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.
Keywords: Artificial intelligence, ChatGPT, Computer vision, Convolutional Neural Network, Deep learning, Diagnosis, Large-language models, Machine learning, PCOS phenotypes, PCOS, Polycystic ovary syndrome, Precision medicine.
