Reinforcement of Explainability of ChatGPT Prompts by Embedding Breast Cancer Self-Screening Rules into AI Responses

TL;DR

This study enhances ChatGPT's explainability in breast cancer screening by embedding self-screening rules, demonstrating improved reasoning and user understanding through a supervised prompt-engineering approach.

Contribution

It introduces a novel method to reinforce explainability in ChatGPT by embedding breast cancer screening rules, bridging AI and clinical reasoning.

Findings

ChatGPT effectively processes embedded screening rules.

Reinforced explainability improves user understanding.

Promising capacity in natural language reasoning for medical advice.

Abstract

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.