[Artificial intelligence-enabled full-course diagnosis and treatment for patients with severe burns and wounds requiring repair: from risk prediction to causal inference].

The diagnosis and treatment of severe burns and complex wounds require the coordinated management of local tissue injury, systemic pathophysiological changes, and sequential clinical interventions. Existing diagnostic and treatment pathways can support bedside evidence-based decision-making and allow clinicians to adjust treatment as a patient's condition evolves. However, limitations remain in the consistency of prehospital and primary-care assessments, the longitudinal integration of multimodal information, and the use of risk prediction results to support clinical decisions. Based on the clinical requirements of severe burn and complex wound care and the available evidences form artificial intelligence studies, this expert commentary examines the capability boundaries of current care pathways, the potential clinical benefits for different users, and the conditions required to extend risk prediction to constrained causal inference. We propose a full-course dynamic diagnostic and treatment framework comprising wound assessment, systemic monitoring, and clinical intervention. Standardized image analysis is used to assess wound area, depth, necrosis, infection, and healing trajectories. Time-series models integrate vital signs, laboratory measurements, inflammatory and infectious markers, metabolic and coagulation status, and organ function to continuously monitor the patient's systemic condition. At the same time, clinical interventions, including fluid resuscitation, anti-infective treatment, debridement and wound coverage, nutritional support, and rehabilitation, are aligned along a unified timeline to preserve the temporal relationships among changes in patient status, intervention timing, and clinical outcomes. Risk prediction is used to estimate potential clinical outcomes that may occur under the existing diagnostic and treatment pathways, whereas counterfactual simulation must be based on explicit causal assumptions, applicability conditions, uncertainty estimates, and boundaries of responsibility, and its results cannot be used directly as treatment prescriptions. The clinical value of artificial intelligence should be established through prospective studies that directly compare the effects of clinicians' independent judgments with those of their artificial intelligence-assisted judgments. In addition to discrimination, evaluation should include assessment time, inter-clinician agreement, calibration, warning lead time, alert and workflow burden, changes in clinical decisions, patient safety, and clinical outcomes. Addressing the capability boundaries of current care pathways, intelligent wound assessment, systemic risk monitoring, causal inference, and translational implementation and governance, this article discusses the pathways and boundaries of artificial intelligence integration into full-course diagnosis and treatment for patients with severe burns and wounds requiring repair.