Accelerating AI Innovation in Healthcare Through Mentorship
Bibliographic record
Abstract
The adoption of Artificial Intelligence (AI) in the Canadian healthcare system falls behind that of other countries. Socio-technological considerations such as organizational readiness and a limited understanding of the technology are a few barriers impeding its adoption. To address this need, this study implemented a five-month AI mentorship program with the primary objective of developing participants' AI toolset. The analysis of our program's effectiveness resulted in recommendations for a successful mentorship and AI development and implementation program. 12 innovators and 11 experts from diverse backgrounds were formally matched and two symposiums were integrated into the program design. 8 interviewed participants revealed positive perceptions of the program underscoring its contribution to their professional development. Recommendations for future programs include: (1) obtaining organizational commitment for each participant; (2) incorporating structural supports throughout the program; and (3) adopting a team-based mentorship approach. The findings of this study offer a foundation rooted in evidence for the formulation of policies necessary to promote the integration of AI in Canada.
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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 | 0.037 | 0.060 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".