| Title | Source | Highlights | Gaps |
| Organizational, professional, and patient characteristics associated with artificial intelligence adoption in healthcare: A systematic review. | | Study organizational, professional, and patient characteristics factors that influence the adoption of Artificial Intelligence (AI) in healthcare. | Technology aspects. Low response rate. |
| Artificial intelligence healthcare service resources adoption by medical institutions based on the TOE framework. | | Study factors influence AI adoption, such as awareness, risk of data, management support, and policy factors. Adopted hierarchy decision-making processes to present factors relation. | Ethical and social factors. Hospital size and government policies are subject to change. |
| Perspective of Information Technology Decision Makers on Factors Influencing Adoption and Implementation of Artificial Intelligence Technologies in 40 German Hospitals: Descriptive Analysis. | | The study focuses on the AI readiness of German hospitals and the challenges to AI adoption. Focus on IT managers. Major challenges include existing IT infrastructure and unclear business cases. | Specific country. Small sample compared to the number of hospitals. Low response rate, survey technical issues. Specific group of stakeholders. |
| Big data analytics: Understanding its capabilities and potential benefits for healthcare organizations. | | Examines the design and implementation of big data analytics in healthcare. Use 26 implementation cases. | Associated challenges. Technology perspective. |
| The role of artificial intelligence in healthcare: a structured literature review. | | Discuss the role of AI in cardiac imaging. Highlight challenges related to data management, ethics, and regulation. | Specific AI application. Human factors. |
| Accelerating the integration of ChatGPT and other large-scale AI models into biomedical research and healthcare. | | Benefits and challenges of integrating large-scale AI models into biomedical research and healthcare. | Organizational challenges. Specific AI application. |
| Artificial intelligence in healthcare: Opportunities and risk for future. | | Literature review from three databases. Highlight challenges such as privacy and ethical issues. | Technological factors, costs, and management role. |
| Mapping the challenges of Artificial Intelligence in the public sector: Evidence from public healthcare. | | Evaluates the challenges encountered AI adoption in public healthcare in China. Used case study methodology. | Specific country. China has a unique healthcare system. Limited to the public sector. |
| Current Challenges and Barriers to Real-World Artificial Intelligence Adoption for the Healthcare System, Provider, and the Patient. | | Discuss the AI adoption challenge in a particular area, ophthalmology. Emphasize ethical and liability concerns. | Technology factors. Specific application area. |
| User Intentions to Use ChatGPT for Self-Diagnosis and Health-Related Purposes: Cross-sectional Survey Study. | | Discuss factors that influence user intention. Highlighted the importance of collaborations among developers and health policymakers. | Users ‘actual use Organization factors. Technology factors. |
| Artificial intelligence in healthcare: Complementing, not replacing, doctors and healthcare providers. | | Discuss the doctor and provider’s concerns about AI, especially the potential replacement. | Organization factors. Technology factors. Patients’ role. |
| Artificial intelligence in dentistry: chances and challenges. | | Discuss AI applications’ benefits and challenges in the field of dentistry. Highlighted data availability and lack of AI deployment plan as challenges. | Specific application area Ethical and social factors. |
| Impact and Challenges of Integrating Artificial Intelligence and Telemedicine into Clinical Ophthalmology. | | Discuss the benefits and challenges of AI in ophthalmology. Focus on legal, safety, and privacy challenges. | Specific application area. Technology factors. |
| Framework for Understanding the Impact of Machine Learning and Artificial Intelligence in Healthcare Industry. | | Discuss AI adoption factors using a conceptual framework. Focus on data, workforce, patients, security, and privacy. | Specific country. Cultural and infrastructural differences. Organization factors. |
| Ethical, legal, and financial considerations of artificial intelligence in surgery. | | Discuss the AI adoption challenge in surgery. Focus on decision-making considerations such as legal, financial, and ethical implications. | Specific application area. Organization factors. Technology Factors. |
| Investigating the impacting factors for the healthcare professionals to adopt artificial intelligence-based medical diagnosis support system (AIMDSS). | | Discuss healthcare professionals’ adoption of AI-based decision-making technology. Highlighted trust, complexity, technology, and personal IT experience as influencing factors. | Specific country (China). Specific application. Organizational Factors. Ethical and legal factors. |