Implications and Consequences of Artificial Intelligence in Healthcare Quality and Medical Training

Abstract

Technology is playing an increasing role in the delivery of healthcare. The appropriate implementation of new technologies is a delicate balance of managing risk and meeting the emerging needs of the population. Healthcare strategies requires ongoing adaptation to deliver high quality healthcare to populations with complex healthcare needs. The implementation of Artificial Intelligence warrants careful deliberation to ensure that implications are considered, and consequences are mitigated. The complexity of Artificial Intelligence systems limits the ability of patients to provide informed consent and for clinicians to detect when an error has occurred. This creates new challenges to concepts of privacy, liability, and shared decision-making in healthcare. As a decision-making tool, Artificial Intelligence is only as accurate as the data with which it is provided. Artificial Intelligence systems incorporate and often amplify existing patterns of practice, including societal biases and inequitable healthcare practices. The momentum created by such innovations can lead to blind optimism and unintentional consequences. Navigating the transition to an Artificial Intelligence-assisted era of healthcare delivery will require an appreciation of the opportunities and limits of each technology. Healthcare educators are tasked with preparing learners across all disciplines of healthcare to function in an increasingly technological and rapidly evolving field of practice. This entails instilling learners with the digital literacy to leverage new tools as well as acknowledgement of limitations. We suggest that using Artificial Intelligence correctly has the potential to enhance the efficiency and quality of healthcare delivery. However, if implemented incorrectly these technologies may exacerbate health disparities, disempower patients, and lead to a reduction in the humanity of medical practice.

Keywords: Healthcare, Quality, Medical Training, Artificial Intelligence

DOI: 10.54941/ahfe1002109