Get started
Work

Artificial intelligence in esophageal cancer: a narrative review from a thoracic surgical perspective

Year: 2026
Type: review
Abstract: Background: Artificial intelligence (AI) has potential applications across the esophageal cancer (EC) care pathway, but reported retrospective performance often has not translated into clinically validated decision support. We review the evidence through the lens of thoracic surgical decisions, where the consequences of prediction errors are asymmetric. Scope: This narrative review examines AI studies relevant to thoracic surgical decisions, with an emphasis on esophageal squamous cell carcinoma (ESCC). Selected reports were mapped to clinical tasks, analysis units, and validation settings. Supplementary PubMed and Web of Science searches informed the review; studies were selected purposively to illustrate clinical tasks and validation problems, not to represent the entire field. Key observations: A randomized endoscopy trial supports improved lesion detection in its study setting. Selected retrospective studies report external testing for nodal risk or pathological response, whereas surgical video and complication models mainly provide internal evaluation. Image-level results, patient-level predictions, and clinical outcomes are distinct. Calibration and prospective pathway evaluation remain necessary before treatment allocation can be based on model output. Conclusions: AI in EC should presently be considered an investigational decision-support layer rather than a replacement for endoscopic, imaging-based, or multidisciplinary assessment. Translation requires patient-level data partitioning, transparent reporting of calibration and clinical utility, independent external validation, and prospective studies using prespecified decision thresholds.
DOI: 10.3389/fonc.2026.1917622
Language: English
Open Access status: gold
Estimated APC paid: $3,801
OpenAlex ID: W7223030382
FWCI:
Cites: 87
Cited by:
Related to: 10