Can Large Language Models Safely Address Patient Questions Following Cataract Surgery?
Open accessAbstract
Recent advances in large language models (LLMs) have generated significant interest in their application across various domains including healthcare.However, there is limited data on their safety and performance in real-world scenarios.This study uses data collected using an autonomous telemedicine clinical assistant.The assistant asks symptom-based questions to elicit patient concerns and allows patients to ask questions about their post-operative recovery.We utilise real-world postoperative questions posed to the assistant by a cohort of 120 patients to examine the safety and appropriateness of responses generated by a recent popular LLM by OpenAI, ChatGPT.We demonstrate that LLMs have the potential to helpfully address routine patient queries following routine surgery.However, important limitations around the safety of today's models exist which must be considered.
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Automated evaluations using content filters or classifiers can detect overtly harmful responses, but nuanced medical contexts require human expert reviews.
“Automated evaluations using content filters or classifiers can detect overtly harmful responses, but nuanced medical contexts require human expert reviews.”
From Trustworthy Medical Question Answering: An Evaluation-Centric Survey (2025) · cites this paper as Chowdhury et al., 2023
Section 3.3 (page not provided) says two ophthalmologists assessed the responses for helpfulness, clinical harm, and appropriateness, supporting the role of expert review in this medical context. The paper does not report that automated content filters or classifiers can detect overtly harmful responses, or compare their performance with expert review. (LLM, conf=0.86)
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“Experts ensure responses address medical issues safely and include essential warnings (Chowdhury et al., 2023).”
- Authors
- Chowdhury, M., Lim, E., Higham, A., McKinnon, R., Ventoura, N., He, Y., & De Pennington, N.
- Affiliations
- University of York (GB)
- Royal Berkshire NHS Foundation Trust (GB)
- Venue
- Proceedings of the 5th Clinical Natural Language Processing Workshop
- Published
- 2023
- Pages
- 131-137
- OpenAlex
- W4385570062
- Cited by
- 17
- References
- 10
- Citations by year
- 2026: 1 · 2025: 9 · 2024: 6 · 2023: 1
- FWCI
- 2.85
- Type
- conference paper
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- cc-by
- Locations
- Clinical Natural Language Processing Workshop (ClinicalNLP) (publishedVersion, cc-by)
- Topics
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
- Health Sciences › Medicine › Public Health, Environmental and Occupational Health › Telemedicine and Telehealth Implementation
- Keywords
- large language models, ChatGPT, cataract surgery, postoperative recovery, patient questions, AI safety, health information accuracy
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