Conditionally positive: a qualitative study of public perceptions about using health data for artificial intelligence research
Bibliographic record
Abstract
ABSTRACT Objectives Given widespread interest in applying artificial intelligence (AI) to health data to improve patient care and health system efficiency, there is a need to understand the perspectives of the general public regarding the use of health data in AI research. Design A qualitative study involving six focus groups with members of the public. Participants discussed their views about AI in general, then were asked to share their thoughts about three realistic health AI scenarios. Data were analysed using qualitative description thematic analysis. Settings Two cities in Ontario, Canada: Sudbury (400 km north of Toronto) and Mississauga, (part of the Greater Toronto Area). Participants Forty-one purposively sampled members of the public (21M:20F, 25-65 years, median age 40). Results Participants had low levels of prior knowledge of AI and mixed, mostly negative, perceptions of AI in general. Most endorsed AI as a tool for the analysis of health data when there is strong potential for public benefit, providing that concerns about privacy, consent, and commercial motives were addressed. Inductive thematic analysis identified AI-specific hopes (e.g., potential for faster and more accurate analyses, ability to use more data), fears (e.g., loss of human touch, skill depreciation from over-reliance on machines) and conditions (e.g., human verification of computer-aided decisions, transparency). There were mixed views about whether consent is required for health data research, with most participants wanting to know if, how and by whom their data were used. Though it was not an objective of the study, realistic health AI scenarios were found to have an educational effect. Conclusions Notwithstanding concerns and limited knowledge about AI in general, most members of the general public in six focus groups in Ontario, Canada perceived benefits from health AI and conditionally supported the use of health data for AI research. STRENGTHS AND LIMITATIONS OF THIS STUDY A strength of this study is the analysis of how diverse members of the general public perceive three realistic scenarios in which health data are used for AI research. The detailed health AI scenarios incorporate points that previous qualitative research has indicated are likely to elicit discussion (e.g., use of health data without express consent, involvement of commercial organisations in health research, inability to guarantee anonymity of genetic data) and may also be useful in future qualitative research studies and for educational purposes. The findings are likely to be relevant to organisations that are considering making health data available for AI research and development. Notwithstanding the diverse ethnic and educational backgrounds of participants, overall the sample represents the general (mainstream) population of Ontario and results cannot be interpreted as presenting the views of specific subpopulations and may not be generalisable across Ontario or to other settings. Given the low level of knowledge about AI in general it is possible that the views of participants would change substantially if they learned and understood more about AI. TRANSPARENCY STATEMENT P. Alison Paprica affirms that the manuscript is an honest, accurate and transparent account of the study being reported; that no important aspects of the study have been omitted; and that there were no discrepancies from the study as originally approved by the University of Toronto Research Ethics Board.
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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 | 0.055 | 0.070 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.014 | 0.020 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 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".