Jonathan Herington, Melissa D McCradden, Kathleen Creel, Ronald Boellaard, Elizabeth C Jones, Abhinav K Jha, Arman Rahmim, Peter J H Scott, John J Sunderland, Richard L Wahl, Sven Zuehlsdorff, Babak Saboury
{"title":"Ethical Considerations for Artificial Intelligence in Medical Imaging: Data Collection, Development, and Evaluation.","authors":"Jonathan Herington, Melissa D McCradden, Kathleen Creel, Ronald Boellaard, Elizabeth C Jones, Abhinav K Jha, Arman Rahmim, Peter J H Scott, John J Sunderland, Richard L Wahl, Sven Zuehlsdorff, Babak Saboury","doi":"10.2967/jnumed.123.266080","DOIUrl":null,"url":null,"abstract":"<p><p>The development of artificial intelligence (AI) within nuclear imaging involves several ethically fraught components at different stages of the machine learning pipeline, including during data collection, model training and validation, and clinical use. Drawing on the traditional principles of medical and research ethics, and highlighting the need to ensure health justice, the AI task force of the Society of Nuclear Medicine and Molecular Imaging has identified 4 major ethical risks: privacy of data subjects, data quality and model efficacy, fairness toward marginalized populations, and transparency of clinical performance. We provide preliminary recommendations to developers of AI-driven medical devices for mitigating the impact of these risks on patients and populations.</p>","PeriodicalId":16758,"journal":{"name":"Journal of Nuclear Medicine","volume":" ","pages":"1848-1854"},"PeriodicalIF":9.1000,"publicationDate":"2023-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10690124/pdf/","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Nuclear Medicine","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.2967/jnumed.123.266080","RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING","Score":null,"Total":0}
引用次数: 0
Abstract
The development of artificial intelligence (AI) within nuclear imaging involves several ethically fraught components at different stages of the machine learning pipeline, including during data collection, model training and validation, and clinical use. Drawing on the traditional principles of medical and research ethics, and highlighting the need to ensure health justice, the AI task force of the Society of Nuclear Medicine and Molecular Imaging has identified 4 major ethical risks: privacy of data subjects, data quality and model efficacy, fairness toward marginalized populations, and transparency of clinical performance. We provide preliminary recommendations to developers of AI-driven medical devices for mitigating the impact of these risks on patients and populations.
期刊介绍:
The Journal of Nuclear Medicine (JNM), self-published by the Society of Nuclear Medicine and Molecular Imaging (SNMMI), provides readers worldwide with clinical and basic science investigations, continuing education articles, reviews, employment opportunities, and updates on practice and research. In the 2022 Journal Citation Reports (released in June 2023), JNM ranked sixth in impact among 203 medical journals worldwide in the radiology, nuclear medicine, and medical imaging category.