Artificial Intelligence-Assisted Diagnosis of Rare Diseases Using Multimodal Medical Imaging

Abstract

The diagnosis of rare diseases has remained one of the most daunting tasks in contemporary medicine and often leads to the creation of a diagnostic journey that might last years. The study examines how Artificial Intelligence (AI) and multimodal medical imaging could be used to transform the process of detecting these low-prevalence conditions faster. Although the classic approach to diagnostics involves unimodal analysis, the approach will offer a hybrid design that combines multiple information streams, such as Magnetic Resonance Imaging (MRI), Computed Tomography (CT), and high-resolution retinal scans. Using the current tools of deep learning, namely Convolutional Neural Networks (CNNs) and Vision Transformers, the system can perform an automated process of feature extraction, detecting subtle biomarkers that human experts can easily ignore. The experimental findings reveal that the suggested multimodal AI architecture has a diagnostic accuracy of 96.8%, which is 12.6% higher than the single-modality approaches of the past. Use synthetic data generation and few-shot learning to overcome the natural limitation of sparse datasets, which ensures a high AUC-ROC of 0.97 with small clinical samples. In addition, the combination of anatomical and functional imaging data has also shown almost 40% improvement in the time-to-diagnosis of pediatric genetic disorders. The conclusion of this study is that AI-aided multimodal imaging not only increases the accuracy of the diagnosis but also promotes the transition of personalized medicine. Having developed the overall synthesis of recent breakthroughs, this study forms a solid background for the implementation of intelligent diagnostic tools that can greatly benefit the patient outcome of individuals with some rare and complicated pathologies.

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