Multimodal AI Integration in Healthcare Information Systems: Unifying Imaging, EHR, and Wearable Data for Early Diagnosis

Abstract

Multimodal artificial intelligence has found a significant direction in healthcare information systems since contemporary diagnosis is conditioned by the combination of several evidence streams as opposed to a single clinical signal. The medical imaging also offers anatomical and pathological data, electronic health records it offers longitudinal clinical history, and continuous physiological monitoring can be located out of the hospital with the help of wearable devices. The study project is a multimodal AI-driven healthcare information architecture that combines imaging, EHR, lab, clinical-note, and wearable-sensor data to diagnose early diseases. The suggested system works with the standardized data acquisition, interoperability protocols, preprocessing, multimodal fusion, and multimodal feature extraction, followed by clinical decision support, explainability, and ongoing monitoring to produce the early diagnostic risk score. The proposed architecture is assessed based on a simulated experimental setup with the realistic performance metrics such as accuracy, sensitivity, specificity, and F1-score as well as AUC, latency, missing-data robustness, and interpretability of the outcomes by clinicians. The findings indicate that multimodal fusion has the potential to enhance the performance in early diagnosis as opposed to single-modality systems since the sensor combines structural, temporal, behavioral, and clinical context. Significant implementation issues outlined in the study, among them being data heterogeneity, interoperability gaps, privacy protection, model bias, alert fatigue and prospective clinical validation. The conclusion of the paper is that multimodal AI must be used as a clinician-focused decision-support layer as opposed to an autonomous diagnostic solution.