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 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 predictions to support clinical decisions. Based on the clinical requirements of severe burn and complex wound care and the available evidence on artificial intelligence, 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. Clinical interventions including fluid resuscitation, antimicrobial 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 estimates outcomes that may occur under the current care pathway, whereas counterfactual simulation requires explicit causal assumptions, clearly defined applicability conditions, uncertainty estimates, and accountability boundaries, and should not be directly interpreted as a treatment prescription. The clinical value of artificial intelligence should be established prospectively by comparing clinicians working independently with clinicians assisted by artificial intelligence. 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. The article discusses the pathway and boundaries of artificial intelligence-enabled full-course care from the perspectives of current pathway capabilities, intelligent wound assessment, systemic risk monitoring, causal inference, and translational implementation and governance.