ORCID
- Krithika Anil: 0000-0002-8027-1665
- Inocencio Maramba: 0000-0002-5464-6021
Document Type
Article
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 review maps these considerations for patients accessing information on long-term conditions in non-clinical settings. Following PRISMA guidelines, we searched databases (MEDLINE, CINAHL, EMBASE, and Web of Science) between April and May 2025. The review synthesises 24 cross-sectional studies regarding patient-facing LLMs, excluding clinician-support tools. Results indicate that LLMs perform well on general topics but struggle with specialised 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.
DOI Link
Publication Date
2026-10-14
Publication Title
Healthcare Technology Letters
Volume
13
Issue
1
Acceptance Date
2026-09-29
Deposit Date
2026-10-19
Funding
N-CODE Network+ funded by EPSRC/MRC via grant EP/W035030/1
Keywords
generative artificial intelligence, neuroethics, ethical issues, legal, patient, health information, health technology, technology translation
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.
Recommended Citation
Anil, K., Maramba, I., Cook, D., Cyrus, D., Fakharuzi, Z., Hossein Namdar, A., West, T., & Shirgill, S. (2026) 'Evidence Synthesis of the Ethical and Legal Challenges of Generative Artificial Intelligence in the Provision of Patient Health Information', Healthcare Technology Letters, 13(1). Available at: 10.1049/htl2.70102
