Artificial Intelligence in the Diagnosis and Management of PCOS

Le résumé fourni par la source

Artificial Intelligence (AI) has shown considerable potential in improving PCOS diagnosis and management. Key AI concepts such as machine learning, computer vision, deep learning, and natural language processing are discussed in the chapter. AI has proven invaluable in the prediction and diagnosis of PCOS, with studies showing high accuracy using AI-based models. Algorithms analyze ultrasound images, scleral images, and other clinical parameters, although the final diagnosis still requires an expert clinician. Machine learning algorithms, including Random Forest, have been beneficial in classifying PCOS and predicting sub-phenotypes. AI also aids in managing PCOS and its complications, analyzing endometrial immune cells, predicting complication risks, recommending diet interventions, and developing early detection systems. Large language models like Chat GPT and Google Bard play crucial roles, offering literature reviews, answering questions, generating patient education material, and facilitating personalized treatments. The future of AI in PCOS suggests a need for more collaboration between technology and clinical medicine researchers, converting clinical research into practical software, and adopting a multimodal AI approach. These advancements could significantly transform PCOS care and deepen our understanding of the disease.

Ce résumé expose les affirmations des auteurs. BNTIC ne l’interprète pas comme une validation indépendante des résultats.