Textual Proficiency and Visual Deficiency: A Comparative Study of Large Language Models and Radiologists in MRI Artifact Detection and Correction.

Objective: To assess the performance of Large Language Models (LLMs) in detecting and correcting MRI artifacts compared to radiologists using text-based and visual questions.

Methods: This cross-sectional observational study included three phases. Phase 1 involved six LLMs (ChatGPT o1-preview, ChatGPT-4o, ChatGPT-4V, Google Gemini 1.5 Pro, Claude 3.5 Sonnet, Claude 3 Opus) and five radiologists (two residents, two junior radiologists, one senior radiologist) answering 42 text-based questions on MRI artifacts. In Phase 2, the same radiologists and five multimodal LLMs evaluated 100 MRI images, each containing a single artifact. Phase 3 reassessed the identical tasks 1.5 months later to evaluate temporal consistency. Responses were graded using 4-point Likert scales for "Management Score" (text-based) and "Correction Score" (visual). McNemar's test compared response accuracy, and the Wilcoxon test assessed score differences.

Results: LLMs outperformed radiologists in text-based tasks, with ChatGPT o1-preview scoring the highest (3.71±0.60 in Round 1; 3.76±0.84 in Round 2) (p<0.05). In visual tasks, radiologists performed significantly better, with the Senior Radiologist achieving 92% and 94% accuracy in Rounds 1 and 2, respectively (p<0.05). The top-performing LLM (ChatGPT-4o) achieved only 20% and 18% accuracy. Correction Scores mirrored this difference, with radiologists consistently scoring higher than LLMs (p<0.05).

Conclusions: LLMs excel in text-based tasks but have notable limitations in visual artifact interpretation, making them unsuitable for independent diagnostics. They are promising as educational tools or adjuncts in "human-in-the-loop" systems, with multimodal AI improvements necessary to bridge these gaps.