aceReliable Detection, Unreliable Clinical Decision-making: Artificial Intelligence Models in Burn Assessment
Background: Multimodal artificial intelligence (AI) systems are increasingly used for visual interpretation tasks. Whether commercially available models can be applied to burn surgery, and to what extent, remains unclear. We evaluated a clinical chain from visual recognition (distinguishing burns from nonburned skin) to treatment decision-making (debridement versus immediate skin grafting), including accuracy, output consistency, and skin tone subgroup performance. Methods: Four multimodal large language models…
Four multimodal models distinguished burns from nonburned skin with 97.2%-100% accuracy, with equivalent performance across Fitzpatrick skin tone strata.
The same models gave unreliable treatment recommendations for burns (debridement versus immediate skin grafting), with accuracy of 24.8% to 72.8% and inconsistent outputs for identical images.
The study tested a small set of 50 cropped photographs and concluded that current applicability in burn surgery is limited, so results do not establish performance in real-world clinical decision-making.
Evidence
- Peer-reviewedPlastic and Reconstructive Surgery - Global Open2026-10-14
How should this claim be treated?
Truvace Impact Record TRV-2026-1394, v1: “Reliable Detection, Unreliable Clinical Decision-making: Artificial Intelligence Models in Burn Assessment.” Truvace, 2026-10-15. /record/TRV-2026-1394 (accessed at citation time). sha256 758dba5df4034da7…
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