Conflicts and complexities around intellectual property and value sharing of artificial intelligence healthcare solutions in public-private partnerships: A qualitative study
Bibliographic record
Abstract
Background Public healthcare systems are increasingly relying on artificial intelligence (AI) technologies to meet growing healthcare needs. Because AI technologies are complex and costly to develop, public-private partnerships (PPPs) between digital companies and university hospital centres are being promoted as a key for the successful development and implementation of AI solutions. This article aims to shed light on stakeholders’ perspectives on the intellectual property (IP) and value sharing of AI technologies developed by PPPs and how their practical experiences can affect the success or failure of such PPPs. Methods Semi-structured interviews were conducted with 29 stakeholders concerned with and/or involved in digital health technologies in a large Canadian university hospital centre. Data were collected and analysed through a mixed deductive-inductive approach. Results The analysis revealed three key themes highlighting AI IP issues of concern for PPP stakeholders. First, the collaborations and contributions required from all stakeholders to develop AI technologies of clinical and commercial value are highly complex and often unclear. Second, the lack of institutional and commercial recognition of clinicians’ essential contributions to AI solution development results in competing academic and business imperatives that hinder their engagement in PPPs. Finally, public healthcare systems’ strategic use of AI requires new policies adapted to the digital economy where IP plays a central role in value generation and sharing. Conclusion For PPPs developing AI healthcare technologies to be successful, updated policies clarifying public healthcare systems’ strategic use of AI are required as well as clear value-sharing frameworks between stakeholders.
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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 | 0.038 | 0.046 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.016 | 0.017 |
| Scholarly communication | 0.008 | 0.010 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 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".