A User-Centered Design of Explainable AI for Clinical Decision Support
Bibliographic record
Abstract
Clinical decision support (CDS) systems are computer applications whose goal is to facilitate the decision-making process of clinicians. In recent years, CDSS has developed an interest in applying machine learning (ML) models to make predictions related to clinical outcomes. The limited interpretability of many ML models is a major barrier to clinical adoption. This challenge has sparked research interest in interpretable and explainable AI, commonly known as XAI. XAI methods are used to construct and communicate explanations of the predictions made by machine learning models so that end users can interpret those predictions. However, these methods are not designed based on end-users' needs; rather, they are based on the developersâ intuitions of what a good explanation is. Furthermore, XAI methods are not tailored to the specific tasks that a user will undertake, nor are they tailored to the interface used to perform those tasks. To tackle these issues, we propose to develop a visual analytic tool to explain an ML model for clinical applications whose design will explicitly take into account the context of tasks and the needs of end-users.
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 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 | 0.002 | 0.001 |
| 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 | 0.001 |
| Open science | 0.001 | 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".