Role of pandemic in driving adoption of artificial intelligence in healthcare industry
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The global population continues to be affected by the ongoing coronavirus pandemic, resulting in a gradual depletion of the limited healthcare resources. In order to fully realize the potential benefits of clinical artificial intelligence (AI), it is necessary to ensure its widespread adoption and use. The current body of research investigates the inclination to use clinical Artificial Intelligence & Machine Learning using a comprehensive survey and identifies the factors that influence its adoption. This study examines the United States and Canada, two North American nations, using a sample size of 1068 individuals. The findings indicate that participants have a significant aversion towards artificial intelligence (AI). In a hypothetical scenario including pre-hospital triage for the coronavirus, just one out of ten individuals expressed a preference for clinical AI and machine learning over clinicians. The level of trust individuals place in clinical AI & ML, together with their level of receptiveness, are two crucial factors that impact the extent to which these technologies are embraced. Our study indicates that individuals who lack social ties and suffer sentiments of mistrust and neglect from human physicians are more likely to adopt clinical AI & ML. These findings indicate that widespread acceptance of clinical AI and machine learning may need individuals to reduce their emotional attachment to humans and demonstrate less reliance on human physicians. Based on our findings, we recommend that prioritizing the establishment of trust, rather than diminishing confidence in physicians, should be the primary focus in any law regarding the use of clinical AI & ML. Keywords: Healthcare, Artificial Intelligence, Machine Learning, Healthcare, Pandemic.
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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 | 0.008 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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 it