Visual-textual integration in LLMs for medical diagnosis: A preliminary quantitative analysis
Multimodal LLMs showed higher diagnostic accuracy than physicians in text-only scenarios, even in cases designed to require visual interpretation, suggesting that while images can enhance diagnostic accuracy, they may not be essential in every instance. Although adding images further improved LLM performance, the magnitude of this improvement was smaller than that observed in physicians. These findings suggest that enhanced visual data processing may be needed for LLMs to achieve the degree of image-related performance gains seen in human examiners.
Authors
- Benjamin Scott Glicksberg (ORCID: https://orcid.org/0000-0003-4515-8090)
- Eyal Klang (ORCID: https://orcid.org/0000-0002-4567-3108)
- Mahmud Omar (ORCID: https://orcid.org/0009-0001-0438-0827)
- Girish N. Nadkarni (ORCID: https://orcid.org/0000-0001-6319-4314)
- Shelly Soffer (ORCID: https://orcid.org/0000-0002-7853-2029)
- Reem Agbareia
Institutions
- Hadassah Medical Center (IL)
- Rabin Medical Center (IL)
- Icahn School of Medicine at Mount Sinai (US)
Publication Details
- Journal
- Computational and Structural Biotechnology Journal
- Published
- 2024-12-21
- DOI
- https://doi.org/10.1016/j.csbj.2024.12.019
- Citations
- 8
- Primary Topic
- Biomedical Text Mining and Ontologies
- Type
- article
- Field-Weighted Citation Impact
- 1.87