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 was developed to meet the needs for performance, transparency, user acceptance and ease of use, central aspects to the adoption of AI-based decision support systems. Providing such remote guidance at the beginning of the chain of care has significant potential for improving cost efficiency, patient experience and outcomes. Being remote, always available and highly scalable, this service is fundamental in high demand situations, such as the current COVID-19 outbreak.
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 D…
Babylon Health（巴比伦健康）；Imperial College London（帝国理工学院）；Yale New Haven Health（耶鲁纽黑文健康）；Stanford University（斯坦福大学）
Online symptom checkers have significant potential to improve patient care, however their reliability and accuracy remain variable. We hypothesised that an artificial intelligence (AI) powered triage and diagnostic system would compare favourably with human doctors with respect to triage and diagnostic accuracy. We performed a prospective validation study of the accuracy and safety of an AI powered triage and diagnostic system. Identical cases were evaluated by both an AI system and human doctors. Differential diagnoses and triage outcomes were evaluated by an independent judge, who was blinde…
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）
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…
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）
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）
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-…
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）
Nanyang Technological University（南洋理工大学）；Alibaba Group Holding Limited（阿里巴巴集团控股有限公司）
Recently, artificial intelligence and machine learning in general have demonstrated remarkable performances in many tasks, from image processing to natural language processing, especially with the advent of deep learning. Along with research progress, they have encroached upon many different fields and disciplines. Some of them require high level of accountability and thus transparency, for example the medical sector. Explanations for machine decisions and predictions are thus needed to justify their reliability. This requires greater interpretability, which often means we need to understand t…
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）
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…
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）
Domain-aware machine learning (ML) models have been increasingly adopted for accelerating small molecule therapeutic design in the recent years. These models have been enabled by significant advancement in state-of-the-art artificial intelligence (AI) and computing infrastructures. Several ML architectures are pre-dominantly and independently used either for predicting the properties of small molecules, or for generating lead therapeutic candidates. Synergetically using these individual components along with robust representation and data generation techniques autonomously in closed loops hold…
One Patient, Many Contexts: Scaling Medical AI with Contextual Intelligence
Harvard Medical School（哈佛医学院）；Clalit Research Institute（Clalit研究所）；Boston Children’s Hospital（波士顿儿童医院）；Harvard Data Science Initiative（哈佛数据科学计划）；Beth Israel Deaconess Medical Center（贝斯以色列女执事医疗中心）；Harvard T.H. Chan School of Public Health（哈佛大学陈曾熙公共卫生学院）；Clalit Health Services（Clalit医疗服务集团）；Ben Gurion University（本-古里安大学）；Ben Gurion University of the Negev（内盖夫本-古里安大学）；Brigham and Women’s Hospital（布莱根妇女医院）；Harvard University（哈佛大学）；Broad Institute of MIT and Harvard（麻省理工学院与哈佛大学布罗德研究所）
Artificial General Intelligence for Medical Imaging Analysis
Massachusetts General Hospital（麻省总医院）；Harvard Medical School（哈佛医学院）；The University of Texas at Arlington（德克萨斯大学阿灵顿分校）；The University of Georgia（佐治亚大学）；Beijing Jiaotong University（北京交通大学）；Chinese University of Hong Kong（香港中文大学）；Second Xiangya Hospital（湘雅二医院）
Aalto University（阿尔托大学）；KTH Royal Institute of Technology（瑞典皇家理工学院）
In the present paper we present the potential of Explainable Artificial Intelligence methods for decision-support in medical image analysis scenarios. With three types of explainable methods applied to the same medical image data set our aim was to improve the comprehensibility of the decisions provided by the Convolutional Neural Network (CNN). The visual explanations were provided on in-vivo gastral images obtained from a Video capsule endoscopy (VCE), with the goal of increasing the health professionals' trust in the black box predictions. We implemented two post-hoc interpretable machine l…
Harvard Medical School（哈佛医学院）；Aetna, CVS Health（安泰保险，CVS健康公司）
Health information is generally fragmented across silos. Though it is technically feasible to unite data for analysis in a manner that underpins a rapid learning healthcare system, privacy concerns and regulatory barriers limit data centralization. Machine learning can be conducted in a federated manner on patient datasets with the same set of variables, but separated across sites of care. But federated learning cannot handle the situation where different data types for a given patient are separated vertically across different organizations and when patient ID matching across different institu…
MedRLM: Recursive Multimodal Health Intelligence for Long-Context Clinical Reasoning, Sensor-Guided Screening, Evidence-Grounded Decision Support, and Community-to-Tertiary Referral Optimization
School of Information, Computer；Communication Technology Sirindhorn International Institute of Technology, Thammasat University Pathum Thani, Thailand 1
The complexities of patient-centred conversational artificial intelligence
Verily Health（Verily健康公司）；University of Oxford（牛津大学）；University of Birmingham（伯明翰大学）；University Hospitals Birmingham NHS Foundation Trust（伯明翰大学医院NHS基金会信托）；Harvard Medical School（哈佛医学院）；Beth Israel Deaconess Medical Center（贝斯以色列女执事医疗中心）
Reasoning in Real World Clinical Care: Why Large Language Models Are Not Yet Safe for Autonomous Clinical Decision Support
Atman Labs（阿特曼实验室）；Oxford University Hospitals（牛津大学医院）；University of Oxford（牛津大学）；NIHR Oxford Biomedical Research Centre（英国国立卫生研究院牛津生物医学研究中心）；Imperial College London（伦敦帝国理工学院）；Technical University of Munich（慕尼黑工业大学）；Massachusetts General Hospital（麻省总医院）；Mass General Brigham（麻省总医院布里格姆医疗系统）；Harvard Medical School（哈佛医学院）；Barts Health NHS Trust（巴特保健国民保健信托基金会）；the University of Texas at Austin（德克萨斯大学奥斯汀分校）
Preserving Privacy, Increasing Accessibility, and Reducing Cost: An On-Device Artificial Intelligence Model for Medical Transcription and Note Generation
PathFinder: A Multi-Modal Multi-Agent System for Medical Diagnostic Decision-Making Applied to Histopathology
University of Washington（华盛顿大学）；Microsoft Research（微软研究院）；David Geffen School of Medicine, UCLA（加州大学洛杉矶分校大卫·格芬医学院）；Allen Institute for AI (AI2)（艾伦人工智能研究所）
Hierarchical Dual-Strategy Unlearning for Biomedical and Healthcare Intelligence Using Imperfect and Privacy-Sensitive Medical Data
Department of Neurosurgery, Qilu Hospital of Shandong University and Institute of Brain and Brain-Inspired Science（齐鲁医院山东大学神经外科和脑科学与脑启发科学研究院）；Department of Neurosurgery, The Affiliated Hospital of Qingdao University（青岛大学附属医院神经外科）；Department of Neurosurgery, Peking Union Medical College Hospital（北京地坛医院神经外科）；School of Software and Microelectronics, Peking University（北京大学软件与微电子学院）；College of Artificial Intelligence, Dalian Maritime University（大连海事大学人工智能学院）；University of Colorado Boulder（科罗拉多大学波尔德分校）；Zhengzhou University（郑州大学）；The University of Hong Kong（香港大学）；Georgia Institute of Technology（佐治亚理工学院）
FUTURE-AI: Guiding Principles and Consensus Recommendations for Trustworthy Artificial Intelligence in Medical Imaging
Barcelona Artificial Intelligence in Medicine Lab (BCN-AIM)（巴塞罗那医学人工智能实验室（BCN-AIM））；Maggioli SPA（马焦利股份公司）；Fundació TIC Salut i Social（信息与通信技术社会基金会）；Generalitat de Catalunya（加泰罗尼亚政府）；Hospital Universitario y Politécnico La Fe（拉费大学及综合医院）；Health Research Institute（健康研究所）；Foundation for Research and Technology（研究与技术基金会）；National Research Council（国家研究委员会）；Maastricht University（马斯特里赫特大学）
Weill Cornell Medical Centre（威尔康奈尔医学中心）；Brigham and Women’s Hospital（布莱根妇女医院）；Boston Children’s Hospital（波士顿儿童医院）
Medical toxicology is the clinical specialty that treats the toxic effects of substances, be it an overdose, a medication error, or a scorpion sting. The volume of toxicological knowledge and research has, as with other medical specialties, outstripped the ability of the individual clinician to entirely master and stay current with it. The application of machine learning techniques to medical toxicology is challenging because initial treatment decisions are often based on a few pieces of textual data and rely heavily on prior knowledge. ML techniques often do not represent knowledge in a way t…
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）
The Ohio State University（俄亥俄州立大学）；The Abigail Wexner Research Institute at Nationwide Children’s Hospital（全国儿童医院阿比盖尔·韦克斯纳研究所）
Trauma mortality results from a multitude of non-linear dependent risk factors including patient demographics, injury characteristics, medical care provided, and characteristics of medical facilities; yet traditional approach attempted to capture these relationships using rigid regression models. We hypothesized that a transfer learning based machine learning algorithm could deeply understand a trauma patient's condition and accurately identify individuals at high risk for mortality without relying on restrictive regression model criteria. Anonymous patient visit data were obtained from years …
Ontology-based knowledge representation for bone disease diagnosis: a foundation for safe and sustainable medical artificial intelligence systems
University of Science, VNUHCM（胡志明市国家大学科学大学）；Viet Nam National University, Ho Chi Minh City（越南国家大学胡志明市分校）；Ho Chi Minh City Hospital for Traumatology and Orthopedics（胡志明市创伤与骨科医院）
