P075: THE CURRENT STATE AND SENTIMENT REGARDING ARTIFICIAL INTELLIGENCE (AI) IN NORTH AMERICAN ANESTHESIOLOGY RESIDENCY PROGRAMS: A MULTI-NATIONAL, MULTI-PROGRAM SURVEY.
Tyler V Elliott, BS1; Joseph C Goldstein, MD2; Heidi V Goldstein, MD2; 1University of Florida College of Medicine; 2Department of Anesthesiology, North Florida/South Georgia Veterans Health System and the University of Florida
Introduction: Artificial intelligence (AI) technology currently plays a significant role in daily life and is expected to further lead an accelerated transformational change in many personal and professional domains, including healthcare. Recognizing its important role in medicine, the American Medical Association (AMA) and its House of Delegates has adopted various policies regarding AI incorporation into medical training. Over the coming years, this technology will likely be further integrated into medical curricula to prepare the future physician workforce.
One particular medical specialty that is expected to be impacted during this transition is anesthesiology, which has already benefited greatly from technological advancements to improve patient safety. Perioperatively, AI utilization can further augment preoperative risk modeling, intraoperative decision support, and pain management strategies. This study aims to establish a current state and sentiment of artificial intelligence training in anesthesiology residency programs around North America, so as to guide leaders in the field in determining the needs and opportunities for an AI curriculum and related policies.
Methods: After obtaining IRB approval (IRB #202201682), we sent email invitations containing a link to our UF Qualtrics questionnaire to North American (United States, Canada, and Mexico) anesthesiology residency program directors who were identified via publicly available contact information. After the initial invitation to participate in the study and two reminder emails, the survey was closed, and the collected data was evaluated.
Results: The study was opened to 169 total program directors, of which 32 agreed to participate (19%). The data collected has provided insights into the respondents’ familiarity with AI educational policy, percentage of respondents that currently offer or plan to offer AI-focused training, perceived importance of integrating AI fundamentals into their program’s anesthesiology curriculum, and rated impact of artificial intelligence on the specialty, among others.
Conclusions: To our knowledge, this is the first study to evaluate the current state and sentiment of AI incorporation into North American anesthesiology residency programs. Though the majority of respondents have indicated that it is at least somewhat important to incorporate AI training into their respective residency program, and an even greater percentage expect AI to bring change to the specialty, less than one-fifth currently offer any type of related training. These findings highlight a significant gap between the recognition of the importance of AI to anesthesia and the ability to offer training. This is further evidenced by various barriers that respondents have indicated as hindrances, including a need for qualified or interested faculty, support for the topic, and additional time within the current curriculum. We hope that leaders within the specialty can utilize these insights to prepare their programs for the continued incorporation of artificial intelligence in medicine.