WangLab at MEDIQA-M3G 2024: Multimodal Medical Answer Generation using Large Language Models
Augustin Toma, Ronald Xie, Steven Palayew, Gary Bader, Bo Wang
Abstract
This paper outlines our submission to the MEDIQA2024 Multilingual and Multimodal Medical Answer Generation (M3G) shared task. We report results for two standalone solutions under the English category of the task, the first involving two consecutive API calls to the Claude 3 Opus API and the second involving training an image-disease label joint embedding in the style of CLIP for image classification. These two solutions scored 1st and 2nd place respectively on the competition leaderboard, substantially outperforming the next best solution. Additionally, we discuss insights gained from post-competition experiments. While the performance of these two described solutions have significant room for improvement due to the difficulty of the shared task and the challenging nature of medical visual question answering in general, we identify the multi-stage LLM approach and the CLIP image classification approach as promising avenues for further investigation.- Anthology ID:
- 2024.clinicalnlp-1.60
- Volume:
- Proceedings of the 6th Clinical Natural Language Processing Workshop
- Month:
- June
- Year:
- 2024
- Address:
- Mexico City, Mexico
- Editors:
- Tristan Naumann, Asma Ben Abacha, Steven Bethard, Kirk Roberts, Danielle Bitterman
- Venues:
- ClinicalNLP | WS
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 624–634
- Language:
- URL:
- https://aclanthology.org/2024.clinicalnlp-1.60
- DOI:
- Cite (ACL):
- Augustin Toma, Ronald Xie, Steven Palayew, Gary Bader, and Bo Wang. 2024. WangLab at MEDIQA-M3G 2024: Multimodal Medical Answer Generation using Large Language Models. In Proceedings of the 6th Clinical Natural Language Processing Workshop, pages 624–634, Mexico City, Mexico. Association for Computational Linguistics.
- Cite (Informal):
- WangLab at MEDIQA-M3G 2024: Multimodal Medical Answer Generation using Large Language Models (Toma et al., ClinicalNLP-WS 2024)
- PDF:
- https://preview.aclanthology.org/jeptaln-2024-ingestion/2024.clinicalnlp-1.60.pdf