Utilizing Artificial Intelligence and Chat Generative Pretrained Transformer to Answer Questions About Clinical Scenarios in Neuroanesthesiology
Bibliographic record
Abstract
OBJECTIVE: We tested the ability of chat generative pretrained transformer (ChatGPT), an artificial intelligence chatbot, to answer questions relevant to scenarios covered in 3 clinical guidelines, published by the Society for Neuroscience in Anesthesiology and Critical Care (SNACC), which has published management guidelines: endovascular treatment of stroke, perioperative stroke (Stroke), and care of patients undergoing complex spine surgery (Spine). METHODS: Four neuroanesthesiologists independently assessed whether ChatGPT could apply 52 high-quality recommendations (HQRs) included in the 3 SNACC guidelines. HQRs were deemed present in the ChatGPT responses if noted by at least 3 of the 4 reviewers. Reviewers also identified incorrect references, potentially harmful recommendations, and whether ChatGPT cited the SNACC guidelines. RESULTS: The overall reviewer agreement for the presence of HQRs in the ChatGPT answers ranged from 0% to 100%. Only 4 of 52 (8%) HQRs were deemed present by at least 3 of the 4 reviewers after 5 generic questions, and 23 (44%) HQRs were deemed present after at least 1 additional targeted question. Potentially harmful recommendations were identified for each of the 3 clinical scenarios and ChatGPT failed to cite the SNACC guidelines. CONCLUSIONS: The ChatGPT answers were open to human interpretation regarding whether the responses included the HQRs. Though targeted questions resulted in the inclusion of more HQRs than generic questions, fewer than 50% of HQRs were noted even after targeted questions. This suggests that ChatGPT should not currently be considered a reliable source of information for clinical decision-making. Future iterations of ChatGPT may refine algorithms to improve its reliability as a source of clinical information.
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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 | 0.111 | 0.453 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".