Summary of Reinforcement Of Explainability Of Chatgpt Prompts by Embedding Breast Cancer Self-screening Rules Into Ai Responses, By Yousef Khan and Ahmed Abdeen Hamed
Reinforcement of Explainability of ChatGPT Prompts by Embedding Breast Cancer Self-Screening Rules into AI Responses
by Yousef Khan, Ahmed Abdeen Hamed
First submitted to arxiv on: 21 Apr 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper explores the fusion of generative AI, specifically ChatGPT 3.5 turbo model, with breast cancer risk assessment to address the global challenge of this disease. The study evaluates ChatGPT’s reasoning capabilities, focusing on its ability to process rules and provide explanations for screening recommendations. The research aims to bridge the technology gap between intelligent machines and clinicians by demonstrating ChatGPT’s proficiency in natural language reasoning. The methodology employs a supervised prompt-engineering approach to enforce detailed explanations for ChatGPT’s recommendations. Synthetic use cases are generated algorithmically to test the model’s processing prowess, evaluating its capacity to process rules comparable to Expert System Shells. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary ChatGPT is a powerful AI tool that can help doctors and researchers better understand breast cancer risk assessment. The paper shows how ChatGPT can be used to analyze complex medical data and provide clear explanations for screening recommendations. This technology has the potential to improve patient care by helping healthcare providers make more informed decisions. |
Keywords
» Artificial intelligence » Prompt » Supervised
