The potential utility of CHATGPT4.0 as an AI assistant in the education and management of patients with Barrett’s esophagus
Bibliographic record
Abstract
Chat Generative Pre-trained Transformer (ChatGPT) has emerged as a new technology for physicians and patients to obtain medical information. Our aim was to assess the ability of ChatGPT 4.0 to deliver high-quality information in response to commonly asked questions and management recommendations for Barrett's esophagus (BE). Twenty-nine questions (14 clinical vignettes and 15 frequently asked questions (FAQ)) on BE were entered into ChatGPT 4.0. Using a 5-point Likert scale, three gastroenterologists with expertise in BE rated the 29 ChatGPT responses for accuracy, completeness, empathy, use of excessive medical jargon, and appropriateness to send to patients. Three separate gastroenterologists generated responses to the same 15 FAQs on BE. A group of blinded patients with BE evaluated both ChatGPT and gastroenterologist responses on quality, clarity, empathy and which of the two responses was preferred. Gastroenterologists rated ChatGPT responses as mostly accurate overall (4.01 out of 5) with 79.3% of responses completely accurate or mostly accurate with minor errors. When compared to gastroenterologist responses, the patient panel rated ChatGPT responses to be of significantly higher quality (4.42 vs. 3.07 out of 5) and empathy (4.33 vs. 2.55 out of 5) (p < 0.0001). In conclusion, ChatGPT 4.0 provides generally accurate and comprehensive information about BE. Patients expressed a clear preference for ChatGPT responses over those of gastroenterologists, finding responses from ChatGPT to be of higher quality and empathy. This study highlights the potential use of ChatGPT 4.0 as an adjunctive tool for physicians to provide real-time, high-quality information about BE to their patients.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.012 | 0.054 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 0.003 |
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".