Summary of A Scalable Approach to Benchmarking the In-conversation Differential Diagnostic Accuracy Of a Health Ai, by Deep Bhatt et al.
A Scalable Approach to Benchmarking the In-Conversation Differential Diagnostic Accuracy of a Health AI
by Deep Bhatt, Surya Ayyagari, Anuruddh Mishra
First submitted to arxiv on: 17 Dec 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Human-Computer Interaction (cs.HC)
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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 The proposed study introduces a scalable benchmarking methodology for assessing health AI systems, which is demonstrated through August, an AI-driven conversational chatbot. The methodology employs 400 validated clinical vignettes across 14 medical specialties, using AI-powered patient actors to simulate realistic clinical interactions. The results show that August achieved a top-one diagnostic accuracy of 81.8% (327/400 cases) and a top-two accuracy of 85.0% (340/400 cases), significantly outperforming traditional symptom checkers. The system also demonstrated improved performance in specialist referrals, requiring fewer questions while maintaining empathetic dialogue throughout consultations. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study introduces an AI-powered healthcare chatbot called August that can diagnose medical conditions with high accuracy. The researchers tested August by giving it 400 scenarios of patients’ symptoms and then comparing its answers to what real doctors would say. August was able to correctly diagnose most cases, beating traditional symptom checkers. It also did well when trying to send patients to specialists for further help. The chatbot even asked fewer questions than the traditional way and still had a friendly conversation with patients. |
