Attitudes and perceptions of medical researchers towards the use of artificial intelligence chatbots in the scientific process: an international cross-sectional survey
Bibliographic record
Abstract
Chatbots are artificial intelligence (AI) programs designed to simulate conversations with humans that present opportunities and challenges in scientific research. Despite growing clarity from publishing organisations on the use of AI chatbots, researchers' perceptions remain less understood. In this international cross-sectional survey, we aimed to assess researchers' attitudes, familiarity, perceived benefits, and limitations related to AI chatbots. Our online survey was open from July 9 to Aug 11, 2023, with 61 560 corresponding authors identified from 122 323 articles indexed in PubMed. 2452 (4·0%) provided responses and 2165 (94·5%) of 2292 who met eligibility criteria completed the survey. 1161 (54·0%) of 2149 respondents were male and 959 (44·6%) were female. 1294 (60·5%) of 2138 respondents were familiar with AI chatbots, and 945 (44·5%) of 2125 had previously used AI chatbots in research. Only 244 (11·4%) of 2137 reported institutional training on AI tools, and 211 (9·9%) of 2131 noted institutional policies on AI chatbot use. Despite mixed opinions on the benefits, 1428 (69·7%) of 2048 expressed interest in further training. Although many valued AI chatbots for reducing administrative workload (1299 [66·9%] of 1941), there was insufficient understanding of the decision making process (1484 [77·2%] of 1923). Overall, this study highlights substantial interest in AI chatbots among researchers, but also points to the need for more formal training and clarity on their use.
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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 | 0.017 | 0.041 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".