ReviewTranslational andrology and urology2026
Progress in the research of artificial intelligence in andrology: a narrative review.
Review in Translational andrology and urology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background and Objective: Artificial intelligence (AI) is an important branch of computer science. In recent years, it has been widely applied in the healthcare field based on machine learning (ML). The diagnosis and treatment of male disorders face various limitations such as complex etiological and pathological mechanisms, strong subjectivity in diagnosis, limited treatment options, and low patient motivation to seek medical care. This article systematically searches and summarizes the application progress of AI in male infertility (MI), erectile dysfunction (ED), premature ejaculation (PE), and prostatic diseases. The overall goal is to provide a structured reference for the development of intelligent male health diagnosis and treatment systems, and to preliminarily explore the future interdisciplinary research directions in the intersection of AI and andrology. Methods: This review focused on literature from 2010 to 2026 concerning AI and male diseases. This study conducted a search on PubMed, Web of Science, Scopus and China National Knowledge Infrastructure (CNKI) using keywords like "artificial intelligence", "machine learning", "infertility, male", "erectile dysfunction", "premature ejaculation" and "prostatic diseases". Both Chinese and English literature have been included. Reviews, original articles and case reports were searched, and off-topic studies were excluded. Key Content and Findings: Current research has found that the application of AI covers multiple aspects: participating in clinical laboratory tests through deep learning algorithms; integrating clinical routine indicators for disease risk prediction; developing intelligent medical devices; using imaging to assist in disease differentiation and classification and exploring pathological mechanisms; assisting in each stage of surgery. In addition, large language models (LLMs) and micro-robots are also in their infancy in medical applications. Conclusions: AI has been introduced into multiple fields of andrology, including MI, ED, PE, and prostatic diseases. This technology demonstrates certain advantages in areas like detection, imaging, intelligent devices, disease prediction, and early screening. However, current research still faces challenges such as data acquisition, model construction, validation methods, regulatory approval, medical reimbursement and ethical norms. Future work should focus more on clinical needs and promote the application of AI in andrology.
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What Socratic 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 Socratic graph.