Machine Learning in Biomedical Engineering and Medical Imaging: Algorithms, Validation, and Clinical Translation

Abstract

Machine learning has become a central pillar of modern biomedical engineering, reshaping how medical images, biosignals, and wearable data are acquired, processed, and translated into clinical decisions. This review synthesises the current state of machine learning in biomedical engineering, with particular emphasis on medical imaging, from algorithmic foundations to regulatory clearance and clinical adoption. The analysis is evidence-based and integrative, drawing on peer-reviewed literature, regulatory registries, and recent empirical evaluations. The results show that the application of machine learning in biomedicine has expanded rapidly: the U.S. Food and Drug Administration authorized a record number of artificial intelligence-enabled medical devices in recent years, with cumulative authorizations exceeding 1,400 devices by the end of 2025, and with radiology accounting for roughly three-quarters of all clearances. Within medical imaging, deep convolutional networks and, increasingly, transformer-based architectures have achieved diagnostic performance comparable to that of expert clinicians across a range of modalities, including chest radiography, computed tomography, and magnetic resonance imaging. At the same time, the translation of these algorithms into routine practice remains uneven, constrained by limited data granularity, imperfect generalizability across populations and devices, fragmented regulatory pathways, and the practical demands of clinical workflow integration. The review identifies the principal technical, data, regulatory, and human-factor barriers to clinical translation, and proposes a set of recommendations for biomedical engineers, clinicians, and regulators aimed at closing the gap between algorithmic performance and real-world benefit. The findings underscore that successful integration of machine learning in biomedical engineering depends less on raw model accuracy than on robust validation, transparent reporting, interdisciplinary collaboration, and the deliberate design of systems that support, rather than replace, clinical judgement.