JMIR Publications

ABSTRACT

Background:

Artificial Intelligence (AI) is increasingly being integrated into healthcare, 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:

This study was designed to review AI adoption in healthcare, assess its usefulness in management of healthcare delivery, and leadership of healthcare systems, and to identify characteristics of early adopters.

Methods:

We conducted a world-wide cross-sectional survey across all six inhabited continents using a self-administered questionnaire developed with the Qualtrics electronic data collection tool. We piloted and reviewed the questionnaire to ensure completeness, accuracy, acceptability, cultural sensitivity, and relevance. Individuals were recruited via an individualized email, following identification from professional associations/organizations, professional networks and social media. Data were analyzed using SPSS and results are presented as narrative, charts and tables. Prior ethical approval was granted by the institutional review board of California State University, Dominguez Hills.

Results:

Five hundred and six (506) healthcare professionals responded to the survey. While 92% of respondents believed that AI has a role in patient care and healthcare management , only 76.5% were willing to support AI adoption and embedding in their organization. Although top managers are responsible for most adoption processes, staff training remains low. AI is mostly used for diagnosis, patient care, precision medicine, and these uses will continue in the near future, but at a reduced level. AI adoption is highest in Europe and lowest in Africa. Blacks/African Americans were more likely to support AI adoption than Whites/Caucasians and Asians. Poor knowledge of AI, fear of job loss and resistance to change were top barriers to AI adoption and embedding.

Conclusions:

To establish seamless AI adoption and embedding, executive healthcare management should communicate better with their teams, provide training on AI to their workers and make sure individuals understand that AI works best when guided by skilled personnel. Also, ethical issues around data ownership and use should be addressed and organizational AI policies developed. Finally, African organizations should be proactive by investing in AI adoption and embedding early, so that they are not left behind in the AI revolution. Clinical Trial: N/A