aceMultimodal AI in Biomedicine: Pioneering the Future of Biomaterials, Diagnostics, and Personalized Healthcare
Multimodal artificial intelligence (AI) is driving a paradigm shift in modern biomedicine by seamlessly integrating heterogeneous data sources such as medical imaging, genomic information, and electronic health records. This review explores the transformative impact of multimodal AI across three pivotal areas: biomaterials science, medical diagnostics, and personalized medicine. In the realm of biomaterials, AI facilitates the design of patient-specific solutions tailored for tissue engineering, drug delivery, a…
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As of June 10 2025, this peer-reviewed review describes multimodal AI that integrates medical imaging, genomic information, electronic health records, and wearable data across biomaterials science, diagnostics and personalized medicine, citing AlphaFold for protein structure prediction and systems that combine imaging, molecular markers and clinical data
The potential impact is a shift toward more predictive, personalized and responsive healthcare through patient-specific tissue engineering and drug delivery solutions and earlier, more precise diagnosis, but the source notes that realization depends on resolving data security, regulatory, transparency, bias and access challenges that were still open at publication
- Review covers three pivotal areas: biomaterials science, medical diagnostics, and personalized medicine
- In biomaterials, AI supports tissue engineering, drug delivery and regenerative therapies with patient-specific solutions
- AlphaFold cited as tool that improved protein structure prediction for enhanced biological compatibility
- In diagnostics, systems combine imaging, molecular markers and clinical data to improve precision and early detection
- In personalized medicine, data from wearable technologies and continuous monitoring systems inform targeted therapeutic strategies
Multimodal AI integrating medical imaging, genomic information and electronic health records enables patient-specific biomaterial design and improves diagnostic precision and targeted therapy
The rundown
The review describes multimodal AI that seamlessly integrates heterogeneous sources such as medical imaging, genomic information, and electronic health records, plus wearable technologies and continuous monitoring systems
Specific mechanisms cited include AlphaFold for protein structure prediction to create biomaterials with enhanced biological compatibility, and synthesis of imaging, molecular markers and clinical data to support early disease detection and individualized health profiles
Sources
- Peer-reviewedNanomaterials2025-06-10
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