Improving Radiology Report Generation Systems by Removing Hallucinated References to Non-existent Priors

Understand

Current deep learning models trained to generate radiology reports from chest radiographs are capable of producing clinically accurate, clear, and actionable text that can advance patient care.

• However, such systems all succumb to the same problem: making hallucinated references to non-existent prior reports.
• Such hallucinations occur because these models are trained on datasets of real-world patient reports that inherently refer to priors.
• To this end, we propose two methods to remove references to priors in radiology reports: (1) a GPT-3-based few-shot approach to rewrite medical reports without references to priors; and (2) a BioBERT-based token classification approach to directly remove words referring to priors.