Artificial intelligence enhances the management of esophageal squamous cell carcinoma in the precision oncology era

Figure 1 Applications of artificial intelligence in esophageal squamous cell carcinoma. CT: Computed tomography; PET: Positron emission tomography.

Figure 2 Challenges and opportunities of artificial intelligence in the management of esophageal squamous cell carcinoma. A: Examples of challenges and opportunities encountered by artificial intelligence (AI) in the field of esophageal squamous cell carcinoma; B: The fundamental architecture of federated learning (FL) in medical applications. FL integrates data from multiple healthcare institutions, enabling collaborative modeling. Initially, a central server establishes a base model and communicates the model's structure and parameters to each healthcare institution. Institutions perform local training using their respective data and return the results to the central server. Through the aggregation of data flows from various institutions, a high-quality shared global model is constructed; C: Brief overview of the integration of multimodal data. Multimodal AI consolidates and analyzes audio, image, and text information to build high-precision models. AI: Artificial intelligence.