MULTIMODAL GENERATIVE AI FOR PRECISION MEDICINE: INTEGRATING MEDICAL IMAGING, CLINICAL RECORDS AND GENOMIC DATA

Abstract

Precision medicine aims to move healthcare from generalized treatment strategies toward patient-specific diagnosis, risk assessment, prognosis, and therapeutic selection. However, clinical decision-making depends on heterogeneous information distributed across medical images, electronic health records, laboratory measurements, genomic profiles, pathology reports, medications, and longitudinal clinical histories. Conventional artificial intelligence systems often process these modalities independently, limiting their ability to capture clinically meaningful relationships across different sources of patient information. This paper proposes a multimodal generative artificial intelligence framework that integrates medical imaging, clinical records, and genomic data within a unified precision-medicine architecture. The proposed framework employs modality-specific encoders for imaging, structured and unstructured clinical information, and genomic features, followed by a multimodal fusion mechanism and a generative reasoning layer. A large multimodal model is used to generate patient-level clinical summaries, risk assessments, biomarker interpretations, and candidate treatment insights while preserving links to the underlying evidence. The framework incorporates retrieval-augmented generation, uncertainty estimation, explainability, privacy protection, and human-in-the-loop validation to address the risks associated with generative AI in healthcare. A comprehensive evaluation methodology is proposed using diagnostic discrimination, calibration, sensitivity, specificity, F1-score, survival prediction, treatment-response prediction, explanation fidelity, hallucination rate, fairness, and robustness. The paper discusses challenges including missing modalities, heterogeneous data formats, genomic privacy, dataset shift, demographic bias, hallucination, clinical validation, and regulatory requirements. The proposed framework provides a research foundation for developing trustworthy multimodal generative AI systems capable of synthesizing complementary biomedical evidence to support individualized clinical decision-making.