BACKGROUND Artificial intelligence (AI) is often promoted as a potential solution to many challenges healthcare systems face worldwide. However, its implementation in clinical practice lags behind technological development. OBJECTIVE This study aimed to gain insights into the current state and prospects of AI technology from the stakeholders most directly involved in its adoption in the healthcare sector, whose perspectives have received limited attention in research to date. METHODS For this purpose, the perspectives of AI researchers and healthcare information technology (IT) professionals in North America and Western Europe were collected and compared for profession-specific and regional differences. In the preregistered, mixed-methods, cross-sectional study, 23 experts were interviewed using a semistructured guide. Data from the interviews were analyzed using deductive and inductive qualitative methods for the thematic analysis along with topic modeling to identify latent topics. RESULTS Through our thematic analysis, four major categories emerged: (1) the current state of AI systems in healthcare, (2) the criteria and requirements for implementing AI systems in healthcare, (3) the challenges in implementing AI systems in healthcare, and (4) the prospects of the technology. Experts discussed the capabilities and limitations of current AI systems in healthcare, in addition to their prevalence and regional differences. Several criteria and requirements deemed necessary for successful implementation of AI systems were identified, including the technology’s performance and security, smooth system integration and human-AI interaction, costs, stakeholder involvement, and employee training. However, regulatory, logistical, and technical issues were identified as the most critical barriers to an effective technology implementation process. In the future, our experts predict both various threats and many opportunities related to AI technology in the healthcare sector. CONCLUSIONS Our work provides new insights into the current state, criteria, challenges, and outlook for implementing AI technology in healthcare from the perspective of AI researchers and IT professionals in North America and Western Europe. For the full potential of AI-enabled technologies to be exploited and for them to contribute to solving current healthcare challenges, critical implementation criteria must be met, on the one hand, and all groups involved in the process must work together, on the other.

Background Artificial intelligence (AI) is often promoted as a potential solution for many challenges health care systems face worldwide. However, its implementation in clinical practice lags behind its technological development. Objective This study aims to gain insights into the current state and prospects of AI technology from the stakeholders most directly involved in its adoption in the health care sector whose perspectives have received limited attention in research to date. Methods For this purpose, the perspectives of AI researchers and health care IT professionals in North America and Western Europe were collected and compared for profession-specific and regional differences. In this preregistered, mixed methods, cross-sectional study, 23 experts were interviewed using a semistructured guide. Data from the interviews were analyzed using deductive and inductive qualitative methods for the thematic analysis along with topic modeling to identify latent topics. Results Through our thematic analysis, four major categories emerged: (1) the current state of AI systems in health care, (2) the criteria and requirements for implementing AI systems in health care, (3) the challenges in implementing AI systems in health care, and (4) the prospects of the technology. Experts discussed the capabilities and limitations of current AI systems in health care in addition to their prevalence and regional differences. Several criteria and requirements deemed necessary for the successful implementation of AI systems were identified, including the technology’s performance and security, smooth system integration and human-AI interaction, costs, stakeholder involvement, and employee training. However, regulatory, logistical, and technical issues were identified as the most critical barriers to an effective technology implementation process. In the future, our experts predicted both various threats and many opportunities related to AI technology in the health care sector. Conclusions Our work provides new insights into the current state, criteria, challenges, and outlook for implementing AI technology in health care from the perspective of AI researchers and IT professionals in North America and Western Europe. For the full potential of AI-enabled technologies to be exploited and for them to contribute to solving current health care challenges, critical implementation criteria must be met, and all groups involved in the process must work together.