ArticleBMC medical informatics and decision making2025
Application of ChatGPT-based artificial intelligence in the diagnosis and management of polycystic ovary syndrome.
Article in BMC medical informatics and decision making, 2025. 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.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors.
Funding
Abstract
This study systematically develops and evaluates the application value of the PCOS-GPT system, an artificial intelligence (AI) assistant based on ChatGPT technology, in the diagnosis and management of polycystic ovary syndrome (PCOS). The research explores innovative pathways for AI-enabled PCOS diagnosis and treatment, aiming to provide an adjunctive diagnostic tool for standardized clinical decision support. Methods: An evidence-based PCOS knowledge base was constructed, covering dimensions such as epidemiology, etiology, clinical manifestations, diagnosis, treatment, and prognosis. The PCOS-GPT system was developed using GPT-3.5 pretraining combined with fine-tuning on domain-specific datasets. Using data from 85 patients, the diagnostic and therapeutic performance of PCOS-GPT was evaluated multidimensionally-accuracy, readability, and operability-using diagnoses by three expert physicians as the gold standard. Compared with GPT-4, PCOS-GPT demonstrated advantages in diagnostic accuracy for PCOS (95.63% vs. 96.40%). Conclusion: PCOS-GPT is an intelligent diagnostic and therapeutic support tool with advantages in diagnostic accuracy. It holds promise for improving standardization in diagnosis and treatment, empowering patient self-management, enhancing access to high-quality healthcare resources, and offering comprehensive health management for PCOS patients. This innovation promotes the development of smart healthcare, benefiting women's health.
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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.