Patient-Facing Generative AI in Telemedicine

This review examines patient-facing generative artificial intelligence in telemedicine. The organizing question is how generated explanations and triage suggestions can be evaluated for comprehension, equity, safety, and escalation. Ten related scholarly sources are synthesized through a decision-centered framework spanning problem definition, mechanism, measurement, evaluation, implementation, and governance. The review does not invent experiments, pooled estimates, or unreported quantitative results. It instead evaluates the strength and transferability of the available evidence, with particular attention to allowing conversational fluency to mask clinical uncertainty and delayed care. The resulting framework links technical or empirical performance to explicit use conditions and identifies tests that should precede wider adoption in teleconsultation and patient communication.

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7. Uddin, J., Feng, C., & Xu, J. (2025). Health Communication on the Internet: Promoting Public Health and Exploring Disparities in the Generative AI Era. Journal of Medical Internet Research, 27, e66032. https://doi.org/10.2196/66032 DOI
8. Vaccari, I., Orani, V., Paglialonga, A., Cambiaso, E., & Mongelli, M. (2021). A Generative Adversarial Network (GAN) Technique for Internet of Medical Things Data. Sensors, 21(11), 3726. https://doi.org/10.3390/s21113726 DOI
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10. Yang, H., Dai, T., Mathioudakis, N., Knight, A. M., Nakayasu, Y., & Wolf, R. M. (2025). Peer perceptions of clinicians using generative AI in medical decision-making. npj Digital Medicine, 8(1), 530. https://doi.org/10.1038/s41746-025-01901-x DOI