The role of artificial intelligence in enhancing interprofessional education and collaborative practice: a mixed methods scoping review
Bibliographic record
Abstract
In this scoping review, we examined the role of artificial intelligence (AI) in enhancing interprofessional education and collaborative practice (IPECP) within healthcare settings. Drawing on the Canadian Interprofessional Health Collaborative “Competency Framework,” the review investigated AI’s capacity to support essential IPECP competencies, including team communication, relationship-focused care, role clarification, and collaborative leadership. A comprehensive literature search identified 15 studies published from 2010 onwards that explored various AI applications, such as virtual reality simulations, clinical decision support systems, and machine learning algorithms, aimed at fostering interprofessional teamwork and improving healthcare outcomes. Key findings suggest that AI could facilitate effective team communication, real-time decision-making, and interprofessional education by enabling consistent, evidence-based recommendations and personalized treatment plans. However, several barriers to AI adoption were noted, including clinician mistrust, data security concerns, and challenges integrating AI within existing healthcare infrastructure. These findings highlight the potential for AI to advance IPECP but underscore the need for further research explicitly aligned with targeted IPECP competencies. Addressing these barriers will be critical to integrating AI into standard team-based healthcare practices.
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 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 | 0.041 | 0.128 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.006 |
| Bibliometrics | 0.021 | 0.019 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.010 | 0.006 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".