AI-Enhanced Predictive Imaging in Precision Medicine: Advancing Diagnostic Accuracy and Personalized Treatment

Abstract

Artificial intelligence (AI) is changing how cancer is diagnosed, predicted, and treated, opening up new approaches to make cancer care more individualized. Rather than offering a broad but superficial overview, this review focuses on four cancers—lung, breast, brain (gliomas), and colorectal—for which AI was shown to be useful in the clinic. AI algorithms, specifically those using convolutional neural networks (CNNs), can enhance early diagnosis while realizing molecular profiling and treatment response assessment through quantitative imaging evaluations. Radiomics together with radiogenomics improves treatment accuracy through the assessment of imaging characteristics that help identify targeted genomic therapies. AI technologies can enhance tumor segmentation precision, stage determination, and target outlining capabilities, which enable adaptive radiation therapy. Initiatives that merge AI with images, clinical results, and genetic science information can deliver thorough personalized assessments that enhance treatment planning decisions. However, AI technology needs to overcome data quality issues, interpretability limitations, and generalizability challenges and needs to meet regulatory compliance requirements before achieving safe and fair implementation. The next phase of development will focus on federated learning to safeguard privacy while institutions collaborate, explainable AI to build transparent systems, and the fusion of diverse data types for comprehensive patient identification and real-time medical decision support through establishing digital twins for individualized treatment assessments. Precision oncology will be transformed by maturing innovations in predictive imaging that allow better timing of diagnosis while providing customized treatments to achieve improved medical results.