THE ABILITY OF ARTIFICIAL INTELLIGENCE CHATBOTS ChatGPT AND GOOGLE BARD TO ACCURATELY CONVEY PREOPERATIVE INFORMATION FOR PATIENTS UNDERGOING OPHTHALMIC SURGERIES
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
INTRODUCTION: To determine whether the two popular artificial intelligence chatbots, ChatGPT and Bard, can provide high-quality information concerning procedure description, risks, benefits, and alternatives of various ophthalmic surgeries. METHODS: ChatGPT and Bard were prompted with questions pertaining to the description, potential risks, benefits, alternatives, and implications of not proceeding with various surgeries in different subspecialties of ophthalmology. Six common ophthalmic procedures were included in the authors' analysis. Two comprehensive ophthalmologists and one subspecialist graded each response independently using a 5-point Likert scale. RESULTS: Likert grading for accuracy was significantly higher for ChatGPT in comparison with Bard (4.5 ± 0.6 vs. 3.8 ± 0.8, P < 0.0001). Generally, ChatGPT performed better than Bard even when questions were stratified by the type of ophthalmic surgery. There was no significant difference between ChatGPT and Bard for response length (2,104.7 ± 271.4 characters vs. 2,441.0 ± 633.9 characters, P = 0.12). ChatGPT responded significantly slower than Bard (46.0 ± 3.0 vs. 6.6 ± 1.2 seconds, P < 0.0001). CONCLUSION: Both ChatGPT and Bard may offer accessible and high-quality information relevant to the informed consent process for various ophthalmic procedures. Nonetheless, both artificial intelligence chatbots overlooked the probability of adverse events, hence limiting their potential and introducing patients to information that may be difficult to interpret.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it