Recent advancements in structured reporting augmented by artificial intelligence (AI) have made significant strides in enhancing the accuracy of bedside chest radiograph reporting, according to a comprehensive prospective study conducted in 2026. The findings emphasize the superiority of AI-prefilled structured reporting (AI-SR) over traditional free-text reporting in boosting diagnostic accuracy and efficiency.
The Rise of AI Tools in Radiology
The field of radiology has recently embraced the integration of structured reporting (SR) alongside AI tools.
Structured reporting enhances the standardization and thoroughness of reports, bolstering clinical decision-making and information extraction. However, it comes with drawbacks: its rigid templates may divert a radiologist’s focus from the diagnostic task and fail to encapsulate the intricacies of complex findings.
Additionally, AI tools can illuminate missed findings and alleviate workload, but they also introduce practical challenges, such as issues of automation bias and potential overdependence among less experienced radiologists.
Effect of AI Tools on Diagnostic Precision and Workflow
In a prospective comparative reader study, eight readers assessed 35 bedside chest radiographs. The group included four novice readers (residents in training and undergraduate medical students) and four experienced readers (resident radiologists in training).
The research found no significant differences in diagnostic accuracy between free-text reporting and structured reporting; however, AI-SR significantly improved diagnostic accuracy across all reader categories. Notably, novice readers benefited the most, as AI assistance elevated their diagnostic performance to levels comparable with seasoned practitioners.
Furthermore, AI-SR markedly reduced reporting times. The average time taken to report on a chest radiograph plummeted from 88.1 seconds ± 38.4 with free-text reporting to 37.3 seconds ± 18.2 for structured reporting, and further down to 25 seconds ± 8.8 with AI-SR.
While novice readers displayed substantial enhancements in efficiency with both SR and AI-SR compared to free-text reporting, no significant efficiency improvements were observed for experienced readers between SR and AI-SR.
Methodological Considerations
A critical limitation of this study was its modest sample size, comprising only 35 radiographs evaluated by eight readers.
All participants completed the three reading sessions in a set sequence using the same images. While the potential for learning effects and ordering biases exists, careful measures such as a two-week interval between sessions and the absence of feedback were implemented to minimize these concerns. Diagnostic inaccuracies notably improved only with the introduction of AI tools in the final session.
The study’s focus on chest radiographs and the methodological variations among emerging studies (e.g., the use of dictation in reporting) may limit the generalization of its findings.
Furthermore, the study pointed out algorithmic aversion: practitioners sometimes disregarded even accurate AI recommendations when framed as experimental. Six readers expressed low trust in the AI model.
Clinical Implications
While structured reporting and AI-SR present exciting possibilities, they also evoke debate within the medical community.
Researchers advocate for evaluating multiple factors beyond mere algorithmic accuracy. This includes the timing of AI outputs, distinctions between experimental and approved tools, and the design of user interfaces. The study encourages a systematic exploration of these aspects to optimize human-AI collaboration in the radiology field.
References
Khoobi M et al. Effect of reporting mode and clinical experience on radiologists’ gaze and image analysis behaviour at chest radiography. Radiology. 2026;318(2). DOI: 10.1148/radiol.251348.
Gaube S. Do as AI say: susceptibility in deployment of clinical decision-aids. NPJ Digit Med. 2021;4(1):31.
