Consensus, dissensus and synergy between clinicians and specialist foundation models in radiology report generation
CoRR(2023)
摘要
Radiology reports are an instrumental part of modern medicine, informing key
clinical decisions such as diagnosis and treatment. The worldwide shortage of
radiologists, however, restricts access to expert care and imposes heavy
workloads, contributing to avoidable errors and delays in report delivery.
While recent progress in automated report generation with vision-language
models offer clear potential in ameliorating the situation, the path to
real-world adoption has been stymied by the challenge of evaluating the
clinical quality of AI-generated reports. In this study, we build a
state-of-the-art report generation system for chest radiographs, Flamingo-CXR,
by fine-tuning a well-known vision-language foundation model on radiology data.
To evaluate the quality of the AI-generated reports, a group of 16 certified
radiologists provide detailed evaluations of AI-generated and human written
reports for chest X-rays from an intensive care setting in the United States
and an inpatient setting in India. At least one radiologist (out of two per
case) preferred the AI report to the ground truth report in over 60$\%$ of
cases for both datasets. Amongst the subset of AI-generated reports that
contain errors, the most frequently cited reasons were related to the location
and finding, whereas for human written reports, most mistakes were related to
severity and finding. This disparity suggested potential complementarity
between our AI system and human experts, prompting us to develop an assistive
scenario in which Flamingo-CXR generates a first-draft report, which is
subsequently revised by a clinician. This is the first demonstration of
clinician-AI collaboration for report writing, and the resultant reports are
assessed to be equivalent or preferred by at least one radiologist to reports
written by experts alone in 80$\%$ of in-patient cases and 66$\%$ of intensive
care cases.
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