Knowledge is not all you need to generate trust in AI use in healthcare
Bibliographic record
Abstract
Abstract Background Canada has invested significantly in artificial intelligence (AI) research and development over the last several years. Canadians’ knowledge of and attitudes towards AI in healthcare are understudied. Objectives To explore the relationships between age, gender, education level, and income on Canadians’ knowledge of AI, their comfort with its use in healthcare, and their comfort with using personal health data in AI research. Methods Ordinal logistics regression and multivariate polynomial regression were applied to data from the 2021 Canadian Digital Health Survey using RStudio and SigmaZone’s Design of Experiments Pro. Results Female and older Canadians self-report less knowledge about AI than males and other genders and younger Canadians. Female Canadians and healthcare professionals are less comfortable with use of AI in healthcare compared to males and people with other levels of education. Discomfort appears to stem from concerns about data security and the current maturity level of the technology. Conclusion Knowledge of AI and the use of AI in healthcare are inversely correlated with age and directly correlated with education and income levels. Overall, female respondents self-reported less knowledge and comfort with AI in healthcare and research than other genders. Privacy concerns should continue to be addressed as a major consideration when implementing AI tools. Canadians, especially older females, not only need more education about AI in healthcare, but also need more reassurance about the safe and responsible use of their data and how bias and other issues with AI are being addressed. Author Summary Artificial intelligence (AI) and its application has garnered significant public interest and excitement within healthcare in recent years. However, its successful integration and use in healthcare will depend on patient and user adoption. As a result, AI tools may be limited in healthcare when user concerns are not carefully addressed and if patients are not educated about how these technologies work. While there have been studies on the attitudes of clinicians and healthcare professionals toward AI, little is known about the general public’s perception of AI within the healthcare setting. Our study addresses this gap in the literature by analyzing data from the 2021 Canadian Digital Health Survey to understand the relationships between Canadians’ attitudes towards AI and various socioeconomic and demographic factors. Our results found that older Canadians, Canadians with less formal education and women need to be better informed about the safe and responsible use of AI and be reassured about good data security practices before it can be broadly accepted by them. In addition, the element of trust may be a factor that is contributing to the higher levels of discomfort with AI observed in middle-aged Canadians. The findings from this study will help stakeholders better implement and broaden the accessibility of AI technologies.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.020 | 0.117 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.011 |
| Scholarly communication | 0.010 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.013 | 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".