Exploring the feasibility of integrating ultra‐high field magnetic resonance imaging neuroimaging with multimodal artificial intelligence for clinical diagnostics

Abstract

Abstract Background The integration of 7 Tesla (7T) magnetic resonance imaging (MRI) with advanced multimodal artificial intelligence (AI) models represents a promising frontier in neuroimaging. The superior spatial resolution of 7TMRI provides detailed visualizations of brain structure, which are crucial forunderstanding complex central nervous system diseases and tumors. Concurrently, the application of multimodal AI to medical images enables interactive imaging‐based diagnostic conversation. Methods In this paper, we systematically investigate the capacity and feasibility of applying the existing advanced multimodal AI model ChatGPT‐4V to 7T MRI under the context of brain tumors. First, we test whether ChatGPT‐4V has knowledge about 7T MRI, and whether it can differentiate 7T MRI from 3T MRI. In addition, we explore whether ChatGPT‐4V can recognize different 7T MRI modalities and whether it can correctly offer diagnosis of tumors based on single or multiple modality 7T MRI. Results ChatGPT‐4V exhibited accuracy of 84.4% in 3T‐vs‐7T differentiation and accuracy of 78.9% in 7T modality recognition. Meanwhile, in a human evaluation with three clinical experts, ChatGPT obtained average scores of 9.27/20 in single modality‐based diagnosis and 21.25/25 in multiple modality‐based diagnosis. Our study indicates that single‐modality diagnosis and the interpretability of diagnostic decisions in clinical practice should be enhanced when ChatGPT‐4V is applied to 7T data. Conclusions In general, our analysis suggests that such integration has promise as a tool to improve the workflow of diagnostics in neurology, with a potentially transformative impact in the fields of medical image analysis and patient management.

Keywords

• 7T imaging
• benchmark
• ChatGPT‐4V
• CNS tumor
• multimodal AI