Introduction:
Healthcare systems are facing mounting challenges from aging populations, increasing patient demand, and chronic disease burdens. Conventional approaches are often insufficient, highlighting the need for innovative tools. Artificial intelligence (AI) has emerged as a transformative solution, capable of analyzing complex data, supporting clinical decisions, and enabling more personalized and efficient care.
Method:
This comprehensive review was conducted following adapted PRISMA guidelines to ensure systematic and transparent reporting. Comprehensive literature search was performed across multiple electronic databases including PubMed/midline, Scopus, Web of Science and ScienceDirect from January 2019 to September 2025. search terms included combinations of “artificial intelligence”, “machine learning”,” healthcare”, “clinical applications”, “diagnostic accuracy”, and related medical informatics terms. Studies were included if they reported on AI applications in healthcare settings, demonstrated clinical outcomes, and were published in peer reviewed journal. Two independent reviewers conducted study selection and data extraction.
Results:
AI applications are expanding across diagnostics, telehealth, personalized medicine, robotic procedures, triage, patient monitoring, research, and administrative support. Studies demonstrate that AI improves diagnostic accuracy in radiology, pathology, and dermatology; streamlines triage and telehealth services; and integrates multimodal data for personalized treatment.
Additionally, AI supports robotic surgeries, patient education, and continuous monitoring, while also contributing to research efficiency and easing administrative tasks such as documentation, scheduling, and resource management. These findings suggest AI improves outcomes, optimizes resources, and reduces clinician workload.
Conclusion:
Despite its transformative potential, challenges such as bias, privacy concerns, lack of transparency, and limited real-world validation hinder full adoption. AI should be viewed as a supportive tool that augments, not replaces, clinician expertise. With rigorous validation, ethical governance, and interdisciplinary collaboration, AI can guide healthcare toward a proactive, precision-based, and patient-centered model.
Lorent Sijarina, MD, graduated from the University of Prishtina, Faculty of Medicine. He actively participates in international research collaborations and is mentored by Professor Patricia Tai, a world- renowned expert in oncology. With a strong commitment to evidence-based practice and global health, Lorent strives to contribute to the advancement of medicine through clinical research, innovation, and interdisciplinary collaboration. His goal is to help improve healthcare outcomes and promote scientific excellence on a global scale.
Copyright 2024 Mathews International LLC All Rights Reserved