Multimodal artificial intelligence in medical biotechnology: Integrating genomics, imaging, and clinical data for precision therapeutics
TL;DR
Overall, multimodal AI represents an important computational framework for integrating diverse biomedical data within precision therapeutics and will depend not only on methodological innovation but also on the development of robust validation frameworks, interoperable data ecosystems, equitable datasets, and clinically meaningful implementation studies capable of demonstrating improvements in patient outcomes.
Abstract
The increasing availability of heterogeneous biomedical data, including genomics, transcriptomics, proteomics, metabolomics, medical imaging, electronic health records, digital pathology, and wearable sensor data, has accelerated the development of multimodal artificial intelligence (AI) approaches for precision therapeutics. By integrating complementary information across multiple data modalities, multimodal AI aims to improve disease characterization, risk stratification, biomarker discovery, therapeutic target identification, and individualized treatment selection beyond what can be achieved using single‐modality analyses. This narrative review critically examines the current landscape of multimodal AI in medical biotechnology and precision therapeutics. Major biomedical data modalities, multimodal integration strategies, and emerging computational architectures are discussed, including deep‐learning frameworks, graph neural networks, biomedical foundation models, and multimodal large language models. Particular attention is given to the comparative strengths and limitations of early, late, and hybrid fusion approaches, as well as challenges associated with missing modalities, data heterogeneity, class imbalance, model calibration, and external validation. The review further evaluates representative applications in oncology, rare diseases, cardiovascular medicine, infectious diseases, neurodegenerative disorders, drug discovery, and companion diagnostics. Clinical translation remains constrained by limited prospective validation, inconsistent reporting standards, interoperability barriers, regulatory uncertainty, and concerns regarding fairness, privacy, and explainability. Emerging approaches such as federated learning and foundation‐model‐based architectures may help address some of these limitations, although their real‐world performance and governance requirements remain under active investigation. Overall, multimodal AI represents an important computational framework for integrating diverse biomedical data within precision therapeutics. Future progress will depend not only on methodological innovation but also on the development of robust validation frameworks, interoperable data ecosystems, equitable datasets, and clinically meaningful implementation studies capable of demonstrating improvements in patient outcomes.