Medication errors pose a significant threat to public health. Despite efforts by health agencies and the implementation of various interventions, such as staff training, medication reconciliation and automation, the persistence of these incidents highlights the need for more effective, scalable solutions. In recent years, machine learning (ML) has emerged as a promising approach in healthcare, offering potential to detect and predict medication errors through data-driven insights. This scoping review aims to systematically map the existing literature on ML-based approaches to predict or detect medication errors across all stages of the medication use process. The review seeks to identify the range of ML applications in this domain, characterise methodological trends and highlight current knowledge gaps. The findings will provide a structured and accessible overview for both clinicians and researchers, supporting the development of safer, more data-informed medication practices.

The review will be conducted in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews guideline. Structured searches will be performed in PubMed, Embase and Web of Science, covering publications from 1 January 2015 to 28 April 2025. Predefined inclusion and exclusion criteria will be used to identify eligible studies. Key information—including ML models, data sources and type, evaluation methods and clinical contexts—will be extracted and analysed using descriptive statistics, visualisations, thematic analysis and narrative synthesis.

This study involves a review of existing literature and does not involve human participants, personal data or unpublished secondary data. As such, ethical approval was not required. All data analysed were obtained from publicly available sources. Findings of the scoping review will be disseminated through professional networks, conference presentations and publications in scientific journals.

This protocol has been registered on the Open Science Framework (https://doi.org/10.17605/OSF.IO/38SFY).

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.