Implementation of AI and ML for Oncology with an Analysis of Biotechnology, Nanotechnology & POC

Abstract

In recent years, the integration of artificial intelligence (AI) and machine learning (ML) has significantly advanced cancer research and treatment. By utilizing deep learning (DL) and natural language processing (NLP), researchers are uncovering new patterns in large datasets, leading to more accurate diagnoses and personalized treatment plans. The synergy of nanotechnology further augments these advancements by improving drug delivery systems and enabling non-invasive tumor characterization. Additionally, point-of-care care diagnostics and global healthcare solutions are examined as vital points for improving cancer care, especially in underprivileged regions. The paper also addresses the ethical limitations that have arisen from applying artificial intelligence and machine learning to oncology, highlighting the need for careful implementation and regulation to maximize benefits while minimizing risks. This paper explores the transformative role of AI and ML in enhancing clinical outcomes, particularly through genomics, proteomics, and bioinformatics.