Patient Specific

Patient-specific modeling in medicine aims to tailor diagnoses, treatments, and prognoses to individual patient characteristics, improving healthcare outcomes. Current research focuses on developing AI-powered models, often employing deep learning architectures like convolutional neural networks (CNNs), recurrent neural networks (RNNs), and transformers, to analyze diverse data modalities (e.g., medical images, genomic data, electronic health records) and generate personalized predictions. This approach is transforming various medical fields, from surgical planning and drug delivery optimization to disease diagnosis and risk stratification, by leveraging the unique characteristics of each patient for more accurate and effective interventions.