Validity and transferability of Model for ASsessing the value of Artificial Intelligence (MAS-AI)
Bibliographic record
Abstract
OBJECTIVES: In 2022, a multidisciplinary group of experts and patients published a Model for ASsessing the value of AI (MAS-AI) in medical imaging. MAS-AI is a critical tool for decision-makers, enabling them to make informed choices on the prioritization of AI solutions. The objective of this study was to assess the face validity and transferability of MAS-AI by investigating workshop participants' perceptions in Denmark, Italy, and Canada regarding the importance of its content. METHODS: A Delphi process was conducted, including inputs from four workshops with a sample of decision makers from hospitals or the healthcare sector, patient partners and various researchers and experts. The participants were asked to rate the importance of each of the domains and subtopics in MAS-AI on a 0-3 Likert scale. RESULTS: A total of 95 participants from three countries participated. The face validity of all MAS-AI domains was confirmed by Denmark, Canada, and Italy, with over 70 percent of the respondents in the first round rating the domains as moderately or highly important. Overall, the five process factors were considered moderately or highly important by between 93 percent and 87 percent of the respondents. All the individual subtopics under each domain were rated above the 70 percent cut-off, except five subtopics for Italy. CONCLUSIONS: The study confirmed the validity of the MAS-AI domains in Denmark, Canada, and Italy. Several improvements in study design and data collection were identified. In the future, analyzing participants to understand which items were rated as important by whom could provide valuable insights.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".