Artificial Intelligence In Predictive Analytics For Next-Generation Cancer Treatment: A Systematic Literature Review Of Healthcare Innovations In The USA

Abstract

The rapid advancement of predictive analytics, biomarker-driven precision medicine, genomic sequencing, nanotechnology, and immunotherapy has significantly transformed cancer diagnosis, treatment selection, and therapeutic outcomes. This systematic literature review, based on the analysis of 147 peer-reviewed studies, explores the role of these emerging technologies in reshaping oncology and evaluates the barriers limiting their widespread adoption. The study followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines to ensure a systematic, transparent, and rigorous review process. The findings indicate that machine learning-based predictive models are enhancing early cancer detection, prognosis, and treatment optimization, with multi-modal AI-driven approaches improving diagnostic accuracy by 15-20% compared to conventional methods. The review further highlights the growing importance of biomarker-driven liquid biopsy techniques, with circulating tumor DNA (ctDNA) and microRNA (miRNA) biomarkers proving highly effective in real-time disease monitoring, recurrence prediction, and treatment response assessment. Additionally, genomic sequencing, particularly whole-exome sequencing (WES) and whole-genome sequencing (WGS), has improved the identification of oncogenic mutations, therapy response prediction, and personalized treatment approaches, despite its high cost and accessibility limitations. The study also emphasizes the critical role of nanotechnology in cancer drug delivery, with liposomal formulations, polymeric nanoparticles, and gold-based drug carriers demonstrating significant improvements in chemotherapy bioavailability, tumor selectivity, and reduced systemic toxicity. Immunotherapy has emerged as a revolutionary cancer treatment modality, with immune checkpoint inhibitors (ICIs), CAR-T cell therapy, and tumor-infiltrating lymphocyte (TIL) therapy achieving unprecedented response rates in hematologic and solid tumors, yet remaining financially and logistically inaccessible for many patients. The economic burden of biomarker-driven therapies, the high cost of genomic sequencing, and the computational challenges of AI-based predictive analytics continue to limit equitable access to precision medicine.