Shalmali Joshi, Iñigo Urteaga, Wouter A C van Amsterdam, George Hripcsak, Pierre Elias, Benjamin Recht, Noémie Elhadad, James Fackler, Mark P Sendak, Jenna Wiens, Kaivalya Deshpande, Yoav Wald, Madalina Fiterau, Zachary Lipton, Daniel Malinsky, Madhur Nayan, Hongseok Namkoong, Soojin Park, Julia E Vogt, Rajesh Ranganath
{"title":"人工智能干预:改善临床结果依赖于人工智能开发和验证的因果方法。","authors":"Shalmali Joshi, Iñigo Urteaga, Wouter A C van Amsterdam, George Hripcsak, Pierre Elias, Benjamin Recht, Noémie Elhadad, James Fackler, Mark P Sendak, Jenna Wiens, Kaivalya Deshpande, Yoav Wald, Madalina Fiterau, Zachary Lipton, Daniel Malinsky, Madhur Nayan, Hongseok Namkoong, Soojin Park, Julia E Vogt, Rajesh Ranganath","doi":"10.1093/jamia/ocae301","DOIUrl":null,"url":null,"abstract":"<p><p>The primary practice of healthcare artificial intelligence (AI) starts with model development, often using state-of-the-art AI, retrospectively evaluated using metrics lifted from the AI literature like AUROC and DICE score. However, good performance on these metrics may not translate to improved clinical outcomes. Instead, we argue for a better development pipeline constructed by working backward from the end goal of positively impacting clinically relevant outcomes using AI, leading to considerations of causality in model development and validation, and subsequently a better development pipeline. Healthcare AI should be \"actionable,\" and the change in actions induced by AI should improve outcomes. Quantifying the effect of changes in actions on outcomes is causal inference. The development, evaluation, and validation of healthcare AI should therefore account for the causal effect of intervening with the AI on clinically relevant outcomes. Using a causal lens, we make recommendations for key stakeholders at various stages of the healthcare AI pipeline. Our recommendations aim to increase the positive impact of AI on clinical outcomes.</p>","PeriodicalId":50016,"journal":{"name":"Journal of the American Medical Informatics Association","volume":" ","pages":""},"PeriodicalIF":4.7000,"publicationDate":"2025-01-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"AI as an intervention: improving clinical outcomes relies on a causal approach to AI development and validation.\",\"authors\":\"Shalmali Joshi, Iñigo Urteaga, Wouter A C van Amsterdam, George Hripcsak, Pierre Elias, Benjamin Recht, Noémie Elhadad, James Fackler, Mark P Sendak, Jenna Wiens, Kaivalya Deshpande, Yoav Wald, Madalina Fiterau, Zachary Lipton, Daniel Malinsky, Madhur Nayan, Hongseok Namkoong, Soojin Park, Julia E Vogt, Rajesh Ranganath\",\"doi\":\"10.1093/jamia/ocae301\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>The primary practice of healthcare artificial intelligence (AI) starts with model development, often using state-of-the-art AI, retrospectively evaluated using metrics lifted from the AI literature like AUROC and DICE score. However, good performance on these metrics may not translate to improved clinical outcomes. Instead, we argue for a better development pipeline constructed by working backward from the end goal of positively impacting clinically relevant outcomes using AI, leading to considerations of causality in model development and validation, and subsequently a better development pipeline. Healthcare AI should be \\\"actionable,\\\" and the change in actions induced by AI should improve outcomes. Quantifying the effect of changes in actions on outcomes is causal inference. The development, evaluation, and validation of healthcare AI should therefore account for the causal effect of intervening with the AI on clinically relevant outcomes. Using a causal lens, we make recommendations for key stakeholders at various stages of the healthcare AI pipeline. Our recommendations aim to increase the positive impact of AI on clinical outcomes.</p>\",\"PeriodicalId\":50016,\"journal\":{\"name\":\"Journal of the American Medical Informatics Association\",\"volume\":\" \",\"pages\":\"\"},\"PeriodicalIF\":4.7000,\"publicationDate\":\"2025-01-07\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of the American Medical Informatics Association\",\"FirstCategoryId\":\"91\",\"ListUrlMain\":\"https://doi.org/10.1093/jamia/ocae301\",\"RegionNum\":2,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"COMPUTER SCIENCE, INFORMATION SYSTEMS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of the American Medical Informatics Association","FirstCategoryId":"91","ListUrlMain":"https://doi.org/10.1093/jamia/ocae301","RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, INFORMATION SYSTEMS","Score":null,"Total":0}
AI as an intervention: improving clinical outcomes relies on a causal approach to AI development and validation.
The primary practice of healthcare artificial intelligence (AI) starts with model development, often using state-of-the-art AI, retrospectively evaluated using metrics lifted from the AI literature like AUROC and DICE score. However, good performance on these metrics may not translate to improved clinical outcomes. Instead, we argue for a better development pipeline constructed by working backward from the end goal of positively impacting clinically relevant outcomes using AI, leading to considerations of causality in model development and validation, and subsequently a better development pipeline. Healthcare AI should be "actionable," and the change in actions induced by AI should improve outcomes. Quantifying the effect of changes in actions on outcomes is causal inference. The development, evaluation, and validation of healthcare AI should therefore account for the causal effect of intervening with the AI on clinically relevant outcomes. Using a causal lens, we make recommendations for key stakeholders at various stages of the healthcare AI pipeline. Our recommendations aim to increase the positive impact of AI on clinical outcomes.
期刊介绍:
JAMIA is AMIA''s premier peer-reviewed journal for biomedical and health informatics. Covering the full spectrum of activities in the field, JAMIA includes informatics articles in the areas of clinical care, clinical research, translational science, implementation science, imaging, education, consumer health, public health, and policy. JAMIA''s articles describe innovative informatics research and systems that help to advance biomedical science and to promote health. Case reports, perspectives and reviews also help readers stay connected with the most important informatics developments in implementation, policy and education.