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

Abstract

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.

Keywords: Large language models; Artificial intelligence; ChatGPT; Cost; Patient education; Endoscopic submucosal dissection; Endoscopic mucosal resection

Core Tip: As large language models increasingly permeate medical workflows, this study offers insight into 3 areas: Performance parity, cost democratization, and clinical readiness. Perhaps one potentially compelling finding was cost efficiency. 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 scans. This is an important limitation in a domain like endoscopy, which often can be driven visually. Large language model performance can drop precipitously when deprived of image context. Without multimodal integration, artificial intelligence tools may inevitably fail to capture key diagnostic signals.