AI-driven patient-centered care: A digital transformation framework for gynecologic cancer genetic counseling

Abstract

Objectives This study evaluates artificial intelligence (AI) reasoning capabilities in gynecologic cancer genetic counseling, comparing the performance of ChatGPT and DeepSeek models to guide patient-centered AI implementation in clinical genetics. Methods Using 40 National Comprehensive Cancer Network-aligned counseling scenarios, we conducted blinded dual-oncologist evaluations of two large language models. Methodological rigor included model anonymization, a pre-calibrated scoring framework, and validated metrics (Global Quality Scale and Patient Education Materials Assessment Tool) assessing informational coherence, understandability, and actionability. Results DeepSeek demonstrated superior informational breadth (mean character difference: −609.0, p < .0001) and visual communication (diagram integration, p < .01), with 49-fold greater probability in recommending clear and actionable actions ( p < .01, OR = 49.0). ChatGPT excelled in concise summarization (22% faster response generation, p = .013). Conclusion Strategic AI model selection—leveraging DeepSeek's visually-rich, structured educational approach for complex information, and ChatGPT's concise, rapid summarization for efficient communication—enhances patient-centered genetic education when combined with clinician oversight. This framework supports healthcare's digital transformation by optimizing human-AI collaboration in hereditary cancer care.

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