The Role of Large Language Models in Hormonal Contraception Consultation

Background: Large Language Models (LLMs) demonstrate promise in medical applications, but their performance in hormonal contraception consultation remains underexplored. Objective: To evaluate the accuracy and comprehensiveness of LLM-generated hormonal contraception counseling compared to evidence-based guidelines. Methods: Ten fictitious clinical case scenarios representing common contraceptive counseling situations were presented to Chat-GPT, Google Gemini, and OpenEvidence. Cases assessed medical eligibility screening, contraindication recognition, drug interactions, side effect management, and emergency contraception guidance. Two board-certified obstetrician-gynecologists independently evaluated the responses based on international clinical guidelines. Results: Across the ten predefined clinical scenarios, all three LLMs achieved high accuracy in identifying contraindications according to Medical Eligibility Criteria (MEC) and provided appropriate alternative contraceptive recommendations. Models demonstrated high proficiency in managing drug interactions, particularly the lamotrigine-estrogen interaction, and provided evidence-based side effect management strategies. However, the communication styles differed. Chat-GPT emphasized structured consultation and shared decision-making, Gemini provided practical action-oriented guidance, and OpenEvidence delivered concise evidence-focused summaries. Conclusions: In this limited set of standardized fictitious cases, the evaluated LLMs generally provided responses that were broadly aligned with selected guideline recommendations. Current LLMs require cautious deployment, given limitations in individualized assessment and health literacy optimization, and are best positioned as complementary educational tools rather than replacements for professional contraceptive counseling.