Overcoming Barriers to Artificial Intelligence Adoption in Healthcare: A Review of Applications, Challenges and Emerging Solutions

Artificial Intelligence (AI) refers to systems designed to mimic human intelligence, enabling machines to perform tasks that typically require reasoning, learning, and decision-making. Today, AI is integrated into everyday life through technologies such as virtual assistants (e.g., Siri, Alexa, and Google Assistant), autonomous transportation systems, aviation technologies, gaming, and digital platforms. While AI has transformed multiple industries, healthcare has emerged as one of its most impactful domains, significantly enhancing medical imaging, disease diagnosis, treatment planning, and patient management. However, the clinical adoption of AI has been constrained by several persistent barriers, including limited computational resources, scarcity of high-quality annotated datasets, lack of interpretability, privacy concerns, regulatory ambiguity, and integration challenges within existing healthcare infrastructures. The emergence and rapid advancement of modern Deep Learning (DL) techniques helped address many of these challenges by enabling AI systems to analyze complex and high-dimensional healthcare data more effectively. Consequently, AI is increasingly leveraged to overcome traditional healthcare system constraints, improving diagnostic precision, workflow efficiency, and patient outcomes. Despite significant progress, unresolved technical, ethical,regulatory and organizational challenges necessitate a comprehensive evaluation of AI’s role in healthcare. This review discusses the evolution and applications of AI in healthcare, examines the limitations of traditional healthcare systems, explores how AI addresses these challenges, identifies current limitations of AI based approaches, and presents potential solutions to guide future advancements in AI applications within healthcare systems.