A Systematic Literature Review of Advancements, Challenges and Future Directions of AI And ML in Healthcare

Abstract

The most remarkable advancements in artificial intelligence (AI) plus machine learning (ML) technologies with integration addicted to healthcare systems face several challenges in delaying their full potential. The purpose of this systematic literature review is to deliver a broad analysis of existing AI and ML applications in healthcare, focusing on diagnostics, predictive analytics, personalized medicine, and administrative operations. The review identifies key innovations and practical benefits while also addressing significant limitations and ethical considerations based on data privacy with algorithm transparency and biases inside AI models. The methodology involved an extensive search of academic databases with Pub Med, IEEE Xplore, Scopus journals, and Web Science for using targeted keywords and Boolean operators to refine search results. Studies were included based on clear inclusion criteria to emphasize peer-reviewed articles published in English over the last five years. Data Extraction was conducted independently with two reviewers to ensure accuracy. The findings reveal considerable advancements in AI-driven diagnostic tools and predictive analytics. However, it highlights critical gaps, particularly in regulatory frameworks and interoperability with existing medical infrastructure. This review underscores the necessity for ethical and unbiased AI applications to give the proposed recommendations for future research and policy development. In this review, the analysis aims to guide healthcare experts and policymakers. Also, researchers are responsible for the incorporation of AI and ML technologies, optimizing patient outcomes and advancing the global quality of healthcare services.