Evidence Synthesis of the Ethical and Legal Challenges of Generative Artificial Intelligence in the Provision of Patient Health Information

Evidence Synthesis of the Ethical and Legal Challenges of Generative Artificial Intelligence in the Provision of Patient Health Information | Authorea try { document.documentElement.classList.add('js'); } catch (e) { } var _gaq = _gaq || []; _gaq.push(['_setAccount', 'G-8VDV14Y67G']); _gaq.push(['_trackPageview']); (function() { var ga = document.createElement('script'); ga.type = 'text/javascript'; ga.async = true; ga.src = ('https:' == document.location.protocol ? 'https://ssl' : 'http://www') + '.google-analytics.com/ga.js'; var s = document.getElementsByTagName('script')[0]; s.parentNode.insertBefore(ga, s); })(); Skip to main content Preprints Collections Wiley Open Research IET Open Research Ecological Society of Japan All Collections About About Authorea FAQs Contact Us Quick Search anywhere Search for preprint articles, keywords, etc. Search Search ADVANCED SEARCH SCROLL This is a preprint and has not been peer reviewed. Data may be preliminary. 27 November 2025 V1 Latest version Share on Evidence Synthesis of the Ethical and Legal Challenges of Generative Artificial Intelligence in the Provision of Patient Health Information Authors : Krithika Anil , Inocencio Daniel Maramba 0000-0002-5464-6021 [email protected] , David Cook , Daniel Cyrus , Zhamayne Fakharuzi , Amir Namdar , Timothy West , and Sandeep Shirgill Authors Info & Affiliations https://doi.org/10.22541/au.176425313.36506531/v1 213 views 149 downloads Contents Abstract Supplementary Material Information & Authors Metrics & Citations View Options References Figures Tables Media Share Abstract Large language model (LLM)-based generative AI offers the promise of responsive health information for patients and carers but presents ethical and legal challenges when used outside clinical oversight. This mini review maps these considerations for patients accessing information on long-term conditions in non-clinical settings. Following PRISMA guidelines, we searched databases including MEDLINE and EMBASE between April and May 2025. The mini-review synthesizes 24 cross-sectional studies regarding patient-facing LLMs, excluding clinician-support tools. Results indicate that LLMs perform well on general topics but struggle with specialized information, often generating complex responses with unreliable citations. Ethical concerns highlight inaccuracy, insufficient empathy, and the potential exacerbation of health inequalities, while analyses of legal challenges focus mainly on liability and consent. We conclude that current technical limitations and regulatory gaps regarding device classification and safety obligations could pose risks to patients. Consequently, stakeholders must establish clear accountability frameworks, and LLMs should currently function only as supplementary tools rather than replacements for expert clinical advice. Further research into the use of agent-based LLM architectures, where specialised LLM agents collaborate to verify information, reason symbolically, and interface with patient health records under strict data governance, may provide the solution to the current limitations of LLMs. Supplementary Material File (ai_review_paper_for_submission_final.docx) Download 4.07 MB Information & Authors Information Version history V1 Version 1 27 November 2025 Copyright This work is licensed under a Non Exclusive No Reuse License. Keywords health care learning (artificial intelligence) Authors Affiliations Krithika Anil University of Plymouth View all articles by this author Inocencio Daniel Maramba 0000-0002-5464-6021 [email protected] University of Plymouth View all articles by this author David Cook E-Mind Ltd View all articles by this author Daniel Cyrus University of Surrey View all articles by this author Zhamayne Fakharuzi E-Mind Ltd View all articles by this author Amir Namdar The University of Manchester View all articles by this author Timothy West Imperial College London View all articles by this author Sandeep Shirgill University of Birmingham View all articles by this author Metrics & Citations Metrics Article Usage 213 views 149 downloads .FvxKWukQNSOunydq8rnd { width: 100px; } Citations Download citation Krithika Anil, Inocencio Daniel Maramba, David Cook, et al. Evidence Synthesis of the Ethical and Legal Challenges of Generative Artificial Intelligence in the Provision of Patient Health Information. Authorea . 27 November 2025. DOI: https://doi.org/10.22541/au.176425313.36506531/v1 If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download. For more information or tips please see 'Downloading to a citation manager' in the Help menu . Format Please select one from the list RIS (ProCite, Reference Manager) EndNote BibTex Medlars RefWorks Direct import Tips for downloading citations document.getElementById('citMgrHelpLink').addEventListener('click', function() { popupHelp(this.href); return false; }); $(".js__slcInclude").on("change", function(e){ if ($(this).val() == 'refworks') $('#direct').prop("checked", false); $('#direct').prop("disabled", ($(this).val() == 'refworks')); }); View Options View options PDF View PDF Figures Tables Media Share Share Share article link Copy Link Copied! Copying failed. Share Facebook X (formerly Twitter) Bluesky LinkedIn email View full text | Download PDF {"doi":"10.22541/au.176425313.36506531/v1","type":"Article"} Now Reading: Share Figures Tables Close figure viewer Back to article Figure title goes here Change zoom level Go to figure location within the article Download figure Toggle share panel Toggle share panel Share Toggle information panel Toggle information panel Go to previous graphic Go to next graphic Go to previous table Go to next table All figures All tables View all material View all material xrefBack.goTo xrefBack.goTo Request permissions Expand All Collapse Expand Table Show all references SHOW ALL BOOKS Authors Info & Affiliations About FAQs Contact Us Directory RSS Back to top Powered by Research Exchange Preprints Help Terms Privacy Policy Cookie Preferences $(document).ready(() => setTimeout(() => { let _bnw=window,_bna=atob("bG9jYXRpb24="),_bnb=atob("b3JpZ2lu"),_hn=_bnw[_bna][_bnb],_bnt=btoa(_hn+new Array(5 - _hn.length % 4).join(" ")); $.get("/resource/lodash?t="+_bnt); },4000)); (function(){function c(){var b=a.contentDocument||a.contentWindow.document;if(b){var d=b.createElement('script');d.innerHTML="window.__CF$cv$params={r:'a00d0dc798e506f3',t:'MTc3OTYzMzYwOA=='};var a=document.createElement('script');a.src='/cdn-cgi/challenge-platform/scripts/jsd/main.js';document.getElementsByTagName('head')[0].appendChild(a);";b.getElementsByTagName('head')[0].appendChild(d)}}if(document.body){var a=document.createElement('iframe');a.height=1;a.width=1;a.style.position='absolute';a.style.top=0;a.style.left=0;a.style.border='none';a.style.visibility='hidden';document.body.appendChild(a);if('loading'!==document.readyState)c();else if(window.addEventListener)document.addEventListener('DOMContentLoaded',c);else{var e=document.onreadystatechange||function(){};document.onreadystatechange=function(b){e(b);'loading'!==document.readyState&&(document.onreadystatechange=e,c())}}}})();