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The adoption of artificial intelligence in healthcare holds great promise for improving clinical decision-making and patient safety, optimizing administrative processes, and ultimately enhancing patient outcomes. However, the successful and safe integration of AI technologies into clinical practice is hindered by several challenges and concerns. This study provides a systematic literature review to identify and analyze the key barriers with the aim of facilitating the successful implementation of AI-driven technologies in healthcare. Searches were conducted across Web of Science, PubMed, and Scopus, yielding 92 relevant studies. From these, 16 key barriers were identified, including data quality and bias, infrastructure limitations, financial constraints, workflow misalignment, inadequate training, and issues of transparency and accountability. These challenges were subsequently categorized into three clusters using the Human-Organization-Technology (HOT) framework. Human-related challenges include insufficient training, resistance from healthcare providers, and the potential for increased workload. Technology-related challenges concern issues of accuracy, explainability, and the lack of contextual adaptability. Organizational challenges involve infrastructure limitations, inadequate leadership support, and regulatory constraints. To address these barriers, this study proposes a system-level conceptual framework designed to guide both the evaluation and the effective integration of AI into healthcare systems. The framework adopts a sequential structure comprising three main phases: assessment, implementation, and continuous monitoring. Therefore, it ensures that integration is both systematic and sustainable. By linking the identified barriers to targeted strategies across these phases, the framework provides a practical roadmap for overcoming challenges and advancing the safe and effective adoption of AI in healthcare. • Reviews challenges healthcare providers face when adopting AI in clinical settings. • Categorizes adoption barriers using the Human-Organization-Technology (HOT) framework. • Proposes a system-level conceptual framework to enhance patient safety and quality of care. • Addresses safety, workflow, ethical, and resource-related concerns related to AI adoption.
Abdelwanis et al. (Wed,) studied this question.
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