Automated Radiology Report Generation Using Multimodal Foundation Models: A Systematic Review Of Clinical Accuracy And Safety
Abstract
Background: The global landscape of diagnostic radiology is currently navigating a precarious inflection point. As imaging volumes surge due to aging populations and expanded screening protocols, the workforce remains critically constrained. Recent global surveys indicate that over 53% of radiologists are experiencing burnout, with workforce shortages cited as a primary concern by nearly half of the profession. This systemic strain exacerbates the risk of diagnostic error—a phenomenon already estimated to affect approximately 40 million patients annually worldwide. Against this backdrop, the integration of Artificial Intelligence (AI), specifically Multimodal Foundation Models (MFMs), has emerged as a potential panacea. These models, capable of processing both visual and textual data, promise to automate the labor-intensive process of radiology report generation (RRG), thereby potentially alleviating clinician workload and standardizing diagnostic quality. However, the transition from experimental architectures to clinical deployment is fraught with challenges related to factual consistency, safety, and trust. Objective: This systematic review aims to provide an exhaustive, nuanced evaluation of the current state of automated radiology report generation using MFMs. Methods: A comprehensive systematic literature search was conducted across major medical and technical databases covering the period from 2020 to 2025. The review adhered to PRISMA guidelines where applicable.…
How strong is the evidence?
Evidence Hub reads this as a systematic review, done in people. Its evidence level is 1 of 5 (Highest · pooled evidence). Pooled evidence from several studies sits at the top of the evidence pyramid. Reported size: 40 participants or records.
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