Keywords: Language Models, Language Models
Motivation: What are the capabilities of multimodal large language models (LLMs) in addressing radiology-related questions, and can they enhance the performance of junior radiologists?
Goal(s): To compare the performance of multimodal LLMs against radiologists of varying expertise levels and assess the impact of LLM assistance on junior radiologists' skills.
Approach: This study evaluated the performance of multimodal LLMs against radiologists using the Radiology ImageQuest dataset, comprising 1,251 cases from six reputable sources.
Results: Advanced LLMs like GPT-4o and Claude-3.5-sonnet demonstrated performance comparable to senior radiologists. Junior radiologists, with GPT-4o's assistance, nearly doubled their accuracy and achieved mid-level performance after a three-month period.
Impact: Multimodal LLMs show promise in radiology education and practice, while further research is needed to validate their impact on real clinical applications
How to access this content:
For one year after publication, abstracts and videos are only open to registrants of this annual meeting. Registrants should use their existing login information. Non-registrant access can be purchased via the ISMRM E-Library.
After one year, current ISMRM & ISMRT members get free access to both the abstracts and videos. Non-members and non-registrants must purchase access via the ISMRM E-Library.
After two years, the meeting proceedings (abstracts) are opened to the public and require no login information. Videos remain behind password for access by members, registrants and E-Library customers.
Keywords