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The increasing volume of radiological images and the associated workload of report generation necessitate efficient solutions, making artificial intelligence (AI) a crucial tool to streamline this process for radiologists. Recent years have seen a surge in research exploring AI-driven radiological report generation directly from images, particularly with the emergence of large vision language models. However, a comprehensive understanding of the current landscape, including specific limitations and the extent to which efforts move beyond abnormality detection to full textual report generation, remains unclear. This scoping review aims to systematically map the existing literature to provide an overview of the current state of AI in generating radiological reports from medical images, including the scope and limitations of existing research. To our knowledge, no prior scoping review has comprehensively mapped this landscape, especially considering recent advancements in foundation models in medicine and related AI architectures. Considering the explosive growth of related studies in recent years, a comprehensive scoping review will be significant in mapping the current research status and understanding relevant limitations.
This scoping review will follow the Preferred Reporting Items for Systematic Reviews and Meta-Analysis extension for Scoping Reviews guidelines to map the literature on AI generating radiological reports from medical images. We will search PubMed, Scopus and Web of Science for peer-reviewed articles (January 2016 to March 2025) using keywords related to AI, radiological reports and medical images. Original research in English focusing on AI-driven report generation from images will be included and studies without report generation or not using medical images as input will be excluded. Two independent reviewers will perform a two-stage screening. Data extraction, guided by the Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modelling Studies checklist and focusing on study characteristics, AI methods, image modalities, report features, limitations and key findings, will be analysed using narrative and descriptive synthesis, with results presented in tables, figures and a narrative summary.
This protocol describes a scoping literature review methodology that does not involve research on humans, animals or their data; therefore, no ethical approval is required. Following the review, the results will be considered for publication in a relevant peer-reviewed journal and may be shared with stakeholders through reports or summaries.
Coronary artery disease (CAD) is a common problem amongst diabetic foot syndrome (DFS) patients, associated with peripheral arterial disease. This analytic cross-sectional study investigates the diagnostic efficacy of the Toe Brachial Index (TBI) in the detection of CAD in 62 DFS patients. The presence of CAD was assessed by longitudinal strain echocardiography, a sensitive method that provides a more accurate measure of intrinsic left ventricular contractility than left ventricular ejection fraction, especially in diabetic patients. Univariate and multivariate logistic regression identified CAD-associated factors. Receiver operating characteristic curve evaluated TBI and toe pressure's diagnostic performance for CAD. p-Values < 0.05 were considered significant. There was a significant association between TBI and CAD, with each 0.01 increase in TBI associated with a 15% decrease in the odds of CAD development (odds ratio = 0.85, 95% CI: 0.72–0.99, p = 0.039). TBI demonstrated an area under the curve of 0.854, a sensitivity of 80.0% and a specificity of 66.7% at a cut-off of 0.69. Additionally, toe pressure exhibited an area under the curve of 0.845, sensitivity of 74.0% and specificity of 75.0% at a cut-off of 68.0 mmHg. Overall accuracy for TBI and toe pressure was 77.4% and 74.2%, respectively, indicating their potential for CAD risk stratification in the DFS population. This study highlights a significant association between low TBI and the presence of CAD in DFS patients. Consequently, TBI emerges as a valuable screening tool for identifying CAD within this population.
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