Multimodal AI in precision medicine: linking omics, imaging and clinical decisions.

Abstract

Precision medicine is shifting from a single-biomarker paradigm toward multimodal integration of molecular, imaging, and clinical data, with artificial intelligence (AI) serving as a key enabling technology. Multimodal imaging, computational pathology, spatial omics and liquid biopsy have all become more adept at capturing disease and biological variation, and foundation models, especially vision-language models, are now creating a common language between histology, molecular and biomedical text. AI-powered pipelines are also improving data quality upstream, which ultimately means that multimodal inference can be applied to tasks of real clinical importance: biomarker screening, modelling treatment outcomes, even autonomous coordination among diagnostic systems, rather than just retrospective prediction. But none of this means more modalities necessarily translate into more value. Additional data may introduce redundancy, technical artefacts, institutional bias, and missingness. The future of multimodal AI should therefore emphasize sufficiency rather than maximalism, with systems designed to accommodate incomplete or asynchronous inputs, quantify uncertainty, and demonstrate added value over established clinical standards. Ultimately, clinical utility will depend on external and prospective validation showing improvements in calibration, efficiency, net clinical benefit, and patient outcomes. The promise of AI-enabled multimodal precision medicine lies not in combining every available data stream, but in determining which molecular, morphological, and clinical information is necessary, complementary, and actionable for an individual patient at a specific decision point.