AI-POWERED DISEASE DIAGNOSIS: DEVELOPING AI ALGORITHMS FOR ACCURATE DISEASE DIAGNOSIS USING MEDICAL IMAGING, ELECTRONIC HEALTH RECORDS, AND GENOMIC DATA

Abstract

Precise and timely diagnosis of disease is still one of the major problems in contemporary medicine, especially in multifaceted, undercrossing, or incipient conditions. Although artificial intelligence (AI) demonstrated striking potential in autonomous areas like medical imaging and electronic health records (EHRs), its full diagnostic potential is in integrating heterogeneous healthcare sources of information. This study develops and validates a robust AI-based diagnostic platform that integrates medical imaging, EHRs, and genomic data to increase diagnostic accuracy and interpretability. A multimodal AI platform was created using CNNs for medical imaging, gradient boosting (XGBoost) for EHR, and DNNs with autoencoders for genomics analysis. Publicly available datasets of 96,000 radiographs, 23,500 structured and unstructured EHR records, and 9,440 genomic profiles were used. Normalization, data augmentation, feature selection, and dimensionality reduction were conducted at the stage of data preprocessing. Late-fusion was used to perform the aggregation of modality-specific outputs into a single diagnostic decision layer. Assessment was performed using accuracy, F1-score, AUC-ROC, and explainability tools like Grad-CAM and SHAP. External validation and comparison with human experts were conducted to assess clinical utility. The multimodal fusion model had diagnostic accuracy of 94.8%, better than single-modality models (90.4% for imaging, 87.1% for EHR, 84.5% for genomics) and better than human clinicians on a test subset by 5.6%. The combined model demonstrated exceptional generalizability on external data and demonstrated improved ability to identify cancers and autoimmune diseases in their early stages. Explainability analysis validated the clinical relevance of features underlying AI predictions, and human-AI agreement was as high as 91.5% on 1,000 test cases. Combining medical imaging, EHRs, and genomic data with AI greatly enhances diagnostic precision, sensitivity, and interpretability. This work demonstrates the synergistic power of multimodal learning for healthcare and outlines an extensible architecture for clinical decision support systems. Expanded use of this model type, coupled with ethical governance, clinician education, and infrastructure investment, can revolutionize diagnostic work and propel precision medicine.

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