Towards revolutionizing precision healthcare: A systematic literature review of artificial intelligence methods in precision medicine

Abstract

In the realm of medicine, artificial intelligence (AI) has emerged as a transformative force, harnessing the power to convert raw data into meaningful insights. Rather than supplanting the discernment of physicians, AI serves as an unprecedented enabler, equipping them with unimaginable tools. Its far-reaching applications encompass drug discovery, disease diagnosis, prognosis, treatment optimization, and outcome prediction. This technological revolution owes much to the prowess of machine learning algorithms, which adeptly process multifaceted data. Consequently, AI is poised to become an integral pillar of digital health systems, shaping and bolstering the realm of personalized medicine. The current landscape is abuzz with AI’s exponential growth, fueling a surge of research ventures aimed at enhancing medical practices. By delving into the realm of precision medicine, this paper endeavors to scrutinize and evaluate recent advancements in healthcare pertaining to the utilization of machine learning (ML) and deep learning (DL) algorithms. This systematic review comprehensively encompasses previously published works, dissecting key concepts, innovations, significant contributions, and pivotal enabling techniques. Aspiring to equip readers with a profound understanding and invaluable insights, this paper proves indispensable to those dedicated to exploring the state-of-the-art and contributing to future literature in this domain.

Keywords

• Precision medicine
• Artificial intelligence
• Machine learning
• Deep learning
• Health care