ReviewZhongguo xiu fu chong jian wai ke za zhi = Zhongguo xiufu chongjian waike zazhi = Chinese journal of reparative and reconstructive surgery2026
[Current applications and prospects of artificial intelligence in burn diagnosis and treatment].
Review in Zhongguo xiu fu chong jian wai ke za zhi = Zhongguo xiufu chongjian waike zazhi = Chinese journal of reparative and reconstructive surgery, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
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0 citing papers in PubMed.
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Authors and funding
2 authors.
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Abstract
Objective: To summarize the current research status of artificial intelligence (AI) in burn diagnosis and treatment, and to anticipate its future development direction. Methods: Research literature on the application of AI in burn diagnosis and treatment both domestically and internationally in recent years was reviewed. The progress of AI application in various stages of burn diagnosis and treatment was summarized, and the challenges in its clinical application were analyzed. Results: AI has demonstrated its value in improving diagnosis and treatment efficiency and prediction accuracy in multiple aspects, including burn wound diagnosis and severity assessment, perioperative decision making and precise intervention, personalized rehabilitation, and prognosis prediction. However, it also faces challenges related to data, models, and clinical translation. Conclusion: AI exhibits significant potential in burn diagnosis and treatment. In the future, it is necessary to address existing challenges such as the scarcity of data, privacy risks, insufficient interpretability, and clinical translation barriers, and promote its application in clinical practice.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.