Resident Physicians’ Perceptions of Artificial Intelligence and Implications for Medical Education: A Qualitative Study
Bibliographic record
Abstract
Background: Educators have called for training in artificial intelligence (AI) in medical education given its certain impact on the future of healthcare. However, there is no consensus regarding how to introduce AI into medical education and little is known about how AI is viewed among medical trainees. In an effort to inform the development of medical education curricula on AI, this study explores perceptions of resident physicians regarding AI in healthcare and its possible impact on their future practice. Methods: The authors conducted focus groups with resident physicians across multiple specialties in 2018-2019. Residents were invited to voluntarily participate during pre-existing conference times. Interview transcripts were coded iteratively, and coded data was clustered into categories and themes to capture resident perceptions on AI. Results: Fifty-six residents from emergency medicine, internal medicine, pathology, pediatrics, and radiology participated in six separate focus groups. Conversations generated the following five overarching themes: healthcare is transforming, AI has a role at the clinical and systems level, concern for lack of agency in the development and implementation of AI, AI presents potential harms and uncertainties, and enduring roles of the physician: humanism, judgment, and responsibility. Conclusion: Residents described humanistic roles that should not be replaced by technology and voiced concerns that physicians lack agency to influence how AI will be used in healthcare. Medical education should explore humanistic and ethical challenges related to AI, provide a foundational understanding of AI technology, and offer opportunities for participation in the development of AI technology when possible.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".