AI second opinions prove valuable in medical diagnosis, complementing human expertise.
LLMs match physician consensus on 80% of medical cases
📚 https://arxiv.org/abs/2410.20636
🎯 Original Problem:
Current research mainly tests LLMs on medical licensing exams, but real-world medical diagnosis requires complex reasoning beyond standardized tests. We need to understand how LLMs perform as second-opinion tools in actual clinical scenarios where even experienced doctors seek peer consultation.
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🔧 Methods used in this study:
• Analyzed 183 complex medical cases from Medscape (2023-2024) with 361 questions
• Tested multiple LLMs against crowd-sourced physician responses
• Used entropy metrics to classify case ambiguity levels
• Implemented API-based model interactions to ensure consistent evaluation
• Evaluated both text-only and multimodal models for cases with imaging data
• Focused on cases where physicians actively sought second opinions
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💡 Key Insights:
• 74.7% of cases were either "somewhat ambiguous" (45.9%) or "moderately ambiguous" (28%)
• LLMs showed immunity to common cognitive biases affecting human clinical reasoning
• Models struggled most with cases requiring "clinical gestalt" or pattern recognition
• Vision-capable models showed slight improvements (76% vs 81%) on imaging cases
• LLMs processed 361 questions in under an hour vs 2-3 days for human doctors
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📊 Results:
• Latest foundational models achieved >80% accuracy vs physician consensus
• Performance varied significantly: 81% on straightforward cases, 43% on complex scenarios
• GPT-4 and Claude-3 models performed best (~80% accuracy)
• Open-source and smaller models struggled significantly (<55% accuracy)
• Cases with substantial physician debate showed lowest LLM consensus (44%)
