Automated Machine Learning in medical research: A systematic literature mapping study

Machine Learning (ML) techniques have become valuable tools in healthcare for tasks such as disease diagnosis, treatment planning, and clinical decision-making. However, their application often requires specialized expertise and considerable development time. Automated Machine Learning (AutoML) has emerged to address these challenges by automating key steps of the ML pipeline, thereby enhancing the efficiency and accessibility of ML integration into clinical workflows. This paper presents a systematic literature mapping following a structured protocol across multiple academic databases. A total of 244 studies published between 2016 and 2025 were analyzed from 171 distinct sources. Most studies employed AutoML for diagnosis prediction (52.8%) and prognosis prediction (31.9%), primarily using tabular (43.4%) and image (31.5%) data. Classification tasks dominated (81.1%), followed by regression tasks (9.4%). Model selection emerged as the primary challenge (25.3%) among the studies not utilizing managed AutoML tools, while data preprocessing remained critical (13.7%) even when using managed services. Although Explainable AI (XAI) was incorporated in only 30.7% of the studies, its adoption showed a notable increase in 2024. These findings indicate that while the application of AutoML in medicine is growing, its black-box nature continues to limit its adoption in interpretability-critical domains. The integration of XAI techniques with AutoML is an emerging trend that seeks to address this limitation. However, further research is necessary to evaluate the clinical impact of these combined approaches and to enhance trust in AutoML-driven decision support systems.