# Machine learning for medication error detection: a scoping review

## TL;DR

This review explores how machine learning can detect medication errors, highlighting current methods and gaps in real-world application.

## Contribution

The paper provides a comprehensive scoping review of ML approaches for medication error detection, identifying key trends and limitations.

## Key findings

- Most studies focus on prescription errors using structured data and tree-based models.
- Fewer studies address medication-administration errors using multimodal data and neural networks.
- Real-world evaluation and generalizability of ML methods remain limited.

## Abstract

Medication errors remain a substantial public health concern, and existing measures, such as workforce training, have achieved only partial success. Advances in data availability and computational methods have led to increasing use of machine learning (ML) to support medication safety. This scoping review synthesizes and categorizes ML-based approaches to medication error detection or prediction.

Following Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR) guidelines, PubMed, Embase, and Web of Science were searched for studies published between 2015 and April 2025. Two reviewers independently performed study selection using predefined eligibility criteria, and data extraction followed a structured extraction framework.

Twenty-two studies met the inclusion criteria. Two dominant ML pipelines were identified. Most studies focused on prescription-related errors, relying on structured clinical data and tree-based models. A smaller group addressed medication-administration errors using unstructured multimodal data, such as images or video, analyzed with neural networks and multi-stage detection pipelines.

ML shows substantial potential for medication error detection, particularly in prescription-focused workflows that align well with existing clinical processes. However, the evidence remains fragmented, with limited generalizability, inconsistent labeling, and scarce real-world evaluation. No studies addressed medication errors in clinical research settings, such as clinical trials, despite their distinct workflows and safety implications.

Advancing ML-based medication error detection will require high-quality multicenter datasets, rigorous and transparent validation, and deeper exploration of underused data modalities, including free text.