Summary
This narrative review examines the convergence of expanded biomedical data sources—biobanks, electronic health records, medical imaging, biosensors and genomic/microbiome sequencing—and their integration via multimodal artificial intelligence to capture disease complexity. The authors map emerging clinical applications and systematically address the technical, modelling and privacy challenges that must be overcome for responsible implementation, though the review does not directly address agricultural or nutritional determinants of health.
UK applicability
Whilst the paper is discipline-specific to computational medicine, its findings on privacy-preserving AI architectures and regulatory frameworks for clinical data integration are potentially relevant to UK health systems and NHS digital infrastructure. However, the review does not address food systems, nutrition or farming practices, limiting direct applicability to Vitagri's core research domain.
Key measures
Not applicable — this is a review article rather than an empirical study; the paper surveys methodological approaches and application domains in multimodal biomedical AI
Outcomes reported
The review synthesises applications of multimodal AI in healthcare contexts including personalised medicine, digital clinical trials, remote monitoring, pandemic surveillance and virtual health assistants. It identifies technical, analytical, data governance and privacy challenges requiring resolution to realise the clinical potential of integrated biomedical AI systems.
Topic tags
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