Residents, Faculty, and Artificial Intelligence: Brave New World or Utopia?

Abstract

Since the late 2022 release of OpenAI’s ChatGPT, an open-source natural language processing tool that can generate human-like intellectual content and responses, journal editors, including those at the Journal of Graduate Medical Education (JGME), have intensified debates around fair, appropriate use of this technology for submitted manuscripts.1-4 We have recently expanded JGME’s overall policy of transparency to include artificial intelligence (AI): “Authors must communicate any use of artificial intelligence technology and similar tools, whether for writing assistance, storage, interpretation of qualitative research data, statistical analysis of numerical data, creation of visual imagery, or other uses.”5 In addition, listed JGME authors must be human. Why? Only humans can be accountable for all aspects of the work, a critical authorship criterion.6 Beyond these considerations of authorship and transparency, we are curious about potential AI uses in graduate medical education (GME) for both the process and content of training.7-9In 2020, we asked 40 thought leaders in medical education what they believed would be the most significant transformation in GME over the next decade: AI was a major theme.10 Three years later, AI use in daily life is everywhere, from cell phones and credit checks to weather and climate predictions. AI examples include natural language processing, speech recognition, and creating written works (See if you can tell the difference between human- and AI-generated text in Box 1). The volume of health information has increased beyond the capacity of humans to sift and digest: in 1950 medical information doubled in 50 years; by 2010, it took 3.5 years, and the estimate for 2020 was 73 days.11 However, only a few undergraduate medical schools have added AI experiences, and the Accreditation Council for Graduate Medical Education Milestones do not include AI competencies.11 Currently AI is being introduced into learning and clinical diagnosis support, automated assessments of clinical skills, setting educational goals, and designing curriculum and performance assessments.12,13Scholars have hailed the potential benefits of AI, such as improving manuscripts for authors writing in a second language, saving researchers time when creating research documents or conducting literature searches, and freeing trainees from memorization to focus on reasoning, counseling, and shared decision-making with patients.11,14 Other scholars are concerned that AI tools in GME present serious risks. These include loss of trainee and patient anonymity in confidential information, failure to develop key competencies as those tasks are outsourced to AI tools, erroneous evidence and assessment summaries, and “paper mills,” or fabrication of fake research to pad resumes and grant applications.15-18 As AI is wholly dependent upon the quality and included biases of the available training data, it may generate inaccurate syntheses, which could be missed by inexperienced trainees and beleaguered faculty. In addition, AI could negatively affect trainees’ ability to develop mental models or learn key foundational skills, such as clinical reasoning in treating complex patients. Many patients, with their unique values, social supports, finances, and medical histories, may be hard to fit into AI’s derived diagnoses and management plans. In 2023, the optimal interactions between physicians and AI, patients and AI, and trainees and AI are unknown, yet graduating residents and fellows will increasingly manage these interactions and confront numerous ethical sequelae.Medical education is notorious for diving headlong into the next innovation, often with scanty evidence. But as Dr Rachel Ellaway points out, “the genie is out of the bottle.”3 We need research that examines the best uses of AI in GME. This research should include exploration of both the use of AI during training and a greater understanding of the ethical, beneficial uses of AI in med