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Purpose <p>To design and develop an AI-based plastic surgery recommendation system using 3D photographs and psychological questionnaire surveys, aiming to provide personalized treatment solutions for the plastic surgery industry.</p> Methods <p>Based on artificial intelligence technology, this study utilized patients’ 3D photographs and psychological questionnaire results as training samples to construct a personalized AI-based plastic surgery recommendation system. This system comprehensively considers factors, such as patients’ anxiety levels, economic status, and psychological expectations. The study selected 5543 cases of plastic surgery outpatients aged 18–55 years, collected their 3D photographs and questionnaire data, and used these for AI system training. The software predicted treatment projects and compared them with doctors’ predictions to validate the system’s accuracy and patient satisfaction. Third-party doctors evaluated the system’s safety, ultimately developing an efficient and accurate plastic surgery recommendation system.</p> Results <p>The economic downturn in the post-COVID-19 era significantly impacted psychological health and the plastic surgery industry. Factors, such as age, education level, income, and gender, had significant effects on patients’ psychological state and treatment willingness. The AI system integrated patients’ psychological state, gender, income, and physical characteristics, providing personalized plastic surgery treatment suggestions and achieving a 93.25% patient satisfaction rate. These results indicate that the AI system offers comparable accuracy and safety to physicians while improving satisfaction, meaning that it could enhance clinical decision-making efficiency.</p> Conclusion <p>The AI-based personalized plastic surgery recommendation system offers an innovative solution for the industry, enhancing the accuracy of treatment suggestions and patient satisfaction, thereby promoting sustainable development. In the post-pandemic era, the plastic surgery industry should focus on patients’ physical, psychological, and economic factors to achieve personalized services.</p> Level of Evidence II <p>Experiment/New Technology. 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 <a href="http://www.springer.com/00266">www.springer.com/00266</a>.</p>

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Multimodal AI System for Plastic Surgery Diagnosis and Decision-Making Using Deep Learning of Psychological Questionnaires and Three-Dimensional Facial Data

  • 期刊论文

  • 发表日期：

    2026年2月9日

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  • Xiaohui Qiu,
  • Shuyue Chen,
  • Jiangjie Tang,
  • Shenghui Liao,
  • Qiuyang Chen,
  • Jianda Zhou

摘要

Purpose

To design and develop an AI-based plastic surgery recommendation system using 3D photographs and psychological questionnaire surveys, aiming to provide personalized treatment solutions for the plastic surgery industry.

Methods

Based on artificial intelligence technology, this study utilized patients’ 3D photographs and psychological questionnaire results as training samples to construct a personalized AI-based plastic surgery recommendation system. This system comprehensively considers factors, such as patients’ anxiety levels, economic status, and psychological expectations. The study selected 5543 cases of plastic surgery outpatients aged 18–55 years, collected their 3D photographs and questionnaire data, and used these for AI system training. The software predicted treatment projects and compared them with doctors’ predictions to validate the system’s accuracy and patient satisfaction. Third-party doctors evaluated the system’s safety, ultimately developing an efficient and accurate plastic surgery recommendation system.

Results

The economic downturn in the post-COVID-19 era significantly impacted psychological health and the plastic surgery industry. Factors, such as age, education level, income, and gender, had significant effects on patients’ psychological state and treatment willingness. The AI system integrated patients’ psychological state, gender, income, and physical characteristics, providing personalized plastic surgery treatment suggestions and achieving a 93.25% patient satisfaction rate. These results indicate that the AI system offers comparable accuracy and safety to physicians while improving satisfaction, meaning that it could enhance clinical decision-making efficiency.

Conclusion

The AI-based personalized plastic surgery recommendation system offers an innovative solution for the industry, enhancing the accuracy of treatment suggestions and patient satisfaction, thereby promoting sustainable development. In the post-pandemic era, the plastic surgery industry should focus on patients’ physical, psychological, and economic factors to achieve personalized services.

Level of Evidence II

Experiment/New Technology. 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.