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Performance of State-of-the-Art Multimodal Large Language Models on an Image-Rich Radiology Board Examination:

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Leading multimodal large language models (MLLMs) now outperform human experts on radiology board exams. Gemini models show significant gains with image analysis and offer cost-effective solutions for medical applications.

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Area of Science:

  • Artificial Intelligence in Medical Imaging
  • Multimodal Large Language Models (MLLMs)
  • Radiology Education and Assessment

Background:

  • The integration of Artificial Intelligence (AI) into healthcare, particularly in radiology, is rapidly advancing.
  • Multimodal Large Language Models (MLLMs) are emerging as powerful tools with the potential to analyze complex medical data, including images and text.
  • Assessing the capabilities of these MLLMs in high-stakes medical examinations is crucial for understanding their practical utility.

Purpose of the Study:

  • To evaluate the multimodal capabilities of current leading MLLMs using a 2024 radiology board examination.
  • To assess MLLMs' proficiency in interpreting and utilizing medical image content within the examination context.
  • To compare MLLM performance against human examinees and analyze their cost-effectiveness for potential radiology applications.

Main Methods:

  • Six contemporary MLLMs were tested on the 2024 Japan Radiological Society board examination (100 multiple-choice questions, 96 image-based).
  • Questions were translated into English by the MLLMs, and performance was analyzed with and without image data for specific models.
  • Models included GPT-4.1, o3, Claude 3.7 Sonnet, Claude 3.7 Sonnet-thinking, Gemini 2.5 Pro Preview, and Gemini 2.5 Flash Preview-thinking.

Main Results:

  • Gemini 2.5 Pro Preview achieved the highest accuracy (76.0%), surpassing the average human score (72.9%).
  • Performance significantly improved with image inclusion for Gemini 2.5 Pro Preview (75.0% vs. 63.5%, p=0.035) and Gemini 2.5 Flash Preview-thinking (68.8% vs. 57.3%, p=0.019).
  • Gemini models demonstrated top-tier performance at a highly competitive cost.

Conclusions:

  • Leading MLLMs, particularly Gemini 2.5 Pro Preview and o3, can exceed average human performance on radiology board examinations.
  • These models effectively leverage medical image information, showing significant performance gains when multimodal capabilities are utilized.
  • The Gemini series offers a compelling combination of high performance and cost-efficiency, indicating rapid advancements for radiology applications.