Learning to Teach AI: Understanding the Needs of Healthcare Professionals
Bibliographic record
Abstract
As Artificial Intelligence (AI) technologies become more integrated into clinical settings to optimize care, healthcare professionals (HCPs) will need to become more adept in responsibly using these novel technologies to augment patient care. A qualitative study, consisting of semi-structured interviews was conducted to explore the informational needs of HCPs and gaps in current AI education. Participants, consisting of educators and learners, were recruited from AI programs. The interview data were analyzed using inductive thematic analysis. Three themes were identified, addressing the need for (1) developing a longitudinal AI curriculum to transform the mindset, skillset, and toolset of providers, (2) cultivating an active learning approach to foster knowledge mobilization and optimize the use of AI tools in the provision of care, and (3) fostering a multidisciplinary approach to AI curriculum design is essential to promote collaborative efforts among HCPs in implementing AI tools. This study identified five key recommendations to prepare HCPs with the knowledge and skills necessary for an AI-driven future.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.021 | 0.034 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.009 | 0.006 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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 source (direct Gemma or distilled Codex), 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".