Reflections on co-creating a model for the value assessment of artificial intelligence technologies
Bibliographic record
Abstract
Aims: A multidisciplinary group of experts and patients developed the Model for ASsessing the value of Artificial Intelligence (MAS-AI) to ensure an evidence-based and patient-centered approach to introducing artificial intelligence technologies in healthcare. In this article, we share our experiences with meaningfully involving a patient in co-creating a research project concerning complex and technically advanced topics. Methods: The co-creation was evaluated by means of initial reflections from the research team before the project started, in a continuous logbook, and through semi-structured interviews with patients and two researchers before and after the active co-creation phase of the project. Results: There were initial doubts about the feasibility of including patients in this type of project. Co-creation ensured relevance to patients, a holistic research approach and the debate of ethical considerations. Due to one patient dropping out, it is important to foresee and support the experienced challenges of time and energy spent by the patient in future projects. Having a multidisciplinary team helped the collaboration. A mutual reflective evaluation provided insights into the process which we would otherwise have missed. Conclusions: We found it possible to create complex and data-intense research projects with patients. Including patients benefitted the project and gave researchers new perspectives on their own research. Mutual reflection throughout the project is key to maximise learning for all parties involved.
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 | 0.161 | 0.190 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.018 | 0.054 |
| Scholarly communication | 0.026 | 0.027 |
| Open science | 0.006 | 0.033 |
| Research integrity | 0.017 | 0.049 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".