Cost vs clinical utility on application of large language models in clinical practice: A double-edged sword

As large language models increasingly permeate medical workflows, a recent study evaluating ChatGPT 4.0's performance in addressing patient queries about endoscopic submucosal dissection and endoscopic mucosal resection offers critical insights into three domains: Performance parity, cost democratization, and clinical readiness. The findings highlight ChatGPT's high accuracy, completeness, and comprehensibility, suggesting potential cost efficiency in patient education. Yet, cost-effectiveness alone does not ensure clinical utility. Notably, the study relied exclusively on text-based prompts, omitting multimodal data such as photographs or endoscopic scans. This is a significant limitation in a visually driven field like endoscopy, where large language model performance may drop precipitously without image context. Without multimodal integration, artificial intelligence tools risk failing to capture key diagnostic signals, underscoring the need for cautious adoption and robust validation in clinical practice.