Summary of High Order Reasoning For Time Critical Recommendation in Evidence-based Medicine, by Manjiang Yu et al.
High Order Reasoning for Time Critical Recommendation in Evidence-based Medicine
by Manjiang Yu, Xue Li
First submitted to arxiv on: 5 May 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
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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 presents a model of high-order reasoning that enables AI-enabled situation-aware software to assist human decision-making in time-critical scenarios. The Large Language Model (LLM) is used to offer recommendations in evidence-based medicine, specifically for ICU applications. The proposed system can evaluate numerous imminent and possible scenarios, retrieve vast amounts of information, and estimate various outcomes within a fraction of a second. High-order reasoning questions are applied to challenge the assumptions or preconditions of the decision-making process, helping humans avoid false-negative or false-positive errors. Experimental results demonstrate the LLM’s optimal performance in “what-if” scenarios, achieving high similarity with human doctors’ treatment plans. The model also shows strong performance in “why-not,” “so-what,” and “how-about” scenarios, providing detailed analysis and predicting patient life status after discharge from ICU with 70% accuracy. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper develops a way for AI to help humans make quick decisions in medical emergencies. The system can look at many possible outcomes and choose the best one based on evidence. It’s like asking “what if” questions to make sure the decision is correct. The AI also explains why it made certain choices, which helps doctors avoid making mistakes. The results show that this AI system works well in different situations, such as predicting patient recovery after discharge from ICU. |
Keywords
» Artificial intelligence » Large language model
