Global Adoption, Promotion, Impact, and Deployment of AI in Patient Care, Health Care Delivery, Management, and Health Care Systems Leadership: Cross-Sectional Survey.

BACKGROUND: Artificial intelligence (AI) is increasingly being integrated into health care, offering a wide array of benefits. Current AI applications encompass patients' diagnosis, treatment, data mining, and more to enhance patient care and quality of life. It is also democratizing access to expert support by providing timely and accurate disease diagnoses, better clinical management, quicker drug discovery, improved disease prevention, big data management, and health protection. OBJECTIVE: The aim of the study is to document AI adoption in health care, assess participants' perception on its usefulness in the management of health care delivery and leadership of health care systems, and identify characteristics of early adopters. METHODS: We conducted a worldwide cross-sectional survey across all 6 inhabited continents using a self-administered questionnaire developed with the Qualtrics electronic data collection tool. This was piloted and reviewed to ensure completeness, accuracy, acceptability, cultural sensitivity, and relevance. Respondents were recruited by individualized email, following identification from professional associations or organizations, professional networks, and social media. Data were analyzed using SPSS (IBM Corp), with results presented as narrative, charts, and tables. RESULTS: In total, 506 health care professionals completed the survey. While 92.3% (467/506) of respondents believed that AI has a role in patient care and health care management, only 76.5% (300/392) were willing to support AI adoption and embedding in their organization. Although top managers are mainly responsible for adoption processes, staff training remains low. AI is currently used mostly for diagnosis, patient care, and precision medicine. These uses of AI will continue in the near future, but in different ways. AI adoption was highest in Europe and lowest in Africa. Black or African American people were more likely to support AI adoption than White and Asian people. Poor knowledge of AI, fear of job loss, and resistance to change were the top barriers to AI adoption and embedding. CONCLUSIONS: AI use in health is global, but the adoption rate varies by geography and individual characteristics. AI adoption communication by executive health care management is poor, as is the level of training of health care staff. To improve AI adoption, management should improve communication with their teams, provide training on AI to their workers, and help individuals understand how AI works. Barriers such as ethical issues around data ownership and use should be addressed. African organizations should be proactive and invest in AI adoption early, so that they are not left behind in the AI revolution.