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Enhancing radiology workflows through collaborative AI-assisted chest X-ray reporting using large vision-language
Chantal Pellegrini1,2, Ege Özsoy3,4, Florian T Gassert5
1School of Computation, Information and Technology, Technical University of Munich, Munich, Germany. chantal.pellegrini@gmail.com.
Objectives:
To evaluate whether collaborative assistance from an artificial intelligence-based tool that proposes partial radiology report content can improve reporting efficiency and radiologist satisfaction in chest X-ray interpretation, without compromising report quality.
Materials And Methods:
In a retrospective study, three radiologists reported 50 MIMIC-CXR chest X-rays twice, once with artificial intelligence (AI) assistance and once without. A specialized large vision-language model (LVLM) provided real-time suggestions, which could be accepted, modified or rejected. The study evaluated writing time, suggestion acceptance, report length and quality and assessed usability and suggestion quality on a 5-point Likert-scale questionnaire. Statistical analysis used paired t-tests or Wilcoxon signed-rank tests based on normality.
Results:
AI assistance reduced mean writing time by 7.80% (p = 0.08), with significant gains for complex reports (18.34%, p < 0.001). Efficiency improvements correlated with suggestion acceptance and were user-dependent, with benefits up to 27.24% (CI: [17.34, 37.14], p < 0.001) for radiologists with high acceptance. Report quality and length remained stable, indicating preserved diagnostic accuracy without degradation. Radiologists rated the tool highly for ease of use (mean: 4.33) and desired regular use (mean: 4), noting minimal errors (mean: 1.67).
Conclusion:
Collaborative AI assistance with an LVLM can improve reporting efficiency if well adopted, particularly for complex cases, without compromising quality, and is well-received by radiologists. These exploratory findings suggest potential to optimize radiology workflows through collaborative reporting and warrant prospective validation in clinical settings.
Critical Relevance Statement:
This study critically evaluates a collaborative AI-assisted reporting tool for chest X-rays, demonstrating its potential to enhance radiologist efficiency without compromising automatically measured report quality, thereby demonstrating a potential path for practical integration of AI into clinical radiology workflows.
Key Points:
A collaborative vision-language model supported radiology workflow is proposed, and its effectiveness is studied in a user study. Mean writing time for a radiology report decreases with AI support without affecting report quality. The AI-assisted tool was rated highly for usability and integration into clinical workflow, supporting its practical adoption in radiology reporting.