Drafting the Future: The Dawn of AI Report Generation in Radiology

Abstract

Radiology faces a global shortage of radiologists. This shortage, particularly in the United States, affects timely diagnosis and exacerbates burnout due to escalating workloads. Because training new radiologists and developing the workforce takes a substantial amount of time, there is growing interest in using artificial intelligence (AI) as a key solution to help meet demand and improve efficiency. Recent years have seen the development of <i>comprehensive AI</i>, where a single algorithm is trained with multitask learning to classify and detect multiple abnormalities on images from high-volume modalities such as radiography and CT. Further, the parallel and rapid development of capability in large language models (LLMs) intersects with comprehensive AI, enabling the production of accurate and human-like draft reports. Multimodal LLMs are capable of accepting images as well as text as inputs, giving them the ability to generate a draft report directly from medical images; this marks a major step forward in AI application in radiology. These AI-produced reports can dramatically enhance efficiency, scaling the net capacity of the current workforce. The authors envision a future in which AI is used to fully automate reporting for high-volume modalities in routine clinical settings, and they discuss evaluation frameworks for safe and effective integration of these AI report generation tools into clinical workflows.

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