{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,28]],"date-time":"2026-04-28T16:09:45Z","timestamp":1777392585267,"version":"3.51.4"},"reference-count":32,"publisher":"Association for Computing Machinery (ACM)","issue":"2","license":[{"start":{"date-parts":[[2025,3,13]],"date-time":"2025-03-13T00:00:00Z","timestamp":1741824000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Manage. Inf. Syst."],"published-print":{"date-parts":[[2025,6,30]]},"abstract":"<jats:p>Medical artificial intelligence (AI) is a cross-disciplinary field focused on developing advanced computing and AI technologies to benefit medicine and healthcare. Globally, medical AI has tremendous potential to support the United Nations\u2019 sustainable development goals pertaining to health and well-being. In particular, large language models (LLMs) afford opportunities for positively disrupting medical AI-related research and practice. We present a research framework for LLMs in medical AI. Our framework considers the interplay between health and well-being goals, disease lifecycle stages, and the important emerging role of LLMs in medical AI processes related to various lifecycle stages. As part of our framework, we describe the LLM multiplex\u2014important multimodal, multi-model, multicultural, and multi-responsibility considerations for LLMs in medical AI. We discuss how the five articles in the special issue relate to this framework and are helping us learn about the opportunities and challenges for LLMs in medical AI.<\/jats:p>","DOI":"10.1145\/3711837","type":"journal-article","created":{"date-parts":[[2025,1,9]],"date-time":"2025-01-09T11:40:26Z","timestamp":1736422826000},"page":"1-7","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":4,"title":["LLMs and Their Applications in Medical Artificial Intelligence"],"prefix":"10.1145","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2323-5091","authenticated-orcid":false,"given":"Wenji","family":"Mao","sequence":"first","affiliation":[{"name":"Institute of Automation, Chinese Academy of Sciences, Beijing, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7163-5247","authenticated-orcid":false,"given":"Xipeng","family":"Qiu","sequence":"additional","affiliation":[{"name":"Fudan University, Shanghai China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7698-7794","authenticated-orcid":false,"given":"Ahmed","family":"Abbasi","sequence":"additional","affiliation":[{"name":"Human-centered Analytics Lab, University of Notre Dame, Notre Dame, United States"}]}],"member":"320","published-online":{"date-parts":[[2025,3,13]]},"reference":[{"key":"e_1_3_1_2_2","article-title":"Make \u201cfairness by design\u201d part of machine learning","author":"Abbasi Ahmed","year":"2018","unstructured":"Ahmed Abbasi, Jingjing Li, Gari Clifford, and Herman Taylor. 2018. Make \u201cfairness by design\u201d part of machine learning. Harv. Bus. Rev. (2018). Retrieved August 1, 2018 from https:\/\/hbr.org\/2018\/08\/make-fairness-by-design-part-of-machine-learning","journal-title":"Harv. Bus. Rev."},{"key":"e_1_3_1_3_2","doi-asserted-by":"publisher","DOI":"10.1287\/isre.2024.editorial.v35.n2"},{"key":"e_1_3_1_4_2","article-title":"Towards measuring and modeling culture in llms: A survey","author":"Adilazuarda Muhammad Farid","year":"2024","unstructured":"Muhammad Farid Adilazuarda, Sagnik Mukherjee, Pradhyumna Lavania, Siddhant Singh, Alham Fikri Aji, Jacki O\u2019Neill, Ashutosh Modi, and Monojit Choudhury. 2024. Towards measuring and modeling culture in llms: A survey. arXiv:2403.15412. Retrieved from https:\/\/arxiv.org\/abs\/2403.15412","journal-title":"arXiv:2403.15412"},{"key":"e_1_3_1_5_2","doi-asserted-by":"publisher","unstructured":"Benjamin Ampel Chi-Heng Yang James Hu and Hsinchun Chen. 2024. 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