The Impact of AI Ethicality on Clinical Decision-making: The Role of AI Trustworthiness and Representativeness
Bibliographic record
Abstract
There are critical ethical concerns about using artificial intelligence (AI) in healthcare. Healthcare professionals are more likely to adopt AI if they believe in its capability and perceive alignment in clinical decisions with ethical principles. Although previous studies have shown ethical issues in clinical decision making, empirical research investigating how AI ethicality influences healthcare professionals’ perceptions of AI performance expectancy remains limited. Drawing on integrated ethical decision making, we propose a research model to examine the effects of AI ethicality and AI trustworthiness on healthcare professionals’ perceptions of AI performance expectancy. Moreover, we explore whether AI representativeness in adhering to EDI (equity, diversity, inclusion) strengthens the impact of AI ethicality on AI performance expectancy. A vignette-based methodology is proposed to test the model using data collected from healthcare professionals. This research aims to contribute to the literature by providing empirical evidence on how ethical and representative AI systems shape healthcare professionals’ expectations.
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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 | 0.061 | 0.239 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.009 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| 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".