The application of artificial intelligence in tissue repair and regenerative medicine related to pediatric and congenital heart surgery: a narrative review
Bibliographic record
Abstract
Artificial intelligence and machine learning have the potential to revolutionize tissue repair and regenerative medicine in the field of pediatric and congenital heart surgery. Artificial intelligence is increasingly being recognized as a transformative force in healthcare with its ability to analyse large and complex datasets, predict surgical outcomes, and improve surgical education and training with the use of virtual reality and surgical simulators. This review explores the current applications of artificial intelligence in predicting surgical outcomes, improving peri-operative decision-making, and facilitating training for surgeons, particularly in low-income countries. By leveraging advanced algorithms and simulations, artificial intelligence can analyse intricate patient data and anatomical variations, enabling early detection of congenital heart defects and optimising surgical approaches. Ultimately, while barriers such as inconsistent data quality and limited resources remain, the advancement of artificial intelligence technologies offers a promising avenue to enhance regenerative medicine related to patient care and surgical education in pediatric and congenital heart surgery.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".