Challenges in Adopting AI-Based Tools in Healthcare: Insights into Clinicians' Perspectives

Artificial intelligence (AI) is rapidly becoming a cornerstone of modern healthcare, offering new ways to improve patient outcomes, ease clinical workloads, and enhance the efficiency of health systems. This review takes a closer look at how AI is being integrated into real-world care— focusing not only on the technology itself but also on the human, institutional, and ethical challenges that shape its adoption. Drawing from a wide body of literature, we examine the evolving roles of machine learning, predictive analytics, and clinical decision support tools, while unpacking the barriers that continue to slow their uptake. Among the most pressing challenges are limited trust in AI among clinicians, fears about professional displacement, gaps in training, and resistance within care teams. At an institutional level, concerns around data governance, privacy, and regulatory fragmentation further complicate the path forward. Yet, there are encouraging signs: initiatives that promote AI literacy, foster transparency, and prioritize patient safety are beginning to make a difference.