Powered Disease Diagnosis: Developing AI Algorithms for Accurate Disease Diagnosis Using Medical Imaging, Electronic Health Records, and Genomic Data

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AI algorithms demonstrate improved diagnostic accuracy in diverse settings, highlighting the significance of multimodal learning.

The multimodal fusion model achieved a diagnostic accuracy of 94.8%, outperforming single-modality models significantly.
Assessment included metrics like accuracy and F1-score, compared against human experts for clinical utility.
Utilizing convolutional neural networks for imaging and XGBoost for EHR, robust data preprocessing enhanced model performance.
This approach indicates that integrating AI with multiple data sources can revolutionize precision medicine and support clinical decisions.

ADEDAMOLA et al. (2025) studied this question.

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View Full Paper

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AI algorithms demonstrate improved diagnostic accuracy in diverse settings, highlighting the significance of multimodal learning.

The multimodal fusion model achieved a diagnostic accuracy of 94.8%, outperforming single-modality models significantly.
Assessment included metrics like accuracy and F1-score, compared against human experts for clinical utility.
Utilizing convolutional neural networks for imaging and XGBoost for EHR, robust data preprocessing enhanced model performance.
This approach indicates that integrating AI with multiple data sources can revolutionize precision medicine and support clinical decisions.

ADEDAMOLA et al. (2025) studied this question.

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