Advancing lung transplantation through machine learning and artificial intelligence
Bibliographic record
Abstract
PURPOSE OF REVIEW: To explore the current applications of artificial intelligence and machine learning in lung transplantation, including outcome prediction, drug dosing, and the potential future uses and risks as the technology continues to evolve. RECENT FINDINGS: While the use of artificial intelligence (AI) and machine learning (ML) in lung transplantation is relatively new, several groups have developed models to predict short-term outcomes, such as primary graft dysfunction and time-to-extubation, as well as long-term outcomes related to survival and chronic lung allograft dysfunction. Additionally, drug dosing models for Tacrolimus levels have been designed, demonstrating proof of concept for modelling treatment as a time-series problem. SUMMARY: The integration of ML models with clinical decision-making has shown promise in improving post-transplant survival and optimizing donor lung utilization. As technology advances, the field will continue to evolve, with enhanced datasets supporting more sophisticated ML models, particularly through real-time monitoring of biological, biochemical, and physiological data.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".