Artificial Intelligence (AI) has gained momentum in medicine, with radiology leading its adoption. Traditionally, radiologists maintain high diagnostic accuracy by integrating multiple imaging modalities to make diagnostic decisions for patients. Early efforts to incorporate AI into radiology utilized single‑modality image analysis, which has low clinical utility and accuracy. AI multimodal image analysis represents the next major innovation, as it extracts properties of each imaging modality (e.g., CT density, MRI soft tissue contrast, PET metabolic activity). This concept is significant, as it may improve the increasingly strained process of analyzing imaging data, which has raised radiologist workload. This narrative review analyzes literature on multimodal AI, diagnostic accuracy, workflow efficiency, and radiology applications, aiming to evaluate how multimodal AI image analysis can impact radiologist efficiency. Search terms including radiology AND artificial intelligence AND workflow, accuracy, and multimodal were used in PubMed and Google Scholar to identify 41 peer-reviewed articles published within the last 10 years. Findings showcase the ability to reduce reading errors and match experienced radiologist abilities. Incorporation into workflow reduces reading times by 11.3% and lowers interpretation delivery times from 11.2 days to 2.7 days . This allows radiologists to interpret more scans quicker and treat critical patients sooner. However, conclusions are limited by study heterogeneity. Ethical implications and limitations also arise, including lack of trust and interpretability, cybersecurity risks, and poor generalizability. Concerns that multimodal AI capabilities could eventually replace radiologists bring unease in the community. This review offers synthesized evidence showing that multimodal AI is a complementary tool for radiologists rather than a replacement.
