Although Large Language Models (LLMs) have shown promising performance in healthcare-related applications, their deployment in the medical domain poses unique challenges of ethical, regulatory, and technical nature. In this study, we employ a systematic participatory approach to investigate the needs and expectations regarding clinical applications of LLMs at Lausanne University Hospital, an academic medical center in Switzerland. Having identified potential LLM use-cases in collaboration with thirty stakeholders, including clinical staff across 11 departments as well nursing and patient representatives, we assess the current feasibility of these use-cases taking into account the regulatory frameworks, data protection regulation, bias, hallucinations, and deployment constraints. This study provides a framework for a participatory approach to identifying institutional needs with respect to introducing advanced technologies into healthcare practice, and a realistic analysis of the technology readiness level of LLMs for medical applications, highlighting the issues that would need to be overcome LLMs in healthcare to be ethical, and regulatory compliant.
Phase 1 Implementation of LLM-generated Discharge Summaries showing high Adoption in a Dutch Academic Hospital
Department of Health Information Office, Data & Digitalization, University Medical Center（健康信息办公室、数据与数字化化大学医院）；Department of Trauma Surgery / Orthopedics, University Medical Center（创伤外科/骨科大学医院）；Department of Orthopaedic Surgery, Massachusetts General Hospital（骨科手术科马萨诸塞总医院）；Harvard Medical School Orthopaedic Trauma Initiative（哈佛医学院骨创伤倡议）；Department of Orthopaedic Surgery and Sports Medicine, Amsterdam UMC location University of Amsterdam（骨科手术与运动医学部门，阿姆斯特丹UMC地点大学）；Department of Orthopaedic Trauma, Flinders University Medical Centre（骨创伤部门，弗林德斯大学医疗中心）；Department of Otolaryngology – Head and Neck Surgery, University Medical Center（耳鼻喉科-头颈外科大学医院）；Department of Pediatrics, Pediatrics Nephrology, Beatrix Children’s Hospital, University Medical Center（儿科、儿科肾病学，比西特儿童医院，大学医院）
Red Teaming for Generative AI, Report on a Copyright-Focused Exercise Completed in an Academic Medical Center
Dana-Farber Cancer Institute（丹娜-法伯癌症研究所）；National Institutes of Health（美国国立卫生研究院）；Beth Israel Deaconess Medical Center（贝斯以色列女执事医疗中心）；Weill Cornell Medicine（威尔康奈尔医学院）；MLCommons；Microsoft Corporation（微软公司）；Massachusetts Institute of Technology（麻省理工学院）；Institute of Medical Informatics, Charité（夏里特医学院医学信息学研究所）；Harvard Medical School（哈佛医学院）；University of Toronto（多伦多大学）；Harvard T.H. Chan School of Public Health（哈佛大学陈曾熙公共卫生学院）；Boston University（波士顿大学）；Veraitech；Argonne National Laboratory（阿贡国家实验室）；Reins AI；Northeastern University（东北大学）；Code for America
Artificial Intelligence (AI) and Machine-Learning (ML) models have been increasingly used in medical products, such as medical device software. General considerations on the statistical aspects for the evaluation of AI/ML-enabled medical diagnostic devices are discussed in this paper. We also provide relevant academic references and note good practices in addressing various statistical challenges in the clinical validation of AI/ML-enabled medical devices in the context of their intended use.
National University of Singapore（新加坡国立大学）；Imperial College London（帝国理工学院）；National Institute of Technology, Tiruchirappalli（蒂鲁吉拉伯利国家理工学院）；Chinese University of Hong Kong（香港中文大学）；Shun Hing Institute of Advanced Engineering（信兴高等工程研究所）
Visual question answering (VQA) in surgery is largely unexplored. Expert surgeons are scarce and are often overloaded with clinical and academic workloads. This overload often limits their time answering questionnaires from patients, medical students or junior residents related to surgical procedures. At times, students and junior residents also refrain from asking too many questions during classes to reduce disruption. While computer-aided simulators and recording of past surgical procedures have been made available for them to observe and improve their skills, they still hugely rely on medic…
Reviewing Clinical Knowledge in Medical Large Language Models: Training and Beyond
School of Computer Science and Technology, Wuhan University of Science and Technology（计算机科学与技术学院，武汉科技大学）；Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System（湖北省智能信息处理与实时工业系统重点实验室）；School of Computer Science and Technology, Huazhong University of Science and Technology（计算机科学与技术学院，华中科技大学）；School of Cyber Science and Engineering, Wuhan University（网络科学与工程学院，武汉大学）；Department of Computer Science, Vrije Universiteit Amsterdam（计算机科学系，阿姆斯特丹自由大学）
Superhuman performance of a large language model on the reasoning tasks of a physician
Beth Israel Deaconess Medical Center（贝斯以色列女执事医疗中心）；Harvard Medical School（哈佛医学院）；Stanford University（斯坦福大学）；Cambridge Health Alliance（剑桥健康联盟）；Brigham and Women's Hospital（布列根和妇女医院）；University of Minnesota Medical School（明尼苏达大学医学院）；University of Maryland School of Medicine（马里兰大学医学院）；Veterans Affairs Maryland Healthcare System（马里兰退伍军人医疗保健系统）；VA Palo Alto Health Care System（帕洛阿尔托退伍军人医疗保健系统）；Massachusetts Institute of Technology（麻省理工学院）；University of Alberta（阿尔伯塔大学）；Beth Israel Lahey Health（贝斯以色列拉海健康）；Massachusetts General Hospital（麻省总医院）；Microsoft Corp（微软公司）
CAT-ViL: Co-Attention Gated Vision-Language Embedding for Visual Question Localized-Answering in Robotic Surgery
Wellcome/EPSRC Centre for Interventional and Surgical Sciences (WEISS), University College London（伦敦大学学院惠康/工程与物理科学研究委员会介入与外科科学中心（WEISS））；The Chinese University of Hong Kong (CUHK)（香港中文大学（CUHK））；Shun Hing Institute of Advanced Engineering, CUHK（香港中文大学信兴高等工程研究所）
This work bridges the technical concepts underlying distributed computing and blockchain technologies with their profound socioeconomic and sociopolitical implications, particularly on academic research and the healthcare industry. Several examples from academia, industry, and healthcare are explored throughout this paper. The limiting factor in contemporary life sciences research is often funding: for example, to purchase expensive laboratory equipment and materials, to hire skilled researchers and technicians, and to acquire and disseminate data through established academic channels. In the …
Beyond Static Knowledge Messengers: Towards Adaptive, Fair, and Scalable Federated Learning for Medical AI
Department of Computer Science and Software Engineering, Auburn University（计算机科学与软件工程系，阿伯丁大学）；Department of Information and Communication Technology, Bangladesh University of Professionals（信息与通信技术系，孟加拉国专业大学）；Mymensingh Medical College and Hospital（迈明辛医疗学院和医院）
Oxford Internet Institute, University of Oxford（牛津大学牛津互联网研究所）；the Alan Turing Institute（阿兰·图灵研究所）
AI Ethics is now a global topic of discussion in academic and policy circles. At least 84 public-private initiatives have produced statements describing high-level principles, values, and other tenets to guide the ethical development, deployment, and governance of AI. According to recent meta-analyses, AI Ethics has seemingly converged on a set of principles that closely resemble the four classic principles of medical ethics. Despite the initial credibility granted to a principled approach to AI Ethics by the connection to principles in medical ethics, there are reasons to be concerned about i…
The Integration of Artificial Intelligence in Undergraduate Medical Education in Spain: Descriptive Analysis and International Perspectives
Galicia Sur Health Research Institute（加利西亚南方健康研究所）；Clinical Hospital San Carlos（圣卡洛斯临床医院）；San Carlos Health Research Institute（圣卡洛斯健康研究所）；Complutense University of Madrid Medical School（马德里康普顿斯大学医学院）；Valdecilla Research Institute（巴尔德西利亚研究所）；University of Murcia（穆尔西亚大学）；University of Barcelona（巴塞罗那大学）；Hospital Clínic（医院诊所）；Pontevedra University Hospital Complex（蓬特韦德拉大学医院综合体）；MedicineAI
A Toolbox for Surfacing Health Equity Harms and Biases in Large Language Models
Google Research（谷歌研究院）；Google DeepMind（谷歌DeepMind）；University of Alberta（阿尔伯塔大学）；Massachusetts Institute of Technology（麻省理工学院）；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（哈佛陈曾熙公共卫生学院）
