Medication errors—defined as failures in the treatment process that lead to, or have the potential to lead to, harm to the patient [1]—constitute a significant threat to public health worldwide. In England alone, more than 237 million medication errors are estimated to occur annually [2]. A study conducted in two adult outpatient cancer centers reported that these incidents affected 23% of prescribed drugs [3]. Additionally, approximately 3% of patients experience preventable harm related to medication use [4].

Medication errors can occur at any stage of the medication use process, including administration [5, 6], prescribing [7], or dispensing [8]. These errors arise not only in routine healthcare settings but also in clinical research, where complex protocols and investigational treatments may introduce additional risk factors [9, 10]. These incidents are inherently multifactorial, often resulting from interrelated factors such as fatigue [11, 12] or work overload [13]. Their consequences can range from minor inconveniences to serious adverse drug events [14, 15], including fatal outcomes [16, 17, 18]. Additionally, medication errors contribute to increased healthcare costs [2, 19] and prolonged hospital stays [20].

In response to this critical issue, several health agencies have launched campaigns to promote safer medication practices. The World Health Organization (WHO) introduced the Medication Without Harm initiative [21] and recently issued a policy brief to support its ongoing execution [22]. In England, the National Health Service (NHS) has identified medication safety as a key aspect of its Patient Safety Strategy [23], noting that drug-related incidents account for approximately 10% of all patient safety reports [24]. In the United States, the Food and Drug Administration (FDA) has long acknowledged the need to address medication errors, which led to the establishment of the Division of Medication Error Prevention and Analysis (DMEPA) in 1999 [25, 26]. This division is specifically dedicated to identifying, analyzing, and preventing medication errors in both premarket and postmarket stages of the drug development and regulatory process.

Various strategies have been explored to mitigate these preventable events [27, 28], including medication reconciliation [29, 30], staff training [31], the adoption of automated drug administration systems [32], or the development of packaging and labeling guidelines to reduce the risk of confusion between look-alike and sound-alike products [33]. However, these interventions face some drawbacks. For example, in clinical trials, investigational drugs may not have finalized commercial packaging or labeling, rendering design-focused strategies inapplicable. Automated medication administration systems require staff training and might cause technical issues [34]. Medication reconciliation entails extensive use of resources, organizational change, and interprofessional collaboration [35], limiting its implementation [36, 37]. Staff training can reduce the risk of medication errors, but financial challenges and workload pressures might limit its implementation [38].

Over recent years, machine learning (ML) methods have achieved notable success across a variety of domains, including computer vision [39, 40], natural language processing [41, 42, 43], and finance [44, 45]. In the healthcare sector, ML has demonstrated promising capabilities in applications such as medical image analysis [46, 47], early disease detection [48, 49], treatment recommendation [50, 51], and clinical trial risk assessment [52, 53, 54]. ML methods are particularly well-suited for learning from complex, and potentially multimodal datasets [55, 56]. As such, they hold strong promise for uncovering intricate relationships among patient characteristics, treatments, and outcomes—patterns that are frequently difficult to capture using traditional rule-based or statistical approaches, especially when working with large-scale data [57, 58]. Moreover, when integrated into healthcare workflows, ML models can provide real-time decision support, thereby contributing to safer and more efficient medical practice [59, 60]. Collectively, these capabilities underscore the promise of ML in the detection of medication errors, a challenge that has already motivated a growing body of research [61, 62, 63, 64, 65].

Since medication errors remain a major concern in clinical practice and research, a comprehensive understanding of how ML has been applied to detect them would be valuable to both researchers and healthcare professionals. For the scientific community, a scoping review could help synthesize current approaches, identify methodological trends, and highlight gaps for future investigation. For clinicians, it would offer an overview of existing tools and emerging technologies that could enhance medication safety. Previous work has highlighted the promise of ML in improving patient safety [66], and a recent scoping review has examined the use of ML in optimizing medication alerts—primarily during the prescribing phase [67]. However, to the best of our knowledge, no review has specifically addressed how ML has been applied across the broader spectrum of medication errors.

This work presents a scoping review protocol designed to systematically map the existing research on ML-based approaches to mitigate medication errors. Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR) guideline [68], we will perform structured searches across PubMed, Embase, and Web of Science, applying predefined inclusion and exclusion criteria to identify relevant studies. Key information—including model, data sources and type, evaluation method, and clinical context—will be extracted and analyzed using descriptive statistics, visualizations, thematic analysis, and narrative synthesis. By providing a comprehensive overview of the current literature, this review aims to support clinicians in identifying relevant tools and guide researchers in advancing the application of ML to improve patient safety.