Summary of Umass-bionlp at Mediqa-m3g 2024: Dermprompt — a Systematic Exploration Of Prompt Engineering with Gpt-4v For Dermatological Diagnosis, by Parth Vashisht et al.
UMass-BioNLP at MEDIQA-M3G 2024: DermPrompt – A Systematic Exploration of Prompt Engineering with GPT-4V for Dermatological Diagnosis
by Parth Vashisht, Abhilasha Lodha, Mukta Maddipatla, Zonghai Yao, Avijit Mitra, Zhichao Yang, Junda Wang, Sunjae Kwon, Hong Yu
First submitted to arxiv on: 27 Apr 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computation and Language (cs.CL)
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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 paper presents our team’s participation in the MEDIQA-ClinicalNLP2024 shared task B, focusing on diagnosing clinical dermatology cases. A novel approach integrates large multimodal models, leveraging GPT-4V under a retriever and re-ranker framework. The investigation reveals that GPT-4V can accurately retrieve the correct skin condition 85% of the time using dermatological images and brief patient histories. Naive Chain-of-Thought (CoT) works well for retrieval, while Medical Guidelines Grounded CoT is required for accurate dermatological diagnosis. A Multi-Agent Conversation (MAC) framework is introduced, showcasing its superior performance and potential over the best CoT strategy. The results suggest that combining naive CoT for retrieval and multi-agent conversation for critique-based diagnosis enables GPT-4V to lead to early and accurate diagnoses of dermatological conditions. This work has implications for improving diagnostic workflows, supporting dermatological education, and enhancing patient care by providing a scalable, accessible, and accurate diagnostic tool. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about using computers to help doctors diagnose skin problems. The team used a special type of artificial intelligence called GPT-4V to look at pictures of skin conditions and brief patient histories. They found that this AI can accurately identify the correct skin condition most of the time (85%). They also developed new ways for the AI to understand medical guidelines and make diagnoses more accurate. The results show that combining these approaches can lead to better and faster diagnoses. This has important implications for improving how doctors diagnose skin problems, making it easier for them to learn, and helping patients get better care. |
Keywords
» Artificial intelligence » Gpt
