A wide range of information sources is utilized to support diagnostic decisions and determine the most appropriate treatment strategies. These sources include medical records, imaging data, audio recordings, laboratory results, and more. Multimodal AI algorithms are gaining increasing relevance, as they focus on integrating diverse patient data sources to provide a more comprehensive and insightful analysis.

In their review ‘The Future of Multimodal Artificial Intelligence Models for Integrating Imaging and Clinical Metadata’, the authors examine how multimodal AI models combine imaging data with clinical metadata to advance healthcare applications. These models are becoming increasingly prominent due to their potential to enhance diagnostic accuracy and improve patient care. The authors emphasize the importance of data harmonization, model generalizability, and ethical considerations for the responsible development and implementation of these advanced AI systems.

The review also clarifies the concept of ‘multimodal’ AI in medical imaging and explores existing frameworks that integrate imaging with other clinical data types. Additionally, it addresses the shift toward more generalizable foundational models and discusses key trends and challenges related to database curation.