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.

翻译：针对全球乳腺癌防治挑战，本研究探索了以ChatGPT 3.5 turbo模型为核心的生成式人工智能与乳腺癌风险评估复杂规则的融合。研究旨在评估ChatGPT的推理能力，重点关注其处理规则并为筛查建议提供解释的潜力。通过展示ChatGPT在自然语言推理方面的独特优势，本研究致力于弥合智能机器与临床医师之间的技术鸿沟。方法学采用监督式提示工程策略，强制要求ChatGPT为其建议提供详细解释。通过算法生成的合成用例作为编码规则的测试场，用于评估模型的处理能力。研究结果凸显了ChatGPT在处理规则方面与专家系统外壳相当的潜力，尤其侧重于自然语言推理能力。本研究提出了"强化可解释性"概念，展示了其在阐明结果和构建用户友好的乳腺癌风险评估界面方面的应用前景。