Beyond Single Biomarkers: Multimodal Machine Learning for Precision Medicine Across Clinical Specialties
Multimodal Machine Learning in Precision Medicine
DOI:
https://doi.org/10.64949/kap5fa07Keywords:
Multimodal machine learning, precision medicine, artificial intelligence, electronic health records, clinical decision support, model validationAbstract
This article is a preprint and has not yet been peer-reviewed. Not for clinical use.
Precision medicine has traditionally relied on individual biomarkers to guide diagnosis, risk stratification, and treatment selection. However, most clinical phenotypes arise from complex interactions between molecular, imaging, physiological, behavioural, and environmental factors that cannot be captured by a single data source. Multimodal machine learning offers a framework for integrating heterogeneous data streams, including electronic health records, medical imaging, genomics, clinical text, biosignals, and wearable-device outputs, to generate more comprehensive patient-level predictions. This review examines the role of multimodal machine learning in advancing precision medicine across oncology, cardiology, neurology, critical care, rare disease, and cardiometabolic medicine. Common data-fusion strategies, including early, intermediate, and late fusion, are summarised. In addition, emerging approaches such as transformer-based architectures and graph neural networks are highlighted. Clinically relevant applications include treatment-response prediction, risk stratification, early detection of deterioration, diagnostic prioritisation, and remote monitoring. Despite encouraging performance in retrospective studies, translation into routine care remains limited by challenges in external validation, calibration, interpretability, bias, data quality, privacy, and workflow integration. Clinicians should therefore evaluate multimodal AI tools not only by discrimination metrics such as area under the curve, but also by clinical utility, generalisability, uncertainty, fairness, and actionability. Established frameworks, including TRIPOD+AI, DECIDE-AI, CONSORT-AI, SPIRIT-AI, and CLAIM, can support more transparent reporting and evaluation. Ultimately, multimodal machine learning should be viewed not as autonomous decision-making, but as a potential clinical decision-support approach whose value depends on whether it improves decisions that matter to patients.
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