Tracing the Evolution of Artificial Intelligence and Machine Learning in Oncology: A Systematic Metadata Analysis

The integration of work done by AI in oncology has turned out to be a transforming power that will revolutionize diagnosis, therapeutic decisions, and treatment optimization. The article represents an extended meta-analysis of 6,382 research papers related to AI applications in oncology, obtained from Google Scholar and arXiv from 2016 to 2024. The obtained analysis has underlined the main trends, breakthroughs, and challenges regarding the implementation of AI and machine learning technologies in oncology. The most important observations will include substantial improvement in AI diagnostics, predictive therapeutic success modeling, and personalized medical interventions. This work has also pointed out the rise of deep learning, natural language processing, and computer vision that promise to help improve the detection and prognosis of cancers and further aid the care for such patients. Again, the review points to the gaps in current limitations: heterogeneous data, ethical issues, and strong validating frameworks. It reflects a general view of AI's transforming face in oncology, thus offering an outlook for such technology in redefining care for cancer and improving care for a patient in a few years.