{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,3]],"date-time":"2026-08-03T15:01:48Z","timestamp":1785769308762,"version":"3.56.0"},"reference-count":149,"publisher":"Association for Computing Machinery (ACM)","issue":"2","funder":[{"name":"Deputyship for Research and Innovation, \u201cMinistry of Education\u201d in Saudi Arabia","award":["IFKSU-HCRA-13-2"],"award-info":[{"award-number":["IFKSU-HCRA-13-2"]}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Multimedia Comput. Commun. Appl."],"published-print":{"date-parts":[[2026,2,28]]},"abstract":"<jats:p>Large Language Models (LLMs) have enormous potential in healthcare, but we also need to be careful and implement strong safety measures. These advanced computer systems can understand complex medical questions written in everyday language and often provide accurate medical information. This article explores how the field has evolved, from the first language models to the LLMs we have now, specifically designed or adjusted for use in the medical world. We explore how LLMs can make important language-related tasks in healthcare more efficient and effective, such as identifying medical terms, understanding how different pieces of information are connected, drawing conclusions from written text, sorting documents, and answering medical queries. We also shed light on new and exciting ways LLMs can use more than just text, like combining written information with images or other types of data. We compare some of the best recent LLMs and discuss how they can be used in everyday medical situations. We also measure how well these models perform in the biomedical field, explaining what each measurement tells us and where it falls short. Last, by discussing the biggest hurdles LLMs face in healthcare\u2014things like generating incorrect information (hallucinations), unfair biases, protecting patient privacy, making sure the models are dependable, and fitting these new tools into how doctors and nurses already work. We also provide a roadmap for future research. We aim to present a clear and fair picture of what LLMs can offer healthcare right now and what is necessary to use them safely and dependably. This includes explaining how researchers are working to make these models easier for doctors and patients to understand. We followed the PRISMA research method to guide our review of the field, from early language models to current LLMs adapted for clinical use.<\/jats:p>","DOI":"10.1145\/3786334","type":"journal-article","created":{"date-parts":[[2025,12,23]],"date-time":"2025-12-23T13:03:17Z","timestamp":1766494997000},"page":"1-32","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":3,"title":["Advances, Evaluation, and Explainability of Large Language Models in Healthcare: A Systematic Review"],"prefix":"10.1145","volume":"22","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4718-0155","authenticated-orcid":false,"given":"Syed Umar","family":"Amin","sequence":"first","affiliation":[{"name":"College of Computer and Information Sciences, Prince Sultan University, Riyadh, Saudi Arabia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8972-8094","authenticated-orcid":false,"given":"Mohsen","family":"Guizani","sequence":"additional","affiliation":[{"name":"Department of Machine Learning, Mohamed Bin Zayed University of Artificial Intelligence (MBZUAI), Abu Dhabi, United Arab Emirates"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5906-9422","authenticated-orcid":false,"given":"M. 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