An Explainable AI Application (AF’fective) to Support Monitoring of Patients With Atrial Fibrillation After Catheter Ablation: Qualitative Focus Group, Design Session, and Interview Study
Bibliographic record
Abstract
BACKGROUND: The opaque nature of artificial intelligence (AI) algorithms has led to distrust in medical contexts, particularly in the treatment and monitoring of atrial fibrillation. Although previous studies in explainable AI have demonstrated potential to address this issue, they often focus solely on electrocardiography graphs and lack real-world field insights. OBJECTIVE: We addressed this gap by incorporating standardized clinical interpretation of electrocardiography graphs into the system and collaborating with cardiologists to co-design and evaluate this approach using real-world patient cases and data. METHODS: We conducted a 3-stage iterative design process with 23 cardiologists to co-design, evaluate, and pilot an explainable AI application. In the first stage, we identified 4 physician personas and 7 explainability strategies, which were reviewed in the second stage. A total of 4 strategies were deemed highly effective and feasible for pilot deployment. On the basis of these strategies, we developed a progressive web application and tested it with cardiologists in the third stage. RESULTS: The final progressive web application prototype received above-average user experience evaluations and effectively motivated physicians to adopt it owing to its ease of use, reliable information, and explainable functionality. In addition, we gathered in-depth field insights from cardiologists who used the system in clinical contexts. CONCLUSIONS: Our study identified effective explainability strategies, emphasized the importance of curating actionable features and setting accurate expectations, and suggested that many of these insights could apply to other disease care contexts, paving the way for future real-world clinical evaluations.
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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 | 0.039 | 0.048 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".