Construction of a multimodal artificial intelligence model for differentiating benign and malignant anterior mediastinal tumors.

ABSTRACT:

Background

Multimodal data fusion approaches not only capture the multidimensional characteristics of diseases but also compensate for the limitations of single-modality data in clinical diagnostics. These methods are increasingly utilized in clinical practice to significantly improve diagnostic accuracy. Artificial intelligence (AI), particularly in the domain of machine learning, has demonstrated a strong ability to extract meaningful features from large-scale datasets and is now widely applied in medical research. Differentiating benign from malignant anterior mediastinal tumors remains challenging in clinical settings. This study aimed to develop a multimodal AI model to accurately distinguish benign from malignant anterior mediastinal tumors.

Methods

We retrospectively

SUBMITTER: Zhang Y 

PROVIDER: S-EPMC12603461 | biostudies-literature | 2025 Oct

REPOSITORIES: biostudies-literature