In this paper, we reflect on ways to improve the quality of bio-medical information retrieval by drawing implicit negative feedback from negated information in noisy natural language search queries. We begin by studying the extent to which negations occur in clinical texts and quantify their detrimental effect on retrieval performance. Subsequently, we present a number of query reformulation and ranking approaches that remedy these shortcomings by resolving natural language negations. Our experimental results are based on data collected in the course of the TREC Clinical Decision Support Track and show consistent improvements compared to state-of-the-art methods. Using our novel algorithms, we are able to reduce the negative impact of negations on early precision by up to 65%.
Retrieval, not hallucinations, will be the limiting factor for LLM-based clinical AI tools
The University of Texas Health Science Center at Houston（德克萨斯大学休斯顿健康科学中心）；Oregon Health & Science University（俄勒冈健康与科学大学）；Mayo Clinic（梅奥诊所）；The University of Texas at Austin（德克萨斯大学奥斯汀分校）
PinView: Implicit Feedback in Content-Based Image Retrieval
University College London（伦敦大学学院）；Aalto University（阿尔托大学）；University of Helsinki（赫尔辛基大学）；University of Leoben（莱奥本大学）；King Mongkut’s Institute of Technology Ladkrabang（先皇理工学院）
Lab-AI: Using Retrieval Augmentation to Enhance Language Models for Personalized Lab Test Interpretation in Clinical Medicine
Department of Statistics（统计学系）；Florida State University（佛罗里达州立大学）；School of Information（信息学院）；Oklahoma State University（俄克拉荷马州立大学）；Morsani College of Medicine（医学学院）；University of South Florida（佛罗里达大学）；College of Social Work（社会工作学院）；Department of Computer Science（计算机科学系）；Emory University（埃默里大学）
Query-free Clothing Retrieval via Implicit Relevance Feedback
Cooperative Medianet Innovation Center, Shanghai Jiao Tong University（上海交通大学协同媒体创新中心）；Institute of Image Communication and Network Engineering, Shanghai Jiao Tong University（上海交通大学图像通信与网络工程研究所）
A Hybrid Citation Retrieval Algorithm for Evidence-based Clinical Knowledge Summarization: Combining Concept Extraction, Vector Similarity and Query Expansion for High Precision
Northwestern University Feinberg School of Medicine（西北大学范伯格医学院）；Bluhm Cardiovascular Institute of Northwestern Medicine（西北大学布鲁姆心血管研究所）；University of Kansas Medical Center（堪萨斯大学医学中心）
RAG4Outcome: A Retrieval-Augmented Multimodal Framework for Prognostic Prediction in Chronic Osteomyelitis
Queen Mary University of London（女王玛丽大学）；Shanghai Sixth People’s Hospital Affiliated to SJTU School of Medicine（上海第六人民医院附属复旦大学医学院）；University College London（大学学院伦敦）
Deep Retrieval at CheckThat! 2025: Identifying Scientific Papers from Implicit Social Media Mentions via Hybrid Retrieval and Re-Ranking
University of Zurich（苏黎世大学）；Centre for Artificial Intelligence（人工智能中心）；Zurich University of Applied Sciences（应用科学大学）；Institute of Neuroinformatics（神经信息学研究所）；European Centre for Living Technology（欧洲生活技术中心）
Learning Implicit User Profiles for Personalized Retrieval-Based Chatbot
Gaoling School of Artificial Intelligence, Renmin University of China（中国人民大学高瓴人工智能学院）；Beijing Key Laboratory of Big Data Management and Analysis Methods（大数据管理与分析方法北京市重点实验室）；Université de Montréal（蒙特利尔大学）
Generative AI Is Not Ready for Clinical Use in Patient Education for Lower Back Pain Patients, Even With Retrieval-Augmented Generation
University of Pittsburgh（匹兹堡大学）；Stern School of Business, New York University（纽约大学斯特恩商学院）；New York University（纽约大学）；Alliance Physical Therapy（联合物理治疗公司）；Athletico Physical Therapy（Athletico物理治疗公司）；University of Pittsburgh Medical Center（匹兹堡大学医学中心）
Grounding Large Language Models in Clinical Evidence: A Retrieval-Augmented Generation System for Querying UK NICE Clinical Guidelines
Institute of Health Informatics, University College London（健康信息学研究所，伦敦大学学院）；Department of Biostatistics and Health Informatics, King’s College London（生物统计学与健康信息学系，伦敦国王学院）；EPSRC DRIVE-Health CDT, London, U.K.（EPSRC DRIVE-Health CDT，伦敦，英国）；Imperial College Healthcare NHS Trust（帝国学院医疗 NHS 信托）；Black Country Healthcare NHS Foundation Trust（黑斯廷斯地区医疗 NHS 基础信托）；Feldon Lane Surgery, The Dudley Group NHS Foundation Trust（费尔顿车道诊所，德比集团 NHS 基础信托）；The Cleveland Clinic, London, U.K.（克利夫兰诊所，伦敦，英国）；CogStack Limited（CogStack 有限公司）
Lessons Learned on Information Retrieval in Electronic Health Records: A Comparison of Embedding Models and Pooling Strategies
University of Wisconsin-Madison（威斯康星大学麦迪逊分校）；Boston Children’s Hospital Harvard Medical School（波士顿儿童医院哈佛医学院）；University of Colorado Anschutz（科罗拉多大学安舒茨分校）；Loyola University Chicago（芝加哥洛约拉大学）
EHR-MCP: Real-world Evaluation of Clinical Information Retrieval by Large Language Models via Model Context Protocol
Department of Radiology, Keio University Hospital, Tokyo, Japan（Keio大学医院放射科）；Division of Infectious Diseases and Infection Control, Keio University Hospital, Tokyo, Japan（Keio大学医院感染病学与感染控制科）
