The Perceived Roles of AI in Clinical Practice: National Survey of 941 Academic Physicians

Abstract

Background: Artificial intelligence (AI) and machine learning models are frequently developed in medical research to optimize patient care, yet they remain rarely used in clinical practice.

Objective: This study aims to understand the disconnect between model development and implementation by surveying physicians of all specialties across the United States.

Methods: The present survey was distributed to residency coordinators at Accreditation Council for Graduate Medical Education-accredited residency programs to disseminate among attending physicians and resident physicians affiliated with their academic institution. Respondents were asked to identify and quantify the extent of their training and specialization, as well as the type and location of their practice. Physicians were then asked follow-up questions regarding AI in their practice, including whether its use is permitted, whether they would use it if made available, primary reasons for using or not using AI, elements that would encourage its use, and ethical concerns.

Results: Of the 941 physicians who responded to the survey, 384 (40.8%) were attending physicians and 557 (59.2%) were resident physicians. The majority of the physicians (651/795, 81.9%) indicated that they would adopt AI in clinical practice if given the opportunity. The most cited intended uses for AI were risk stratification, image analysis or segmentation, and disease prognosis. The most common reservations were concerns about clinical errors made by AI and the potential to replicate human biases.

Conclusions: To date, this study comprises the largest and most diverse dataset of physician perspectives on AI. Our results emphasize that most academic physicians in the United States are open to adopting AI in their clinical practice. However, for AI to become clinically relevant, developers and physicians must work synergistically to design models that are accurate, accessible, and intuitive while thoroughly addressing ethical concerns associated with the implementation of AI in medicine.

Keywords: AI adoption; artificial intelligence in medicine; barriers to AI adoption; clinical decision support systems; health care technology acceptance; machine learning in health care; medical informatics; physician attitudes.