Large Language Models in Gastroenterology and Gastrointestinal Surgery: A New Frontier in Patient Communication and Education
Bibliographic record
Abstract
When integrated into healthcare, large language models (LLMs) have transformative and revolutionary effects, including significant potential for improving patient care and streamlining clinical processes. However, one specialty that particularly requires data on LLM use is gastroenterology and gastrointestinal surgery, a gap we sought to address in our research. Advanced artificial intelligence (AI) systems like LLMs have demonstrated the ability to mimic human communication, assist in diagnosis, provide patient education, and support medical research simultaneously. Despite these advantages, challenges such as biases, data privacy concerns, and lack of transparency in decision-making remain critical. The role of regulations in mitigating these risks is widely debated, with proponents advocating for structured oversight to enhance trust and patient safety, while others caution against potential barriers to innovation. Rather than replacing human expertise, AI should be integrated thoughtfully to complement clinical decision-making. Ensuring a balanced approach requires collaboration between medical professionals, AI developers, and policymakers to optimize its responsible implementation in healthcare.
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 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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.002 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".