The Role of Artificial Intelligence in Chronic Liver Diseases and Liver Transplantation
Bibliographic record
Abstract
BACKGROUND & AIMS: In hepatology, pattern recognition in laboratory data and clinical characteristics is the hallmark of clinical care. Artificial intelligence (AI) tools, like machine or deep learning and large language models, provide interesting mechanisms for facilitating care advancement. The complexity and diversity of data, as well as genetic, environmental, and lifestyle factors, all contribute to individualized recommendations intuitively made by clinicians for patients with liver disease. AI tools provide the opportunity to train on high-volume data and simulate the clinician's subconscious thought processes in decision making. With tremendous growth in hepatology-focused AI, critical efforts are needed to consider multicenter efforts and enabling collection of clean data that are as free as possible of bias. Prospective evaluation of AI tools seamlessly integrated into workflows, especially through clinical trials, as well as patient partner and clinical stakeholder engagement, will be key to building trust in the individualized predictions provided. This review delves into the AI literature in hepatology for diagnostic, prognostic, and therapeutic applications.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".