The illusion of competence: Evaluating the clinical reasoning of large language models in pediatric gastroenterology

To evaluate the diagnostic accuracy and clinical reasoning of three frontier large language models (LLMs) across standardized pediatric gastroenterology, hepatology, and nutrition (PGHN) clinical vignettes.

In this cross-sectional study, 25 fictional PGHN vignettes were developed by one board-certified pediatric gastroenterologist and evaluated using three LLMs: Gemini 3.1 Pro, ChatGPT 5.4 Thinking, and Claude Sonnet 4.6 Extended. A conditional two-step prompting protocol was applied. Three blinded, PGHN-certified co-authors independently scored responses using a structured instrument covering four domains (diagnostic accuracy, management, patient safety, reference quality; 0-2 each) and a global quality of clinical reasoning (QCR) score (1-5 Likert scale). Interobserver reliability was assessed using the intraclass correlation coefficient (ICC). Group differences were analyzed using the Kruskal-Wallis H test with Dunn post-hoc correction.

Interobserver reliability was excellent (ICC: 0.98 for domains; 0.87 for QCR). All three models achieved perfect diagnostic accuracy (median 2.00, interquartile range: 2.00-2.00). Statistically significant intermodel differences were identified in reference quality (p = 0.048) and QCR (p = 0.049). Claude achieved significantly higher QCR scores than ChatGPT (p = 0.043) and demonstrated the highest overall reference quality. Qualitative analysis revealed critical pharmacological dosing errors, contextual blindness, temporal obsolescence, and a high frequency of fabricated citations.

While current LLMs demonstrate good diagnostic pattern recognition in PGHN, reproducible and potentially life-threatening failures in pharmacological reasoning and reference accuracy create a dangerous illusion of competence. These findings suggest that LLMs may support differential diagnosis brainstorming in PGHN but do not establish their safety in real-world clinical scenarios.