A Systematic Review on Data Science and Artificial Intelligence Applications in Healthcare Sector

Abstract

This systematic review focuses on the emerging domains of data science and AI in healthcare; the research question guiding the study was to establish the trends and gaps in literature. Over the past few decades, with the advancement of technology, various application areas of Artificial Intelligence in healthcare delivery has seen a drastic improvement from diagnosis to treatment and patient monitoring. Thus, the search included all the major databases in AI containing universal machine learning, natural language, and predictive analysis techniques. This review also divides these applications into several fields which include disease risk prognosis, medical image analyzing, and personalized medicine stressing that they will bring about improvements on the clinical diagnosis and patients’ care. Concerning the critical challenges it provides a response regarding such issues as ethical questionnaires, technical constraints, and prospects of spreading its application in spheres of healthcare. This issue is particularly relevant because ethical issues such as the protection of the patient’s identity, fairness in algorithm design, and the lack or appropriate regulatory measures become apparent. Another set of barriers relates to technical issues, including data conversion and the possibilities of using AI models in cancer diagnosis. Nevertheless, based on the aforementioned challenges, the review suggests potential directions of future studies and practical advancements that the authors and the healthcare community in general should consider in order to unlock the potential of data science and AI for improving worldwide healthcare systems.