ireland（都柏林大学计算机科学学院）
MAP: Evaluation and Multi-Agent Enhancement of Large Language Models for Inpatient Pathways
The Chinese University of Hong Kong（香港中文大学）；Guangzhou Hospital of Integrated Traditional Chinese and Western Medicine（广州市中西医结合医院）；Shenzhen Traditional Chinese Medicine Hospital（深圳市中医院）；Zhongshan Hospital Fudan University（复旦大学附属中山医院）
AmarDoctor: An AI-Driven, Multilingual, Voice-Interactive Digital Health Application for Primary Care Triage and Patient Management to Bridge the Digital Health Divide for Bengali Speakers
MedAi Bangladesh Limited（MedAi孟加拉有限公司）；Central Police Hospital, Dhaka, Bangladesh（达卡警察医院）；University of Birmingham, UK（英国伯明翰大学）
Elsevier, Health and Commercial Markets（爱思唯尔，健康与商业市场部）
Clinicians face several significant barriers to search and synthesize accurate, succinct, updated, and trustworthy medical information from several literature sources during the practice of medicine and patient care. In this talk, we will be presenting our research behind the development of a Focused Clinical Search Service, powered by a Healthcare Knowledge Graph, to interpret the query intent behind clinical search queries and retrieve relevant medical snippets from a diverse corpus of medical literature.
Deployment and Evaluation of an EHR-integrated, Large Language Model-Powered Tool to Triage Surgical Patients
Division of Hospital Medicine, Department of Medicine, Stanford University（斯坦福大学医院医学部，医学部）；Stanford University School of Medicine（斯坦福大学医学院）；Stanford Health Care, Technology and Digital Solutions（斯坦福健康，技术与数字解决方案）；Division of Computational Medicine, Stanford University（斯坦福大学计算医学部）；Stanford Health Care, Nursing Informatics（斯坦福健康，护理信息学）
Clinical documentation increasingly uses automatic speech recognition and summarization, yet converting conversations into actionable medical orders for Electronic Health Records remains unexplored. A solution to this problem can significantly reduce the documentation burden of clinicians and directly impact downstream patient care. We introduce the MEDIQA-OE 2025 shared task, the first challenge on extracting medical orders from doctor-patient conversations. Six teams participated in the shared task and experimented with a broad range of approaches, and both closed- and open-weight large lang…
