Use of artificial intelligence in the management of burns – Present and future
Bibliographic record
Abstract
Artificial intelligence (AI) is expanding its footprints across all our activities. The main tools of AI include machine learning, artificial neural networks, natural language processing, and computer vision. It has been used across various medical specialties, and there are numerous studies that have investigated and validated various tools of AI in clinical practice. Plastic surgery and its subspecialties like burns have always been in the forefront of surgical innovations. We discuss the various articles that have used AI tools for burn management. AI tools have been successfully used to calculate the TBSA and depth of burns from burn photographs. AI algorithms have been used in clinical decision-making in burn critical care like early recognition of sepsis and acute kidney injury and also prediction of need for ventilation. Artificial neural networks have looked at optimum serum concentrations of antibiotics in severe burn patients. There are tools incorporating AI to surgical robotics and surgical decision-making. AI has been extensively used in the prediction of burn mortality and length of stay calculations. The unique nature of injury in burn patients and the developments of AI till now in this field leave burn management in an exceptionally good position to harness the next phases of innovations in AI.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".