Background
The Pediatric Emergency Department (PED) faces significant challenges, such as high patient volumes, time-sensitive decisions, and complex diagnoses.
Large Language Models (LLMs) have the potential to enhance patient care; however, their effectiveness in supporting the diagnostic process remains uncertain, with studies showing mixed results regarding their impact on clinical reasoning. We aimed to assess LLM-based chatbots performance in realistic PED scenarios, and to explore their use as diagnosis-making assistants in pediatric emergency.
Methods
We evaluated the diagnostic effectiveness of 5 LLMs (ChatGPT-4o, Gemini 1.5 Pro, Gemini 1.5 Flash, Llama-3-8B, and ChatGPT-4o mini) compared to 23 physicians (including 10 PED physicians, 6 PED residents, and 7 Emergency Medicine residents).
Both LLMs and physicians had to provide one primary diagnosis and two differential diagnoses for 80 real-practice pediatric clinical cases from the PED of a tertiary care Children’s Hospital, with three different levels of diagnostic complexity.
The responses from both LLMs and physicians were compared to the final diagnoses assigned upon patient discharge; two independent experts evaluated the answers using a five-level accuracy scale. Each physician or LLM received a total score out of 80, based on the sum of all answer points.
Results
The best performing chatbots were ChatGPT-4o (score: 72.5) and Gemini 1.5 Pro (score: 62.75), the first performing better (p < 0.05) than PED physicians (score: 61.88).
Emergency Medicine residents performed worse (score: 43.75) than both the other physicians and chatbots (p < 0.01). Chatbots’ performance was inversely proportional to case difficulty, but ChatGPT-4o managed to match the majority of the correct answers even for highly difficult cases.
Discussion
ChatGPT-4o and Gemini 1.5 Pro could be a valid tool for ED physicians, supporting clinical decision-making without replacing the physician’s judgment.
Shared protocols for effective collaboration between AI chatbots and healthcare professionals are needed.
Questo è uno degli articoli scientifici pubblicati da uno o più collaboratori e data scientist di synbrAIn.
Se sei interessato a saperne di più, leggi l’intero articolo qui.
