在现代医疗保健系统中，快速增长的成本/复杂性，越来越多的治疗方案，以及爆炸式的信息流，往往不能有效地到达前线，阻碍了随着时间的推移选择最佳治疗决策的能力。本文的目标是开发一个通用（非疾病特异性）计算/人工智能（AI）框架来应对这些挑战。这提供了两个潜在的功能：1）用于探索各种医疗保健政策、支付方法等的模拟环境，2）临床人工智能的基础--一种可以像医生一样思考的人工智能。这种方法结合了马尔可夫决策过程和动态决策网络，从临床数据中学习，并通过模拟替代顺序决策路径来制定复杂的计划，同时捕捉医疗系统中各种组件之间有时相互冲突，有时相互协同的相互作用。它可以在部分可观察的环境中（在缺少观察或数据的情况下）通过维护关于患者健康状况的信念状态来操作，并作为计划和重新计划的在线代理。使用来自电子健康记录的真实患者数据对该框架进行了评估。这样的AI框架很容易超越当前的常规治疗（TAU）病例率/医疗保健服务收费模型（单位变化成本：$189 vs. $ 497），同时获得30-35%的患者结局增加。调整某些模型参数进一步增强了这一优势，以大约一半的成本获得大约50%的改进。在精心设计和问题制定的情况下，人工智能仿真框架即使在复杂和不确定的环境中也可以近似最优决策。未来的工作进行了描述，概述了潜在的线的研究和集成的机器学习算法的个性化医疗。
英文摘要：
In the modern healthcare system, rapidly expanding costs/complexity, the growing myriad of treatment options, and exploding information streams that often do not effectively reach the front lines hinder the ability to choose optimal treatment decisions over time. The goal in this paper is to develop a general purpose (non-disease-specific) computational/artificial intelligence (AI) framework to address these challenges. This serves two potential functions: 1) a simulation environment for exploring various healthcare policies, payment methodologies, etc., and 2) the basis for clinical artificial intelligence - an AI that can think like a doctor. This approach combines Markov decision processes and dynamic decision networks to learn from clinical data and develop complex plans via simulation of alternative sequential decision paths while capturing the sometimes conflicting, sometimes synergistic interactions of various components in the healthcare system. It can operate in partially observable environments (in the case of missing observations or data) by maintaining belief states about patient health status and functions as an online agent that plans and re-plans. This framework was evaluated using real patient data from an electronic health record. Such an AI framework easily outperforms the current treatment-as-usual (TAU) case-rate/fee-for-service models of healthcare (Cost per Unit Change: $189 vs. $497) while obtaining a 30-35% increase in patient outcomes. Tweaking certain model parameters further enhances this advantage, obtaining roughly 50% more improvement for roughly half the costs. Given careful design and problem formulation, an AI simulation framework can approximate optimal decisions even in complex and uncertain environments. Future work is described that outlines potential lines of research and integration of machine learning algorithms for personalized medicine.
Generating survival times using Cox proportional hazards models with cyclic time-varying covariates, with application to a multiple-dose monoclonal antibody clinical trial
Artificial Intelligence for Dementia Research Methods Optimization
Ulster University（阿尔斯特大学）；University Hospitals Bristol and Weston NHS Foundation Trust（布里斯托尔大学医院与韦斯顿国民健康服务体系基金会信托）；University of Bristol（布里斯托尔大学）；Université de Montréal（蒙特利尔大学）；University of Aberdeen（阿伯丁大学）；University of Nottingham（诺丁汉大学）；UK Dementia Research Institute（英国痴呆症研究所）；Imperial College London（帝国理工学院）；University College London（伦敦大学学院）；University of Verona（维罗纳大学）；University of Sheffield（谢菲尔德大学）
A new direction to promote the implementation of artificial intelligence in natural clinical settings
Institute of Computing Technology, Chinese Academy of Sciences（中国科学院计算技术研究所）；University of Chinese Academy of Sciences（中国科学院大学）；Capital Medical University（首都医科大学）；Shenzhen University（深圳大学）
Clinical acceptance of software based on artificial intelligence technologies (radiology)
MOSCOW HEALTH CARE DEPARTMENT RESEARCH AND PRACTICAL CLINICAL CENTER FOR DIAGNOSTICS AND TELEMEDICINE TECHNOLOGIES（莫斯科卫生部诊断与远程医疗技术研究与实践临床中心）；MOSCOW HEALTH CARE DEPARTMENT BEST PRACTICES IN MEDICAL IMAGING（莫斯科卫生部医学影像最佳实践中心）
Towards a New Science of a Clinical Data Intelligence
Siemens AG（西门子股份公司）；University Hospital Erlangen（埃尔朗根大学医院）；Friedrich-Alexander-University Erlangen-Nuremberg（弗里德里希-亚历山大大学埃尔朗根-纽伦堡分校）；Charité - Universitätsmedizin Berlin（柏林夏里特医学院）；Averbis GmbH（Averbis有限公司）；DFKI GmbH（德国人工智能研究中心有限公司）；Fraunhofer Institute for Integrated Circuits（弗劳恩霍夫集成电路研究所）；Institut für Frauengesundheit GmbH（女性健康研究所有限公司）；Ludwig Maximilian University of Munich（慕尼黑路德维希-马克西米利安大学）
Artificial Intelligence for Digital and Computational Pathology
Harvard Medical School（哈佛医学院）；Massachusetts General Hospital（麻省总医院）；Broad Institute of Harvard and MIT（哈佛大学与麻省理工学院博德研究所）；Dana-Farber Cancer Institute（丹娜-法伯癌症研究所）；Massachusetts Institute of Technology（麻省理工学院）
Clinical Semantic Intelligence (CSI): Emulating the Cognitive Framework of the Expert Clinician for Comprehensive Oral Disease Diagnosis
Gilan University of Medical Sciences（加兹万大学医学科学学院）；University of Göttingen（哥廷根大学）；University of Tehran（德黑兰大学）；Hamadan University of Technology（哈马丹技术大学）；Islamic Azad University（伊斯兰 Azad 大学）
Simulating Clinical AI Assistance using Multimodal LLMs: A Case Study in Diabetic Retinopathy
Dana-Farber Cancer Institute & Tufts University School of Medicine（达纳-法伯癌症研究所及塔夫茨大学医学院）；Dana-Farber Cancer Institute Brigham and Women’s Hospital & Harvard Medical School（达纳-法伯癌症研究所布里特妇女医院及哈佛医学院）
Artificial Intelligence in Healthcare: Lost In Translation?
QUEST-Center for Transforming Biomedical Research, Berlin Institute of Health（柏林卫生研究所QUEST转化生物医学研究中心）；Charité Universitätsmedizin Berlin（柏林夏里特医学院）；CLAIM - Charité Lab for AI in Medicine（夏里特医学人工智能实验室（CLAIM））；School of Computing and Digital Technology, Faculty of Computing, Engineering and the Built Environment, Birmingham City University（伯明翰城市大学计算、工程与建筑环境学院计算与数字技术系）
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
Simulating clinical interventions with a generative multimodal model of human physiology
Department of Computer Science and Applied Mathematics, Weizmann Institute of Science（魏茨曼科学研究所计算机科学与应用数学系）；Department of Molecular Cell Biology, Weizmann Institute of Science（魏茨曼科学研究所分子细胞生物学系）；NVIDIA；Novo Nordisk Foundation Center for Basic Metabolic Research, University of Copenhagen（诺沃维克基金会基础代谢研究中心，哥本哈根大学）；Faculty of Medical and Health Sciences, Tel Aviv University（特拉维夫大学医学与健康科学学院）；The Jesse Z and Sara Lea Shafer Institute for Endocrinology and Diabetes, National Center for Childhood Diabetes, Schneider Children’s Medical Center of Israel（杰西Z和索菲亚·李·沙弗内分泌学与糖尿病研究所，以色列儿童糖尿病国家中心，施耐德儿童医学中心）；Mohamed bin Zayed University of Artificial Intelligence（穆罕默德·本·扎耶德人工智能大学）
Artificial Intelligence in Pediatric Echocardiography: Exploring Challenges, Opportunities, and Clinical Applications with Explainable AI and Federated Learning
Peter L. Reichertz Institute for Medical Informatics of TU Braunschweig and Hannover Medical School（布伦瑞克工业大学与汉诺威医学院彼得·L·赖歇茨医学信息学研究所）；Hannover Medical School（汉诺威医学院）
Recommending Clinical Trials for Online Patient Cases using Artificial Intelligence
National Library of Medicine, National Institutes of Health（美国国立卫生研究院国家医学图书馆）；National Cancer Institute, National Institutes of Health（美国国立卫生研究院国家癌症研究所）
Generative Artificial Intelligence in Medical Imaging: Foundations, Progress, and Clinical Translation
Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences（中国科学院深圳先进技术研究院）；University of Chinese Academy of Sciences（中国科学院大学）；Macao Polytechnic University（澳门理工大学）
Assessing clinical utility of Machine Learning and Artificial Intelligence approaches to analyze speech recordings in Multiple Sclerosis: A Pilot Study
Charles University（查理大学）；Czech Technical University in Prague（布拉格捷克理工大学）；General University Hospital（综合大学医院）；Brigham and Women’s Hospital（布莱根妇女医院）；Harvard Medical School（哈佛医学院）；Johns Hopkins University（约翰斯·霍普金斯大学）
AI Thrust, Information Hub, HKUST(GZ)（香港科技大学（广州）人工智能 thrust 与信息中心）；College of Computer Science and Technology, Zhejiang University（浙江大学计算机科学与技术学院）；School of Computer Science and Engineering, South China University of Technology（华南理工大学计算机科学与工程学院）；School of Medicine, Stanford University（斯坦福大学医学院）；Computer Science Department, UIUC（伊利诺伊大学厄巴纳-香槟分校计算机科学系）；Medical Big Data Center, Guangdong Provincial People’s Hospital (Guangdong Academy of Medical Sciences), Southern Medical University（广东省人民医院（广东省医学科学院）医学大数据中心，南方医科大学）；Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, College of Pharmaceutical Sciences, Zhejiang University（浙江大学人工智能医学创新研究院，浙江大学药学院）；The Second Affiliated Hospital, Zhejiang University School of Medicine（浙江大学医学院附属第二医院）；GE HealthCare（通用电气医疗）；Shanghai Artificial Intelligence Laboratory（上海人工智能实验室）；IQVIA；Computer Science Department, Stanford University（斯坦福大学计算机科学系）；Informatics, Harvard Medical School, Harvard University（哈佛医学院，哈佛大学informatics部门）；State Key Laboratory for Novel Software Technology at Nanjing University, School of Computer Science, Nanjing University（南京大学新型软件技术国家重点实验室，南京大学计算机科学学院）
Artificial Intelligence, speech and language processing approaches to monitoring Alzheimer's Disease: a systematic review
Usher Institute, Edinburgh Medical School, The University of Edinburgh（爱丁堡大学爱丁堡医学院厄舍研究所）；Centre for Dementia Prevention, The University of Edinburgh（爱丁堡大学痴呆预防中心）
语言是阿尔茨海默病临床信息的宝贵来源，因为它与神经变性同时下降。因此，语音和语言数据已被广泛研究与其诊断。本文总结了当前关于使用人工智能、语音和语言处理来预测阿尔茨海默病背景下认知能力下降的研究结果，详细介绍了当前的研究程序，强调了其局限性，并提出了解决这些问题的策略。我们对2000年至2019年期间在PROSPERO（参考CRD 42018116606）中注册的原始研究进行了系统性综述。跨学科检索涵盖了工程学（ACM和IEEE）、心理学（PsycINFO）、医学（PubMed和Embase）和Web of Science的六个数据库。相关论文的参考书目筛选至2019年12月。根据合格性标准，从3，654个检索结果中选择了51篇文章。四个表格总结了他们的研究结果：研究细节（目的、人群、干预措施、比较、方法和结局）、数据细节（规模、类型、模式、注释、平衡、可用性和研究语言）、方法（预处理、特征生成、机器学习、评价和结果）和临床适用性（研究意义、临床潜力、偏倚风险和优势/局限性）。虽然几乎所有51项研究都报告了有希望的结果，但很少有在临床研究或实践中实施。我们的结论是，该领域的主要局限性是标准化差，结果的可比性有限，以及研究目的和临床应用之间的脱节程度。缩小这些差距的努力应该支持未来研究转化为临床实践。
Artificial Intelligence-Based Methods for Fusion of Electronic Health Records and Imaging Data
College of Science and Engineering, Hamad Bin Khalifa University（哈马德·本·哈利法大学科学与工程学院）；College of Health and Life Sciences, Hamad Bin Khalifa University（哈马德·本·哈利法大学健康与生命科学学院）
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）
Artificial intelligence to improve clinical coding practice in Scandinavia: a crossover randomized controlled trial
Norwegian Centre for E-health Research（挪威电子健康研究中心）；UiT The Arctic University of Norway（挪威北极大学）；University Hospital of North Norway（北挪威大学医院）；The Norwegian Centre for Clinical Artificial Intelligence（挪威临床人工智能中心）
A Design Framework for operationalizing Trustworthy Artificial Intelligence in Healthcare: Requirements, Tradeoffs and Challenges for its Clinical Adoption
organization= Faculty of Medicine；Health Technology, Tampere University , city= Tampere , postcode= 33100 , country= Finland；organization= TECNALIA, Basque Research \& Technology Alliance (BRTA) , city= Derio , postcode= 48160 , country= Spain；organization= Department of Mathematics, University of the Basque Country (UPV/EHU) , city= Leioa , postcode= 48940 , country= Spain；organization= Tampere Heart Hospital, Tampere University Hospital , city= Tampere , postcode= 33520 , country= Finland
Explainable Artificial Intelligence Methods in Combating Pandemics: A Systematic Review
Wallace H. Coulter School of Biomedical Engineering, Georgia Institute of Technology（佐治亚理工学院华莱士·H·库尔特生物医学工程学院）；Emory University（埃默里大学）；Department of Electrical and Computer Engineering, Georgia Institute of Technology（佐治亚理工学院电气与计算机工程系）；School of Biology, Georgia Institute of Technology（佐治亚理工学院生物学院）；Department of Nuclear and Radiological Engineering, Georgia Institute of Technology（佐治亚理工学院核与放射工程系）
