Artificial intelligence is already ubiquitous, and is increasingly being used to autonomously make ever more consequential decisions. However, there has been relatively little research into the existing and possible consequences for population health equity. A narrative review was undertaken using a hermeneutic approach to explore current and future uses of narrow AI and automated decision systems (ADS) in medicine and public health, issues that have emerged, and implications for equity. Accounts reveal a tremendous expectation on AI to transform medical and public health practices. Prominent demonstrations of AI capability - particularly in diagnostic decision making, risk prediction, and surveillance - are stimulating rapid adoption, spurred by COVID-19. Automated decisions being made have significant consequences for individual and population health and wellbeing. Meanwhile, it is evident that hazards including bias, incontestability, and privacy erosion have emerged in sensitive domains such as criminal justice where narrow AI and ADS are in common use. Reports of issues arising from their use in health are already appearing. As the use of ADS in health expands, it is probable that these hazards will manifest more widely. Bias, incontestability, and privacy erosion give rise to mechanisms by which existing social, economic and health disparities are perpetuated and amplified. Consequently, there is a significant risk that use of ADS in health will exacerbate existing population health inequities. The industrial scale and rapidity with which ADS can be applied heightens the risk to population health equity. It is incumbent on health practitioners and policy makers therefore to explore the potential implications of using ADS, to ensure the use of artificial intelligence promotes population health and equity.
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）
Framing Analysis of Health-Related Narratives: Conspiracy versus Mainstream Media
Institute of Interactive Systems and Data Science, Graz University of Technology（格拉茨技术大学交互系统与数据科学研究所）；Department of Sociology, University of Graz（格拉茨大学社会学系）
Patterns vs. Patients: Evaluating LLMs against Mental Health Professionals on Personality Disorder Diagnosis through First-Person Narratives
IDEAS Research Institute（IDEAS研究 institute）；Adam Mickiewicz University（亚当·密茨凯维奇大学）；AMU Center for Artificial Intelligence（AMU人工智能中心）；Poznań University of Medical Sciences（波兹南医学科学大学）；Maria Curie-Skłodowska University（玛丽·居里-斯克洛多夫斯卡大学）
Improving Equity in Health Modeling with GPT4-Turbo Generated Synthetic Data: A Comparative Study
University of Maryland, College Park（马里兰大学学院公园校区）；Johns Hopkins University（约翰斯·霍普金斯大学）；Center for Digital Health and Artificial Intelligence（数字健康与人工智能中心）；Robert H. Smith School of Business（罗伯特·H·史密斯商学院）；Carey Business School（凯瑞商学院）
University College London（伦敦大学学院）；NIHR University College London Hospitals Biomedical Research Centre（英国国家健康研究所伦敦大学学院医院生物医学研究中心）；McGill University（麦吉尔大学）
Evaluation of A National Digitally-Enabled Health Promotion Campaign for Mental Health Awareness using Social Media Platforms Tik Tok, Facebook, Instagram, and YouTube
MOH Office for Healthcare Transformation, Singapore（新加坡卫生部医疗转型办公室）；Yong Loo Lin School of Medicine, National University of Singapore, Singapore（新加坡国立大学李光前医学院）；Ophthalmology Academic Clinical Program (ACP), Duke-NUS Medical School, Singapore（杜克-新加坡国立大学医学院眼科学术临床项目）；School of Business, National University of Singapore, Singapore（新加坡国立大学商学院）；NUS Saw Swee Hock School of Public Health (NUS-SSHSPH), Singapore, Singapore（新加坡国立大学许宝騄学校公共健康）
