Investigating public perception on use of ChatGPT in initial consultations prior to healthcare provider consultations
Bibliographic record
Abstract
Background: This study investigates public perception of using AI-powered ChatGPT for initial consultations before seeing healthcare providers. It aims to understand AI’s capabilities and how to implement such tools in healthcare settings. Methods: A survey containing nineteen questions was distributed to 391 participants aiming to explore public perceptions on the use of ChatGPT prior to initial healthcare consultations via questions pertaining to several domains regarding use of ChatGPT in healthcare. Collected data was summarized and presented through visual depictions. Continuous variables with normal distributions were expressed as median and interquartile range, while categorical variables are represented as numbers and percentages. Statistical significance was assessed using the Mann–Whitney U test for continuous variables and the chi-square test (χ²) for categorical variables. Results: The median satisfaction score was 3.00 (IQR: 2.00, 3.00), providing insights into user satisfaction. 42.7% believed AI-powered chatbots adequately addresses healthcare concerns. Comfort levels in sharing health information and confidence (accuracy, reliability) had median scores of 3.00 (IQR: 2.00, 4.00) and 3.00 (IQR: 2.00, 3.00), respectively, on a scale of 1 to 5. 31.2% were willing to try AI in their next consultation, 40.4% were unsure, and 28.4% declined. Notably, 64.5% preferred interacting with a human healthcare provider, and 46.0% expressed that their comfort with AI use depended on the specific healthcare concern. Conclusion: The current findings shed light on the importance of understanding the capabilities of ChatGPT. Further research needs to be carried out to better understand how ChatGPT can be implemented in the current era.
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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 | 0.006 | 0.040 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".