The role of artificial intelligence in burn assessment, complication diagnosis, and outcome prediction: a narrative review
Bibliographic record
Abstract
Burn injury remains a major global health challenge, causing an estimated 180 000 deaths annually. The marked heterogeneity in burn severity, complications, and outcomes highlights the need for more objective and efficient evaluation strategies. Artificial intelligence (AI) has emerged as a promising approach to support clinical decision-making and improve patient care in this field. In this narrative review, we summarize the growing applications of AI in burn care, including the assessment of burn depth and total body surface area, monitoring of wound healing, prediction of postburn complications, and estimation of clinical outcomes. AI-based models have demonstrated strong performance in automating wound assessment, optimizing fluid resuscitation, and predicting complications such as sepsis, inhalation injury, and acute kidney injury. Furthermore, AI-driven prediction of mortality risk and hospital length of stay has shown potential to inform early interventions and improve resource allocation. Despite encouraging progress, most studies to date rely on small, single-center datasets and limited model validation, underscoring the need for larger, multi-institutional efforts, and standardized data sharing. Integrating AI into burn management holds great promise for enhancing diagnostic precision, forecasting outcomes, and personalizing treatment strategies. As these technologies advance, clinician familiarity and collaboration with AI tools will be critical to fully realize their potential in transforming burn care.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| 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 | 0.002 |
| 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".