For the diagnostic inference under uncertainty Bayesian networks are investigated. The method is based on an adequate uniform representation of the necessary knowledge. This includes both generic and experience-based specific knowledge, which is stored in a knowledge base. For knowledge processing, a combination of the problem-solving methods of concept-based and case-based reasoning is used. Concept-based reasoning is used for the diagnosis, therapy and medication recommendation and evaluation of generic knowledge. Exceptions in the form of specific patient cases are processed by case-based reasoning. In addition, the use of Bayesian networks allows to deal with uncertainty, fuzziness and incompleteness. Thus, the valid general concepts can be issued according to their probability. To this end, various inference mechanisms are introduced and subsequently evaluated within the context of a developed prototype. Tests are employed to assess the classification of diagnoses by the network.
Examining the Vulnerability of Multi-Agent Medical Systems to Human Interventions for Clinical Reasoning
Stanford University（斯坦福大学）；Jordan High School（乔丹高中）；UC San Diego（加利福尼亚大学圣迭戈分校）；Winchester High School（温彻斯特高中）；James Logan High School（詹姆斯·洛根高中）；Rutgers University（罗格斯大学）；Foothill–De Anza College（福希尔-德安扎学院）；Fordham University（福特汉姆大学）；Chapman University（查普曼大学）；UC Berkeley（加利福尼亚大学伯克利分校）
Quantifying Self-diagnostic Atomic Knowledge in Chinese Medical Foundation Model: A Computational Analysis
School of Computer Science and Technology, Soochow University（苏州大学计算机科学与技术学院）；School of Data Science, The Chinese University of Hong Kong, Shenzhen（香港中文大学（深圳）数据科学学院）；Shenzhen Research Institute of Big Data（深圳大数据研究院）；University of Science and Technology of China（中国科学技术大学）
Inflated Excellence or True Performance? Rethinking Medical Diagnostic Benchmarks with Dynamic Evaluation
GSAI, Renmin University of China（清华大学人工智能研究院，中国人民大学）；Tencent Jarvis Lab（腾讯 Jarvis实验室）；Beijing Key Laboratory of Research on Large Models and Intelligent Governance（北京大型模型与智能治理研究关键实验室）；Engineering Research Center of Next-Generation Intelligent Search and Recommendation, MOE（下一代智能搜索与推荐工程技术研究中心，教育部）
Pioneer Alpha（先锋阿尔法）；University of Alabama at Birmingham（阿拉巴马大学伯明翰分校）；Medical College of Georgia at Augusta University（奥古斯塔大学佐治亚医学院）；University of Memphis（孟菲斯大学）
Learning to Be A Doctor: Searching for Effective Medical Agent Architectures
AGI Lab, Westlake University（西拉雅大学AGI实验室）；Henan University（河南大学）；Affiliated Hospital of Xuzhou Medical University（徐州医科大学附属医院）；Xuzhou Medical University（徐州医科大学）；Nanyang Technological University（南洋理工大学）；IHPC, Agency for Science, Technology and Research, Singapore（新加坡科技研究局IHPC）；CFAR, Agency for Science, Technology and Research, Singapore（新加坡科技研究局CFAR）
From Medical Records to Diagnostic Dialogues: A Clinical-Grounded Approach and Dataset for Psychiatric Comorbidity
X - LANCE Lab, Shanghai Jiao Tong University, China（上海交通大学X-LANCE实验室）；Shanghai Mental Health Center, SJTU School of Medicine, China（上海精神卫生中心，上海交通大学医学院）；Chen Frontier Lab for AI and Mental Health, Tianqiao and Chrissy Chen Institute, Shanghai, China（陈前沿人工智能与心理健康实验室，田青和克里斯蒂娜·陈研究所）
Beyond Uncertainty Reduction: Calibrated Planning for Interactive Diagnosis
Lanzhou University（兰州大学）；Hunan University（湖南大学）；National University of Singapore（新加坡国立大学）；City University of Hong Kong（香港城市大学）；The Ohio State University（俄亥俄州立大学）
Comparisons between a Large Language Model-based Real-Time Compound Diagnostic Medical AI Interface and Physicians for Common Internal Medicine Cases using Simulated Patients
Shihwa Medical Center（始华医疗中心）；Helpmedoc Inc.（Helpmedoc公司）；Asan Medical Center（峨山医疗中心）；University of Ulsan College of Medicine（蔚山大学医学院）
本研究开发了基于大语言模型的实时复合诊断医疗AI界面，通过非随机临床试验与医生比较，发现其在常见内科病例中诊断准确率更高（80% vs 50-70%）、时间更短（缩短44.6%）、成本更低（降低98.1%），患者满意度相当，显示出辅助初级保健的潜力。
DR.BENCH: Diagnostic Reasoning Benchmark for Clinical Natural Language Processing
University of Wisconsin Madison（威斯康星大学麦迪逊分校）；Loyola University Chicago（芝加哥洛约拉大学）；Boston Children’s Hospital（波士顿儿童医院）；Harvard University（哈佛大学）；Rush University Medical Center（拉什大学医学中心）
PACT: Learning Diverse Diagnostic Strategies via Privileged Synthesis and Branch Consensus
Beihang University（北京航空航天大学）；Baidu（百度）；ByteDance（字节跳动）；Beijing Academy of Blockchain and Edge Computing（北京区块链与边缘计算研究院）；Renmin University of China（中国人民大学）
Leveraging Medical Knowledge Graphs Into Large Language Models for Diagnosis Prediction: Design and Application Study
University of Colorado Anschutz Medical Campus（科罗拉多大学安舒茨医学园区）；University of Wisconsin–Madison（威斯康星大学麦迪逊分校）；University of Aberdeen（阿伯丁大学）；Boston Children's Hospital, Harvard Medical School（哈佛医学院波士顿儿童医院）；Loyola University Chicago（芝加哥洛约拉大学）
Empowering Medical Equipment Sustainability in Low-Resource Settings: An AI-Powered Diagnostic and Support Platform for Biomedical Technicians
Carnegie Mellon University Africa（卡内基梅隆大学非洲分校）；Medical Artificial Intelligence Laboratory（医学人工智能实验室）；University of Pennsylvania（宾夕法尼亚大学）；McGill University（麦吉尔大学）
MedIQA: A Scalable Foundation Model for Prompt-Driven Medical Image Quality Assessment
Faculty of Applied Sciences, Macao Polytechnic University, Macao, China（澳门理工学院）；Department of Engineer Science, University of Oxford, Oxford, UK（牛津大学工程科学系）；Great Bay University, Dongguan, China（东莞大湾大学）；College of Physics and Information Engineering, Fuzhou University, Fuzhou, China（福州大学物理与信息工程学院）；Shanghai Jiao Tong University, Shanghai, China（上海交通大学）；Department of Radiology, Shenzhen People’s Hospital, Shenzhen, China（深圳人民医院放射科）
DiagnosisArena: Benchmarking Diagnostic Reasoning for Large Language Models
Shanghai Jiao Tong University（上海交通大学）；SII；SPIRAL Lab（SPIRAL实验室）；Generative AI Research Lab (GAIR)（生成式AI研究实验室）；Shanghai Chest Hospital（上海胸科医院）；Beijing Anzhen Hospital, Capital Medical University（北京安贞医院，首都医科大学）
