机器学习（ML），尤其是深度学习（DL）应用正在进入我们生活的所有主要领域。由于对患者生命具有重大影响，可信人工智能的发展在医学中尤为重要。虽然可信性涉及伦理、技术和隐私要求等多个方面，但我们重点关注DL中数据质量（训练/测试）的重要性。由于数据质量决定了ML产品的行为，评估数据质量将在医学人工智能产品的监管审批中发挥关键作用。我们按照PRISMA指南，使用PubMed和ACM Digital Library数据库进行了一项系统综述。我们识别出2362项研究，其中62条记录符合我们的资格标准。从这些文献中，我们综合了关于数据质量框架的现有知识，并将其与ML在医学中应用的视角相结合。因此，我们提出了METRIC框架，这是一个专门针对医学训练数据的数据质量框架，包含15个意识维度，医学ML应用的开发者应沿着这些维度调查数据集。这些知识有助于减少作为不公平主要来源的偏见，提高鲁棒性，促进可解释性，从而为医学中可信人工智能奠定基础。将这种对医学数据集的系统评估纳入监管审批流程，有可能加速ML产品的审批，并为新标准奠定基础。
英文摘要：
The adoption of machine learning (ML) and, more specifically, deep learning (DL) applications into all major areas of our lives is underway. The development of trustworthy AI is especially important in medicine due to the large implications for patients' lives. While trustworthiness concerns various aspects including ethical, technical and privacy requirements, we focus on the importance of data quality (training/test) in DL. Since data quality dictates the behaviour of ML products, evaluating data quality will play a key part in the regulatory approval of medical AI products. We perform a systematic review following PRISMA guidelines using the databases PubMed and ACM Digital Library. We identify 2362 studies, out of which 62 records fulfil our eligibility criteria. From this literature, we synthesise the existing knowledge on data quality frameworks and combine it with the perspective of ML applications in medicine. As a result, we propose the METRIC-framework, a specialised data quality framework for medical training data comprising 15 awareness dimensions, along which developers of medical ML applications should investigate a dataset. This knowledge helps to reduce biases as a major source of unfairness, increase robustness, facilitate interpretability and thus lays the foundation for trustworthy AI in medicine. Incorporating such systematic assessment of medical datasets into regulatory approval processes has the potential to accelerate the approval of ML products and builds the basis for new standards.
Assessing Foundation Models' Transferability to Physiological Signals in Precision Medicine
Mountain Biometrics Inc（芒廷生物ometrics公司）；Huntsman Mental Health Institute（亨茨曼心理健康研究所）；University of Utah（犹他大学）；Dartmouth College（达特茅斯学院）；Intermountain Health（intermountain医疗）；University of Maryland（马里兰大学）
Emerging Challenges in Personalized Medicine: Assessing Demographic Effects on Biomedical Question Answering Systems
University of Colorado Boulder（科罗拉多大学博尔德分校）；University of Colorado Denver（科罗拉多大学丹佛分校）；Memorial Healthcare System（纪念医疗系统）；Johannes Gutenberg University Mainz（美因茨约翰内斯·古腾堡大学）
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…
Sahlgrenska Academy, University of Gothenburg（哥德堡大学萨尔格伦斯卡学院）；Sahlgrenska University Hospital（萨尔格伦斯卡大学医院）；University of Gothenburg（哥德堡大学）
Machine learning is used in medicine to support physicians in examination, diagnosis, and predicting outcomes. One of the most dynamic area is the usage of patient generated health data from intensive care units. The goal of this paper is to demonstrate how we advance cross-patient ML model development by combining the patient's demographics data with their physiological data. We used a population of patients undergoing Carotid Enderarterectomy (CEA), where we studied differences in model performance and explainability when trained for all patients and one patient at a time. The results show t…
The increasing impact of algorithmic decisions on people's lives compels us to scrutinize their fairness and, in particular, the disparate impacts that ostensibly-color-blind algorithms can have on different groups. Examples include credit decisioning, hiring, advertising, criminal justice, personalized medicine, and targeted policymaking, where in some cases legislative or regulatory frameworks for fairness exist and define specific protected classes. In this paper we study a fundamental challenge to assessing disparate impacts in practice: protected class membership is often not observed in …
Leveraging Group Relative Policy Optimization to Advance Large Language Models in Traditional Chinese Medicine
University of Missouri（密苏里大学）；Christopher S. Bond Life Sciences Center, University of Missouri（密苏里大学克里斯托弗·S·邦德生命科学中心）；Community Health Service Center Shanghai Pudong New Area（上海市浦东新区社区卫生服务中心）；Shanghai University of Traditional Chinese Medicine（上海中医药大学）
To facilitate the widespread acceptance of AI systems guiding decision-making in real-world applications, it is key that solutions comprise trustworthy, integrated human-AI systems. Not only in safety-critical applications such as autonomous driving or medicine, but also in dynamic open world systems in industry and government it is crucial for predictive models to be uncertainty-aware and yield trustworthy predictions. Another key requirement for deployment of AI at enterprise scale is to realize the importance of integrating human-centered design into AI systems such that humans are able to …
Machine learning in medicine leverages the wealth of healthcare data to extract knowledge, facilitate clinical decision-making, and ultimately improve care delivery. However, ML models trained on datasets that lack demographic diversity could yield suboptimal performance when applied to the underrepresented populations (e.g. ethnic minorities, lower social-economic status), thus perpetuating health disparity. In this study, we evaluated four classifiers built to predict Hyperchloremia - a condition that often results from aggressive fluids administration in the ICU population - and compared th…
Charles Sturt University（查尔斯特大学）；University of Maryland（马里兰大学）
The information ecosystem today is overwhelmed by an unprecedented quantity of data on versatile topics are with varied quality. However, the quality of information disseminated in the field of medicine has been questioned as the negative health consequences of health misinformation can be life-threatening. There is currently no generic automated tool for evaluating the quality of online health information spanned over a broad range. To address this gap, in this paper, we applied a data mining approach to automatically assess the quality of online health articles based on 10 quality criteria. …
Assessing the Feasibility of Early Cancer Detection Using Routine Laboratory Data: An Evaluation of Machine Learning Approaches on an Imbalanced Dataset
Large Language Models Illuminate a Progressive Pathway to Artificial Healthcare Assistant: A Review
Peking University（北京大学）；Peking University Cancer Hospital and Institute（北京大学肿瘤医院暨北京市肿瘤防治研究所）；National Engineering Laboratory for Big Data Analysis and Applications, Peking University（北京大学大数据分析与应用技术国家工程实验室）；Peking University Changsha Institute for Computing and Digital Economy（北京大学长沙计算与数字经济研究院）
Metric Hub: A metric library and practical selection workflow for use-case-driven data quality assessment in medical AI
Physikalisch-Technische Bundesanstalt（联邦物理技术研究院）；Fraunhofer Institute for Integrated Circuits IIS（弗劳恩霍夫集成电路研究所）；Friedrich-Alexander-University Erlangen Nuremberg（弗里德里希-亚历山大-埃尔兰根-纽伦堡大学）；Univ Rennes（雷恩大学）；Inserm（法国国家健康与医学研究院）；Politecnico di Milano（米兰理工大学）；Fondazione IRCCS Istituto Nazionale dei Tumori di Milano（米兰国家肿瘤研究所IRCCS基金会）；Masaryk University（马萨里克大学）；Berlin Institute of Health (BIH)（柏林健康研究所）；RISE Research Institutes of Sweden（瑞典RISE研究院）；CETIC Centre d’excellence en technologies de l’information et de la communication（CETIC信息与通信技术卓越中心）；Fraunhofer Institute for Telecommunications Heinrich-Hertz-Institute HHI（弗劳恩霍夫电信研究所海因里希·赫兹研究所）；Multitel Research Center（Multitel研究中心）；Technical University Berlin（柏林工业大学）；Einstein Centre for Digital Future（爱因斯坦数字未来中心）
Algorithmic Accountability in Small Data: Sample-Size-Induced Bias Within Classification Metrics
School of Electrical Engineering & Computer Science, Washington State University（电气工程与计算机科学学院，华盛顿州立大学）；Department of Mathematics and Statistics, Washington State University（数学与统计学系，华盛顿州立大学）
Beihang University（北京航空航天大学）；University of California, Davis（加利福尼亚大学戴维斯分校）
Machine learning for data-driven diagnosis has been actively studied in medicine to provide better healthcare. Supporting analysis of a patient cohort similar to a patient under treatment is a key task for clinicians to make decisions with high confidence. However, such analysis is not straightforward due to the characteristics of medical records: high dimensionality, irregularity in time, and sparsity. To address this challenge, we introduce a method for similarity calculation of medical records. Our method employs event and sequence embeddings. While we use an autoencoder for the event embed…
S-Chain: Structured Visual Chain-of-Thought For Medicine
University of Toronto（多伦多大学）；Knovel Engineering Lab（Knovel工程实验室）；German Research Centre for Artificial Intelligence（德国人工智能研究中心）；University of Stuttgart（斯图加特大学）；Chonnam National University（全南大学）；Singapore University of Technology and Design（新加坡科技设计大学）；Bucknell University（巴克内尔大学）；Concordia University（康考迪亚大学）；Korea University（高丽大学）；Justus Liebig University Giessen（吉森大学）；Japan Advanced Institute of Science and Technology（日本先进科学技术大学）；Hue University（顺化大学）；College of William & Mary（威廉与玛丽学院）；Deakin University（迪肯大学）；National University of Singapore（新加坡国立大学）；University of Texas at Austin（德克萨斯大学奥斯汀分校）；University of California, Berkeley（加利福尼亚大学伯克利分校）；New Jersey Institute of Technology（新泽西理工学院）；University of California San Diego（加利福尼亚大学圣迭戈分校）；MBZUAI（穆罕默德·本·扎耶德人工智能大学）；Oldenburg University（奥尔登堡大学）；Stanford University（斯坦福大学）；Max Planck Research School for Intelligent Systems（马克斯·普朗克智能系统研究学院）；Auburn University（奥本大学）
Enhancing Mutual Trustworthiness in Federated Learning for Data-Rich Smart Cities
Mohammad Bin Zayed University of Artificial Intelligence（穆罕默德·本·扎耶德人工智能大学）；Polytechnique Montréal（蒙特利尔理工学院）；Lebanese American University（黎巴嫩美国大学）；Khalifa University（哈利法大学）；New York University（纽约大学）；Technology Innovation Institute (TII)（技术创新研究所）
