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Large language models for post-discharge follow-up in erectile dysfunction care: a narrative review

   
@article{TAU154391,
	author = {Yiman Zhang and Bodong Lv and Runnan Xu and Chenghao Shi},
	title = {Large language models for post-discharge follow-up in erectile dysfunction care: a narrative review},
	journal = {Translational Andrology and Urology},
	volume = {15},
	number = {5},
	year = {2026},
	keywords = {},
	abstract = {Erectile dysfunction (ED) is common and closely linked to cardiometabolic disease, psychological distress, and relationship problems. Although effective treatments are available, outcomes depend on sustained post-discharge follow-up to assess response, adherence, adverse effects, and psychosocial needs. In practice, ED follow-up is often hindered by stigma, embarrassment, limited access, and fragmented communication. This narrative review examines the potential role, risks, and implementation requirements of large language model (LLM)-assisted follow-up in ED care.},
	issn = {2223-4691},	url = {https://tau.amegroups.org/article/view/154391}
}