Third-party cyber evaluations involving OpenAI models
openai.com·14h ago
AI Research
arXiv CS.AI·May 26·5 min read
Uncertainty Reasoning with Large Language Models for Explainable Disease Diagnosis
TL;DR
Clinical decision-making often struggles with incomplete patient narratives, leading to unreliable diagnoses. A neuro-symbolic reasoning framework was developed to integrate large language models (LLMs) with formal logic for explainable medical diagnosis.
✦ Why It Matters
Engineers can leverage this framework to develop more reliable and interpretable AI systems for medical diagnosis.
Key Takeaways
- Integrate LLMs with formal logic for better medical diagnosis.
- Utilize fuzzy logic to handle uncertainty in patient narratives.
- Enable iterative refinement of diagnoses based on physician feedback.
- Validate AI-driven conclusions against clinical standards.
How It Works
The framework embeds patient narratives and clinical guidelines into a neural knowledge base, allowing LLMs to extract structured medical information. It employs a two-stage reasoning process: first, it generalizes diagnostic patterns from the narratives, and second, it verifies these inferences using a logic programming engine.
Each symptom is treated as a fuzzy predicate with probabilistic weights, enabling nuanced reasoning that aligns with clinical standards.