AI models detect PCOS, eating disorders with explainability

Researchers have developed open-source language models to detect a triple burden of polycystic ovary syndrome (PCOS), body image distress, and disordered eating in social media posts. Using a dataset of 1,000 PCOS-related posts, three models (Gemma-2-2B, Qwen3-1.7B, and DeepSeek-R1-Distill-Qwen-1.5B) were fine-tuned with Low-Rank Adaptation to provide explanations and textual evidence. The top-performing model achieved 75.3% accuracy on a held-out set, demonstrating robust comorbidity detection and explainability, though its effectiveness decreases with diagnostic complexity, suggesting its primary use for screening. AI

IMPACT Demonstrates AI's potential for early screening of complex comorbidities in public health data.

RANK_REASON Academic paper detailing a novel application of AI for medical research. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

• DeepSeek-R1-Distill-Qwen-1.5B
• Gemma-2-2B
• Lee et al. (2017)
• Qwen3-1.7B

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