The Impact of AI Assistance on Radiology Reporting: A Pilot Study Using Simulated AI Draft Reports

Radiologists face increasing workload pressures amid growing imaging volumes, creating risks of burnout and delayed reporting times. While artificial intelligence (AI) based automated radiology report generation shows promise for reporting workflow optimization, evidence of its real-world impact on clinical accuracy and efficiency remains limited. This study evaluated the effect of draft reports on radiology reporting workflows by conducting a three reader multi-case study comparing standard versus AI-assisted reporting workflows. In both workflows, radiologists reviewed the cases and modified either a standard template (standard workflow) or an AI-generated draft report (AI-assisted workflow) to create the final report. For controlled evaluation, we used GPT-4 to generate simulated AI drafts and deliberately introduced 1-3 errors in half the cases to mimic real AI system performance. The AI-assisted workflow significantly reduced average reporting time from 573 to 435 seconds (p=0.003), without a statistically significant difference in clinically significant errors between workflows. These findings suggest that AI-generated drafts can meaningfully accelerate radiology reporting while maintaining diagnostic accuracy, offering a practical solution to address mounting workload challenges in clinical practice.

Direct Preference Optimization for Suppressing Hallucinated Prior Exams in Radiology Report Generation

Recent advances in generative vision-language models (VLMs) have exciting potential implications for AI in radiology, yet VLMs are also known to produce hallucinations, nonsensical text, and other unwanted behaviors that can waste clinicians' time and cause patient harm. Drawing on recent work on direct preference optimization (DPO), we propose a simple method for modifying the behavior of pretrained VLMs performing radiology report generation by suppressing unwanted types of generations. We apply our method to the prevention of hallucinations of prior exams, addressing a long-established problem behavior in models performing chest X-ray report generation. Across our experiments, we find that DPO fine-tuning achieves a 3.2-4.8x reduction in lines hallucinating prior exams while maintaining model performance on clinical accuracy metrics. Our work is, to the best of our knowledge, the first work to apply DPO to medical VLMs, providing a data- and compute- efficient way to suppress problem behaviors while maintaining overall clinical accuracy.

Style-Aware Radiology Report Generation with RadGraph and Few-Shot Prompting

Automatically generated reports from medical images promise to improve the workflow of radiologists. Existing methods consider an image-to-report modeling task by directly generating a fully-fledged report from an image. However, this conflates the content of the report (e.g., findings and their attributes) with its style (e.g., format and choice of words), which can lead to clinically inaccurate reports. To address this, we propose a two-step approach for radiology report generation. First, we extract the content from an image; then, we verbalize the extracted content into a report that matches the style of a specific radiologist. For this, we leverage RadGraph -- a graph representation of reports -- together with large language models (LLMs). In our quantitative evaluations, we find that our approach leads to beneficial performance. Our human evaluation with clinical raters highlights that the AI-generated reports are indistinguishably tailored to the style of individual radiologist despite leveraging only a few examples as context.