Challenges for the Integration of Artificial Intelligence in Healthcare Services: A Decision-Making Approach

This study aims to elucidate the interdependent effects of the challenges and risks of using artificial intelligence in the healthcare sector. The ten challenges and risks obtained by literature were assessed by five professionals involved in managing health. Participants were selected based on having at least ten years of academic or professional experience in health. The participants made their judgments on the topic of structured forms. DEMATEL (The Decision-Making Trial and Evaluation Laboratory) technique investigated the cause-effect relationships between the identified integration challenges. According to DEMATEL analysis results in terms of the degree of importance, safety and security risk (SSR) is ranked in the first place, and inadequate patient risk assessments (IPRA), data quality risks (DQR), verifiability risks (VR), stakeholders perceived mistrust (SPM), integration challenges (IC), ethical considerations (EC), algorithm/decision-making bias (AMB) and job displacement risks (JDR) are ranked in the following places. In addition, DQR, AMB, SSR, VR, IPRA, and DPR are causal variables; EC, IC, JDR, and SPM are regarded as effects. These factors highlight the need for robust mechanisms to ensure the integrity of data, the accuracy of risk assessments, and the transparency of the decision-making processes of AI. Negative impacts on ethics, inclusion, employment, and trust between stakeholders will likely be reduced by addressing the root causes, such as data quality, risk assessment, and algorithmic bias, and developing policies to address them.