Evidence map›Paper›PMID 42277421›Full record

ArticleAesthetic plastic surgery2026

Psychological Risk Assessment in Plastic Surgery via a DeepSeek Large Language Model: A Retrospective Cohort Study.

Yi-Dan Sun, Xiang-Wen Xu, Dan-Dan Liu, Yu-Zhou Long, Lin Luo, Meng-Fan Wu, Yan-Ting Ou, Yong-Bo Zhang, Yong-Yan Cui

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Article in Aesthetic plastic 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.

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1 · What the graph read from it

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5 · Who and what money

Authors and funding

9 authors.

Yi-Dan SunDepartment of Plastic Surgery, Peking University Shenzhen Hospital, Shenzhen, 518036, People's Republic of China.
Xiang-Wen XuDepartment of Plastic Surgery, Shenzhen Xinhua Hospital, Shenzhen, 518110, People's Republic of China.
Dan-Dan LiuDepartment of Plastic Surgery, Peking University Shenzhen Hospital, Shenzhen, 518036, People's Republic of China.
Yu-Zhou LongGuangdong Key Laboratory for Biomedical Measurements and Ultrasound Imaging, National-Regional Key Technology Engineering Laboratory for Medical Ultrasound, School of Biomedical Engineering, Shenzhen University Medical school, Shenzhen, 518060, People's Republic of China.
Lin LuoDepartment of Plastic Surgery, Peking University Shenzhen Hospital, Shenzhen, 518036, People's Republic of China.
Meng-Fan WuDepartment of Plastic Surgery, Peking University Shenzhen Hospital, Shenzhen, 518036, People's Republic of China.
Yan-Ting OuDepartment of Plastic Surgery, Shenzhen Xinhua Hospital, Shenzhen, 518110, People's Republic of China.
Yong-Bo ZhangGuangdong Key Laboratory for Biomedical Measurements and Ultrasound Imaging, National-Regional Key Technology Engineering Laboratory for Medical Ultrasound, School of Biomedical Engineering, Shenzhen University Medical school, Shenzhen, 518060, People's Republic of China. ybzhang.1@qq.com.
Yong-Yan CuiDepartment of Plastic Surgery, Peking University Shenzhen Hospital, Shenzhen, 518036, People's Republic of China. cyy8188@163.com.ORCID http://orcid.org/0000-0003-1400-1218

Funding

Guangdong Medical Science and Technology Research Foundation A2023237National Natural Science Foundation of China 82302810Natural Science Foundation Shenzhen Science and Technology Innovation Program JCYJ20220531094004010Peking University Shenzhen Hospital Research Project Fund LCYJ2020008Shenzhen Key Medical Discipline Construction Fund SZXK026
6 · The paper itself

Abstract

backgroundPreoperative psychological risk assessment is critical yet challenging in plastic surgery because of high rates of body dysmorphic disorder (BDD: 3.2-16.6%) and limited clinical screening tools. This study evaluated an artificial intelligence large language model (DeepSeek-LLM) for psychological risk stratification.

methodsIn this retrospective cohort study, preoperative questionnaires (14-item instrument assessing demographics, psychological status, and surgical expectations) and postoperative dispute data from 1826 patients were analyzed. Three blinded plastic surgeons scored patients' surgical risk via a 10-point visual analog scale (VAS). The concordance between physician scores and DeepSeek-R1 LLM-generated risk assessments was evaluated via Bland‒Altman analysis and Pearson correlation.

resultsPatients with BDD (12.0%, n = 24/200) and those experiencing postoperative disputes (2.4%, n = 40/1627) presented significantly elevated preoperative risk scores compared with controls (BDD: median 7.00 [6.0, 11.0] vs. 6.00 [3.0, 9.0], p = 0.027; disputes: 7.00 [5.3, 9.0] vs. 6.00 [3.0, 8.0], p = 0.015). "Satisfaction with previous surgical outcomes" and "surgical financial stress" emerged as the most significant risk factors for postoperative disputes. DeepSeek-generated surgical risk scores demonstrated strong concordance with physician evaluations (Pearson r = 0.883, p < 0.001), with 93.9% of the data points within the 95% limits of agreement (Bland‒Altman analysis).

conclusionsAccording to the results of this study, the DeepSeek can be a promising efficient tool for communication and assessment, generating profiles that detail patients' needs, psychology, and economic status. They can assist doctors in conducting more effective preoperative conversations and in assessing surgical risks to a certain extent. LEVEL OF EVIDENCE IV: This journal requires that authors assign a level of evidence to each article. For a full description of these evidence-based medicine ratings, please refer to the Table of Contents or the online Instructions to Authors www.springer.com/00266.

Indexed as

Body Dysmorphic DisordersPlastic Surgery ProceduresSurgery, PlasticAdolescentAdultCohort StudiesFemaleHumansLarge Language ModelsMaleMiddle AgedPreoperative CareRetrospective StudiesRisk AssessmentSurveys and QuestionnairesYoung AdultAI large language modelBody dysmorphic disorderPlastic surgery

Identifiers

PMID42277421

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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.