AI in Cancer Prognosis, Prediction, and Treatment Selection
Cancer is one of the major causes of morbidity and mortality in people throughout the world; it accounts for roughly 19%. New cases of 3 million and death of 10 million per annum. Despite the enhancements in diagnosing cancer, predicting it, and curing it, individualized, and evidence-based medicine is still a problem. AI and especially ML present solutions enhancing the accuracy of health services and oncology patients’ results. AI and ML have significant uses in the cancer field such as risk evaluation, diagnosis, prognosis of patients’ outcome or a choice of therapy. Some of these technologies have been carried out to predict different types of cancer with higher accuracy than clinicians and can be used to improve the diagnosing, prognosis, and living of different illness patients. This paper synthesizes a review of the state-of-the-art AI and ML in cancer prediction and cancer care delivery, challenges, and future directions.
The utility of AI and ML in oncology is evident in the field as risk assessment, early diagnosis, prognosis and treatment choice possibilities. The AI approaches are used to work on multiple and massive data including the history of patients with cancer, scans, and genetically to come up with a higher result in the prediction of cancer. For instance, the Google DeepMind AI systems are already surpassing the efficiency of medical specialists in the detection of breast cancer; at the same time, other AI models demonstrate high efficacy in diagnosing prostate and lung cancer. Such factors as disease severity, patient age, and physical condition may also be analyzed by AI, which leads to developing individual treatment regimens and enhancing the patient’s quality of life. However, there are challenges which include data privacy, data fragmentation, and the nature of AI systems that can hamper their effectiveness in the healthcare sector.
Research has shown that AI and ML can be used in accurately diagnosing different forms of cancer such as breast, prostate, lung, liver and colorectal cancers. These technologies can be used to predict pathology profiles, image analysis and even genetic markers and give more accurate diagnosis and treatment management. Compared with the traditional approach, it proved that the AI models have better outcomes in risk assessment and early diagnosis. The decision to incorporate AI in clinical practice should help in clinical decision making, better resource management and availability of better treatment interventions. Nevertheless, it should be noted that some issues should be solved for the further development of AI in oncology to become more prevalent in the modern world.
Author: Neer Patel
Zhang B, Shi H, Wang H. Artificial intelligence across oncology specialties: current applications and emerging tools. BMJ Oncology. 2023;24(4):1099-1108. doi:10.31557/APJCP.2023.24.4.1099. Accessed July 31, 2024. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10312208/
