Artificial Intelligence-Driven Theranostics: Integrating Machine Learning For Precision Diagnosis And Personalized Therapy

Abstract

Theranostics links disease characterization with treatment selection and response assessment, creating a framework in which diagnostic information directly informs individualized therapy. Recent advances in artificial intelligence (AI), machine learning (ML), deep learning, radiomics, radiogenomics, and multimodal data integration are expanding this concept from target verification toward quantitative prediction of diagnosis, prognosis, therapeutic response, dose, and toxicity. This review examines how AI can connect imaging, electronic health records, genomics, transcriptomics, proteomics, pharmacogenomics, and treatment response data across a unified theranostic workflow. Evidence from ophthalmology, dermatology, pathology, cardiology, oncology, drug response prediction, radiopharmaceutical therapy, and personalized dosing demonstrates that ML models can extract clinically useful patterns from complex datasets and support patient level stratification. In nuclear medicine, AI assisted segmentation, quantitative imaging, and dosimetry may shorten analysis time and facilitate patient-specific radiopharmaceutical therapy. In precision oncology, radiomics and radiogenomics can associate imaging phenotypes with molecular characteristics and treatment outcomes, while computational drug response models can prioritize therapies for molecularly defined disease. However, clinical translation remains constrained by dataset shift, limited external validation, data leakage, class imbalance, algorithmic bias, interpretability, privacy, interoperability, regulatory uncertainty, and insufficient prospective evidence. Future progress will require multicenter datasets, standardized data pipelines, transparent reporting, calibrated uncertainty, explainable models, federated or privacy preserving learning, prospective clinical trials, and continuous post deployment monitoring. AI driven theranostics is therefore best viewed as a decision support ecosystem in which computational prediction complements, rather than replaces, clinical expertise and experimentally validated treatment pathways.