AI Adoption Challenges with Digital Health

Digital health technologies, particularly Artificial Intelligence (AI), present substantial opportunities to address the many complex issues facing today’s healthcare industry by enhancing healthcare efficiency, clinical decision-making, and patient engagement. Despite the research and investments in AI, its use in healthcare has been exceedingly slow. This research employs a scoping literature review methodology to systematically examine and synthesize the existing body of knowledge concerning the adoption and integration of AI within digital health contexts with a focus on practical adoption challenges. The review process utilized a scoping approach involving four key methodological stages: establishing selection criteria, conducting iterative literature searches, performing structured data extraction, and synthesizing extracted findings. Initially, 71 articles were identified, subsequently narrowed through iterative keyword-based searches to a final set of 35 key references. This methodological rigor ensures a comprehensive yet focused examination of relevant literature, enhancing the robustness of the analysis and facilitating the identification of critical research gaps. Prior literature surveys in digital health and AI frequently provided broad perspectives on technological capabilities without systematically addressing practical adoption challenges or ethical and regulatory implications comprehensively. In contrast, this paper expands the current literature by explicitly exploring the practical impacts of AI on operational efficiencies in healthcare, its role in augmenting rather than replacing clinical decision-making processes, and the nuanced implications of AI-driven patient engagement technologies. This review addresses gaps highlighted in prior studies by integrating analysis of regulatory frameworks, ethical considerations around data privacy, consent, and algorithmic bias, which previous reviews have typically addressed separately or superficially. Key findings from this review underscore the transformative potential of AI across healthcare domains, including streamlined hospital operations, enhanced accuracy in diagnostics through imaging and clinical decision support systems, and improved patient interaction through AI-driven telemedicine and virtual assistants. Nevertheless, significant barriers remain, notably ethical challenges around patient data privacy, regulatory uncertainties, and human factors such as physician trust and acceptance of AI technologies. This paper contributes uniquely to existing research by emphasizing the necessity for interdisciplinary collaboration, rigorous data governance standards, proactive ethical frameworks, and structured training programs to ensure successful and sustainable AI integration in healthcare. The findings provide clear strategic recommendations for healthcare providers, technology developers, and policy makers to facilitate effective and ethical AI implementation. Finally, this review proposes directions for future research, advocating for longitudinal studies to evaluate the long-term efficacy and integration success of AI technologies in healthcare settings. This comprehensive synthesis offers a strategic resource for navigating the complex landscape of AI-driven healthcare transformation, clearly positioning it as an essential contribution to both academic research and healthcare practice domains. Key References (Additional references are available from the author) Gomez-Cabello, C.A., Borna, S., Pressman, W., Haider, S.A., Haider, C.R., & Forte, A.J. (2024). Artificial-intelligence-based clinical decision support systems in primary care: A scoping review of current clinical implementations, European Journal of Investigation in Health, Psychology and Education. 14, 685–698. Regan, E. A. (2022). Changing the research paradigm for digital transformation in healthcare delivery. Frontiers in Digital Health, 4, 911634-911634.