Abstract

ARTIFICIAL INTELLIGENCE & MACHINE LEARNING: A NEW ERA IN CANCER RESEARCH, DETECTION AND THERAPY

Kavitha CH. N.*, Rajaneesh P., Pavan Prakash Reddy S.S., Vidya Sagar S. and Chandini R. B.

ABSTRACT

Artificial intelligence (AI) is the intelligence displayed by machines that are created by humans and Machine learning (ML) refers to computers' capacity to learn from data on their own. Artificial intelligence and machine learning techniques are breaking into biomedical research and health care, which importantly includes cancer research and oncology, where the potential applications are vast. These include detection and diagnosis of cancer, subtype classification, optimization of cancer treatment and identification of new therapeutic targets in drug discovery. AI, machine learning, and deep learning can all be used to improve cancer care and patient outcomes. While big data used to train machine learning models may already exist, leveraging this opportunity to realize the full promise of artificial intelligence in both the cancer research space and the clinical space will first require significant obstacles to be surmounted. Integration of AI technology in cancer care could improve the accuracy and speed of diagnosis, aid clinical decision-making, and lead to better health outcomes. Many studies in novel cancer research are focused on implementation of artificial intelligence and machine learning techniques while ensuring standards are maintained so as to transform cancer diagnosis and the prognosis and treatment of patients with cancer and to drive biological discovery. With the advent of personalized cancer medicine, the portfolio of anti-tumor agents and companion diagnostic assays has experienced an unprecedented expansion, and a plethora of clinical trials are exploring the most effective treatments for the best-matching patients. In this context, data-driven approaches for optimizing clinical matching, reducing the cost of diagnostic testing, and improving the prediction of clinical phenotypes hold great promise for enhancing the clinical management of patients and maximize the value of precision oncology. In this current review, an attempt was made to highlight the significance of artificial intelligence and machine learning in cancer therapy as, in current scenario, there is a tremendous increase in computer engineering and use of computational approaches in cancer prediction and therapy outcome evaluation.

Keywords: Artificial intelligence, Machine learning, Neural Networks, Cancer detection, Cancer therapy.