ReviewSexual medicine2026
Utilization and prospects of artificial intelligence in the diagnosis, prediction, and treatment of erectile dysfunction.
Review in Sexual medicine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
1 citing paper in PubMed.
- Erectile Dysfunction: A Comprehensive Review of Pathophysiology, Diagnosis and Contemporary Management.Medicina (Kaunas, Lithuania) · 2026Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Introduction: Erectile dysfunction (ED) represents a significant global public health challenge in men's health, with its prevalence exacerbated by population aging. Given the rapid advancement of artificial intelligence (AI) in healthcare, there is growing interest in its novel applications for ED diagnosis and treatment. Objectives: This systematic review synthesizes existing research on AI's role in ED management, examining its current development, application in diagnosis and treatment, and key benefits and challenges. Methods: We executed a PRISMA-guided systematic review across PubMed, Web of Science, and Chinese databases (CNKI/Wanfang) through June 19, 2025. Analysis targeted three domains: (1) the technological evolution of AI tools specific to ED, (2) applications in clinical diagnosis, prediction, and personalized treatment, and (3) the emerging role of large language models (LLMs). Results: The review identified major milestones in AI's technological evolution for ED and highlighted its significant clinical advantages, particularly through intelligent questionnaires and wearable devices enabling precise diagnosis, alongside efficacy in developing personalized treatments and predicting disease progression. While AI, particularly LLMs, demonstrates emerging potential, critical challenges persist. Conclusion: This review establishes a critical theoretical and technical foundation for implementing AI in men's healthcare, demonstrating its significant potential to transform the management of ED through improved diagnostics, personalized treatment, and predictive capabilities. While the interpretive nature of the synthesis presents an inherent limitation, cross-validation among researchers enhanced the reliability of the findings. However, clinical adoption remains contingent upon addressing key challenges related to data privacy, ethical considerations, and interoperability.
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What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.