  Addressing the global challenge of breast cancer, this research explores the
fusion of generative AI, focusing on ChatGPT 3.5 turbo model, and the
intricacies of breast cancer risk assessment. The research aims to evaluate
ChatGPT's reasoning capabilities, emphasizing its potential to process rules
and provide explanations for screening recommendations. The study seeks to
bridge the technology gap between intelligent machines and clinicians by
demonstrating ChatGPT's unique proficiency in natural language reasoning. The
methodology employs a supervised prompt-engineering approach to enforce
detailed explanations for ChatGPT's recommendations. Synthetic use cases,
generated algorithmically, serve as the testing ground for the encoded rules,
evaluating the model's processing prowess. Findings highlight ChatGPT's
promising capacity in processing rules comparable to Expert System Shells, with
a focus on natural language reasoning. The research introduces the concept of
reinforcement explainability, showcasing its potential in elucidating outcomes
and facilitating user-friendly interfaces for breast cancer risk assessment.
