Crucial Role of Understanding in Human-Artificial Intelligence Interaction for Successful Clinical Adoption
Bibliographic record
Abstract
Numerous research studies have demonstrated the potential of artificial intelligence (AI) to enhance diagnostic accuracy, alleviate the workload of healthcare workers preventing burnout, and ultimately improve healthcare outcomes.The U.S. FDA has already approved over 1000 AI and machine learning-enabled medical devices [1].However, despite these, compelling examples of AI's benefits through widespread adoption in real-world clinical practice, outside controlled research settings, are still rare.In AI-assisted healthcare, human-AI interaction plays a pivotal role in achieving the effectiveness of AI.Human-AI interaction refers to the complex dynamics between healthcare professionals using AI tools and the tools.This includes how healthcare professionals interpret and respond to AI-generated results and, more broadly, also encompasses how they manage the digital workload from AI use, such as cognitive overload and digital fatigue.Despite its importance, the nuances of human-AI interaction have
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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 | 0.016 | 0.051 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.011 | 0.005 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.025 | 0.027 |
| Insufficient payload (model declined to judge) | 0.008 | 0.005 |
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".