Artificial Intelligence-Driven Usability Testing Products and Ethical Considerations for Medical Solutions Design
Bibliographic record
Abstract
Usability testing is critical to developing equitable medical devices and digital health tools; however, traditional methods have been found to be resource-intensive and inconsistent, posing concerns for the adequate representation of marginalized communities in medical solution development. Artificial intelligence (AI)-driven usability testing methods have emerged as a promising solution to assist user experience (UX) analysts with their evaluation and mitigating potential evaluator bias. However, its reliability, particularly its impact on inclusivity and equity of marginalized communities, remains uncertain. Following the findings of a previous narrative literature review, this competitive evaluation assessed on-the-market AI-informed tools from seven prominent usability testing platforms, comparing the current state of AI-driven usability testing in the literature and commercially, using an adapted Society of Automotive Engineers five levels of automation and a three-level equity consideration scale. Six platforms offered Level 1 automation AI-products, assisting UX evaluators with facilitation and data analysis, while one achieved Level 3 conditional automation. Four platforms did not explicitly address the equity impact of their products, with only one platform incorporating a bias-reduction feature. Overall, AI-informed tools provide potentially inexpensive usability testing alternatives for digital health tools; however, more research is required to validate the consistency, accuracy, and reliability of these tools in usability testing practice.
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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 | 0.343 | 0.503 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.004 | 0.015 |
| Scholarly communication | 0.013 | 0.011 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.006 | 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".