Machine Learning and AI in Cancer Prognosis, Prediction, and Treatment Selection: A Critical Approach

• Published in: Journal of multidisciplinary healthcare Vol. 16; pp. 1779 - 1791
• Main Authors: Zhang, Bo, Shi, Huiping, Wang, Hongtao
• Format: Journal Article
• Language: English
• Published: New Zealand Dove Medical Press Limited 01.01.2023; Dove; Dove Medical Press
• Subjects: Algorithms; Artificial intelligence; cancer diagnosis; Cancer patients; cancer-related mortality; Computational linguistics; Development and progression; Health aspects; Health care reform; Language processing; Liver cancer; Machine learning; Medical care; Natural language interfaces; Quality management; Review; Risk assessment; Technology application; Training; treatment selection; Type 2 diabetes; China; cancer-related mortality; cancer diagnosis; treatment selection; machine learning; artificial intelligence
• ISSN: 1178-2390; 1178-2390
• DOI: 10.2147/JMDH.S410301

Cancer is a leading cause of morbidity and mortality worldwide. While progress has been made in the diagnosis, prognosis, and treatment of cancer patients, individualized and data-driven care remains a challenge. Artificial intelligence (AI), which is used to predict and automate many cancers, has emerged as a promising option for improving healthcare accuracy and patient outcomes. AI applications in oncology include risk assessment, early diagnosis, patient prognosis estimation, and treatment selection based on deep knowledge. Machine learning (ML), a subset of AI that enables computers to learn from training data, has been highly effective at predicting various types of cancer, including breast, brain, lung, liver, and prostate cancer. In fact, AI and ML have demonstrated greater accuracy in predicting cancer than clinicians. These technologies also have the potential to improve the diagnosis, prognosis, and quality of life of patients with various illnesses, not just cancer. Therefore, it is important to improve current AI and ML technologies and to develop new programs to benefit patients. This article examines the use of AI and ML algorithms in cancer prediction, including their current applications, limitations, and future prospects.