Leadership for AI Transformation in Health Care Organization: Scoping Review
Bibliographic record
Abstract
BACKGROUND: The leaders of health care organizations are grappling with rising expenses and surging demands for health services. In response, they are increasingly embracing artificial intelligence (AI) technologies to improve patient care delivery, alleviate operational burdens, and efficiently improve health care safety and quality. OBJECTIVE: In this paper, we map the current literature and synthesize insights on the role of leadership in driving AI transformation within health care organizations. METHODS: We conducted a comprehensive search across several databases, including MEDLINE (via Ovid), PsycINFO (via Ovid), CINAHL (via EBSCO), Business Source Premier (via EBSCO), and Canadian Business & Current Affairs (via ProQuest), spanning articles published from 2015 to June 2023 discussing AI transformation within the health care sector. Specifically, we focused on empirical studies with a particular emphasis on leadership. We used an inductive, thematic analysis approach to qualitatively map the evidence. The findings were reported in accordance with the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analysis extension for Scoping Reviews) guidelines. RESULTS: A comprehensive review of 2813 unique abstracts led to the retrieval of 97 full-text articles, with 22 included for detailed assessment. Our literature mapping reveals that successful AI integration within healthcare organizations requires leadership engagement across technological, strategic, operational, and organizational domains. Leaders must demonstrate a blend of technical expertise, adaptive strategies, and strong interpersonal skills to navigate the dynamic healthcare landscape shaped by complex regulatory, technological, and organizational factors. CONCLUSIONS: In conclusion, leading AI transformation in healthcare requires a multidimensional approach, with leadership across technological, strategic, operational, and organizational domains. Organizations should implement a comprehensive leadership development strategy, including targeted training and cross-functional collaboration, to equip leaders with the skills needed for AI integration. Additionally, when upskilling or recruiting AI talent, priority should be given to individuals with a strong mix of technical expertise, adaptive capacity, and interpersonal acumen, enabling them to navigate the unique complexities of the healthcare environment.
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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 | 0.007 | 0.004 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".