What question did this study set out to answer?

To critically examine the current landscape, integration strategies, computational architectures, and clinical translation barriers of multimodal artificial intelligence in precision therapeutics.

August 9, 2026Precision Medical SciencesOpen Access

Multimodal artificial intelligence in medical biotechnology: Integrating genomics, imaging, and clinical data for precision therapeutics

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To critically examine the current landscape, integration strategies, computational architectures, and clinical translation barriers of multimodal artificial intelligence in precision therapeutics.
Narrative review synthesizing multimodal biomedical data inputs, including genomics, transcriptomics, proteomics, metabolomics, medical imaging, digital pathology, electronic health records, and wearable sensors.
Evaluated computational integration methods—including early, late, and hybrid fusion—alongside architectures such as deep learning, graph neural networks, foundation models, and multimodal large language models.
Multimodal AI architectures consistently improve disease characterization, risk stratification, target identification, and individualized therapy selection compared to single-modality models across oncology, cardiology, and rare diseases.
Clinical adoption is substantially hindered by missing data modalities, high heterogeneity, limited prospective validation, lack of reporting standards, regulatory uncertainties, and algorithmic fairness concerns.

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Implication

Narrative review highlights multimodal data integration across complex diseases, suggesting significant potential and persistent clinical hurdles for precision therapeutics.

Cite This Study

A 2026 study studied this question.

synapsesocial.com/papers/6a7cb4725de18cf3f8fab439 https://doi.org/10.1002/prm2.70047

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Implication

Narrative review highlights multimodal data integration across complex diseases, suggesting significant potential and persistent clinical hurdles for precision therapeutics.

Multimodal artificial intelligence in medical biotechnology: Integrating genomics, imaging, and clinical data for precision therapeutics

Key Points

Discussion

Implication

Cite This Study

Multimodal artificial intelligence in medical biotechnology: Integrating genomics, imaging, and clinical data for precision therapeutics

Key Points

Discussion

Implication

Cite This Study

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