UMass-BioNLP at MEDIQA-M3G 2024: DermPrompt -- A Systematic Exploration of Prompt Engineering with GPT-4V for Dermatological Diagnosis
<|reference_start|>UMass-BioNLP at MEDIQA-M3G 2024: DermPrompt -- A Systematic Exploration of Prompt Engineering with GPT-4V for Dermatological Diagnosis: This paper presents our team's participation in the MEDIQA-ClinicalNLP2024 shared task B. We present a novel approach to diagnosing clinical dermatology cases by integrating large multimodal models, specifically leveraging the capabilities of GPT-4V under a retriever and a re-ranker framework. Our investigation reveals that GPT-4V, when used as a retrieval agent, can accurately retrieve the correct skin condition 85% of the time using dermatological images and brief patient histories. Additionally, we empirically show that Naive Chain-of-Thought (CoT) works well for retrieval while Medical Guidelines Grounded CoT is required for accurate dermatological diagnosis. Further, we introduce a Multi-Agent Conversation (MAC) framework and show its superior performance and potential over the best CoT strategy. The experiments suggest that using naive CoT for retrieval and multi-agent conversation for critique-based diagnosis, GPT-4V can lead to an early and accurate diagnosis of dermatological conditions. The implications of this work extend to improving diagnostic workflows, supporting dermatological education, and enhancing patient care by providing a scalable, accessible, and accurate diagnostic tool.<|reference_end|>
@article{vashisht2024umass-bionlp,
    title={UMass-BioNLP at MEDIQA-M3G 2024: DermPrompt -- A Systematic Exploration
  of Prompt Engineering with GPT-4V for Dermatological Diagnosis},
    author={Parth Vashisht, Abhilasha Lodha, Mukta Maddipatla, Zonghai Yao, Avijit
  Mitra, Zhichao Yang, Junda Wang, Sunjae Kwon, Hong Yu},
    journal={arXiv preprint arXiv:2404.17749},
    year={2024},
    archivePrefix={arXiv},
    eprint={2404.17749},
    primaryClass={cs.AI cs.CL}
}