MINI REVIEW article
Front. Artif. Intell.
Sec. Medicine and Public Health
This article is part of the Research TopicUnlocking New Frontiers in Clinical Lab: AI driven Risk Stratification and Predictive AnalyticsView all 3 articles
Advancing Precision Medicine with Multi-Modal AI: Clinical Validation Across Genomics, Imaging, and EHR Data
Provisionally accepted
- 1Integral University, Lucknow, India
- 2Yeungnam University, Gyeongsan-si, Republic of Korea
- 3University of Hail, Hail, Saudi Arabia
- 4Fondazione PTV Policlinico Tor Vergata, Rome, Italy
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Precision healthcare is increasingly oriented toward the development of therapeutic strategies that are as individualized as the patients receiving them. Central to this paradigm shift is artificial intelligence (AI)-enabled multi-modal data integration, which consolidates heterogeneous data streams—including genomic, transcriptomic, proteomic, imaging, environmental, and electronic health record (EHR) data into a unified analytical framework. This integrative approach enhances early disease detection, facilitates the discovery of clinically actionable biomarkers, and accelerates rational drug development, with particularly significant implications for oncology, neurology, and cardiovascular medicine. Advanced machine learning (ML) and deep learning (DL) algorithms are capable of extracting complex, non-linear associations across data modalities, thereby improving diagnostic precision, enabling robust risk stratification, and informing patient-specific therapeutic interventions. Furthermore, AI-driven applications in digital health, such as wearable biosensors and real-time physiological monitoring, allow for continuous, dynamic refinement of treatment plans. This review examines the transformative potential of multi-modal AI in precision medicine, with emphasis on its role in multi-omics data integration, predictive modeling, and clinical decision support. In parallel, it critically evaluates prevailing challenges, including data interoperability, algorithmic bias, and ethical considerations surrounding patient privacy. The synergistic convergence of AI and multi-modal data represents not merely a technological innovation but a fundamental redefinition of individualized healthcare delivery.
Keywords: artificial intelligence, multi-modal integration, data-driven medicine, personalized treatment, AI-driven clinical decision
Received: 11 Nov 2025; Accepted: 08 Dec 2025.
Copyright: © 2025 Khan, NAN, Khan, Guarnera and Akhtar. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Shahper Nazeer Khan
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