A Mixed Methods Comparison of Artificial Intelligence-Powered Clinical Decision Support System Interfaces for Multiple Criteria Decision Making in Antidepressant Selection
Bibliographic record
Abstract
Abstract BACKGROUND Artificial intelligence-powered clinical decision support systems (AI-CDSS) have recently become foci of research. When clinicians face decisions about treatment selection, they must contemplate multiple criteria simultaneously. The relative importance of these criteria often depends on the clinical scenario, as well as clinician and patient preferences. It remains unclear how AI-CDSS can optimally assist clinicians in making these complex decisions. In this work we explore clinician reactions to different presentations of AI results in the context of multiple criteria decision-making during treatment selection for major depressive disorder. METHODS We developed an online platform for depression treatment selection to test three interfaces. In the probabilities alone (PA) interface, we presented probabilities of remission and three common side effects for five antidepressants. In the clinician-determined weights (CDW) interface, participants assigned weights to each of the outcomes and obtained a score for each treatment. In the expert-derived weights interface (EDW), outcomes were weighted based on expert opinion. Each participant completed three clinical scenarios, and each scenario was randomly paired with one interface. We collected participants’ impressions of the interfaces via questionnaires and written and verbal feedback. RESULTS Twenty-two physicians completed the study. Participants felt that the CDW interface was most clinically useful (H=10.29, p<0.01) and more frequently reported that it had an impact on their decision making (PA: in 55.5% of experienced scenarios, CDW: in 59.1%, EDW: in 36.6%). Clinicians most often chose a treatment different from their original choice after reading the clinical scenario in the CDW interface (PA: 26.3%, CDW: 33.3%, EDW: 15.8%). CONCLUSION Clinicians found a decision support interface where they could set the weights for different potential outcomes most useful for multi-criteria decision making. Allowing clinicians to weigh outcomes based on their expertise and the clinical scenario may be a key feature of a future clinically useful multi-criteria AI-CDSS.
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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 | 0.045 | 0.149 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".