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Introduction <p>Artificial intelligence (AI) is rapidly transforming healthcare, with increasing applications in surgical evaluation. In breast cancer surgery, achieving aesthetic symmetry is essential for patient satisfaction and emotional well-being. While human evaluation remains fundamental, AI-driven symmetry assessment promises objective alternatives. This study evaluates the performance of publicly available AI models in breast symmetry assessment and compares them with Pyolo8, a custom AI model developed by the authors. Additionally, the study explores the potential emotional impact and ethical considerations of AI-generated assessments in postoperative breast cancer patients.</p> Methods <p>Sixty-eight patients who underwent breast reconstruction were evaluated with the use of publicly available AI models and contrasted with an AI model developed by the authors named Pyolo8. All results were evaluated by human observers.</p> Results <p>ChatGPT 4o and Pyolo8 AI models showed statistically significant moderate to strong positive correlation for postoperative assessment when compared to human observers. Direct interaction between AI models and patients was censored due to concerns of misinterpretation.</p> Conclusions <p>Both ChatGPT and Pyolo8 showed moderate to strong correlation&#xa0;with humans, but ChatGPT demonstrated superior communication skills. However, AI systems may lack the subtlety and empathy required for direct patient interactions, as vulnerable postoperative patients receiving an AI-generated symmetry assessment without appropriate clinical context may experience emotional distress or misinterpret the results. Human oversight and empathetic communication remain essential to ensure quality care while AI is increasingly integrated into medicine.</p> Level of Evidence IV <p>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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Evaluating Surgical Results in Breast Cancer with Artificial Intelligence

  • 期刊论文

  • 发表日期：

    2025年5月27日

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  • Nitzan Kenig,
  • Javier Monton Echeverria,
  • Aina Muntaner Vives

摘要

Introduction

Artificial intelligence (AI) is rapidly transforming healthcare, with increasing applications in surgical evaluation. In breast cancer surgery, achieving aesthetic symmetry is essential for patient satisfaction and emotional well-being. While human evaluation remains fundamental, AI-driven symmetry assessment promises objective alternatives. This study evaluates the performance of publicly available AI models in breast symmetry assessment and compares them with Pyolo8, a custom AI model developed by the authors. Additionally, the study explores the potential emotional impact and ethical considerations of AI-generated assessments in postoperative breast cancer patients.

Methods

Sixty-eight patients who underwent breast reconstruction were evaluated with the use of publicly available AI models and contrasted with an AI model developed by the authors named Pyolo8. All results were evaluated by human observers.

Results

ChatGPT 4o and Pyolo8 AI models showed statistically significant moderate to strong positive correlation for postoperative assessment when compared to human observers. Direct interaction between AI models and patients was censored due to concerns of misinterpretation.

Conclusions

Both ChatGPT and Pyolo8 showed moderate to strong correlation with humans, but ChatGPT demonstrated superior communication skills. However, AI systems may lack the subtlety and empathy required for direct patient interactions, as vulnerable postoperative patients receiving an AI-generated symmetry assessment without appropriate clinical context may experience emotional distress or misinterpret the results. Human oversight and empathetic communication remain essential to ensure quality care while AI is increasingly integrated into medicine.

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.