JMIR Publications

ABSTRACT

Background:

Breast cancer is a leading global health concern necessitating advancements in recurrence prediction and management. The development of an Artificial intelligence-based clinical decision support system (AI-CDSS) using ChatGPT addresses this need with the aim of enhancing both prediction accuracy and user accessibility.

Objective:

The primary objective of this study was to develop and validate an advanced machine-learning model for a web-based AI-CDSS application, leveraging the question-and-answer guidance capabilities of ChatGPT to enhance data preprocessing and model development, thereby improving the prediction of breast cancer recurrence.

Methods:

This study focused on developing an advanced machine-learning model by leveraging data from the Tri-Service General Hospital breast cancer registry of 3,577 patients (2004–2016). ChatGPT is integral to preprocessing and model development, aiding in hormone receptor categorization, age binning, and one-hot encoding. Techniques such as the synthetic minority oversampling technique (SMOTE) address the imbalance of datasets. Various algorithms, including light gradient-boosting machine (LGBM), Gradient Boosting, and Extreme Gradient Boosting (XGB), were employed, and their performance was evaluated using metrics such as the area under the curve (AUC), accuracy, sensitivity, and F1-score.

Results:

The LGBM model demonstrated superior performance, with an AUC of 0.80, followed closely by the Gradient Boosting and XGB models. The web interface of the AI-CDSS tool was effectively tested in clinical decision-making scenarios, proving its utility in personalized treatment planning and patient involvement.

Conclusions:

The AI-CDSS tool, enhanced by ChatGPT, marks a significant advancement in breast cancer recurrence prediction, offering a more individualized and accessible approach for clinicians and patients. Although promising, further validation in diverse clinical settings is recommended to confirm its efficacy and expand its utility.