Evaluating ChatGPT-4omni in paediatric developmental screening: direct versus sequential prompts.
Pathrada Traipidok1, Papichaya Srisombundit1, Nattaporn Tassanakijpanich1
1Prince of Songkla University Faculty of Medicine, Hat Yai, Songkhla, Thailand.
BMJ Paediatrics Open
|March 3, 2025
Summary
Large Language Models like ChatGPT-4o show promise for paediatric developmental screening. Direct Prompting was more sensitive, while Sequential Prompting was more specific in this study.
Area of Science:
- Pediatric Healthcare Technology
- Artificial Intelligence in Medicine
- Developmental Psychology
Background:
- Developmental screening is crucial for early intervention in children.
- Current screening methods can be time-consuming and resource-intensive.
- Large Language Models (LLMs) offer potential for innovative healthcare solutions.
Purpose of the Study:
- To evaluate the efficacy of ChatGPT-4o for paediatric developmental screening.
- To compare Direct and Sequential Prompting methods using ChatGPT-4o.
- To benchmark ChatGPT-4o's performance against the Bayley Scales of Infant Development, Third Edition.
Main Methods:
- 106 paediatric cases were analyzed.
- ChatGPT-4o was employed using Direct and Sequential Prompting techniques.
- Performance metrics included sensitivity, specificity, and overall accuracy.
Main Results:
- Direct Prompting achieved 73.42% sensitivity and 69.81% overall accuracy.
- Sequential Prompting demonstrated 62.96% specificity and 67.92% overall accuracy.
- Both LLM prompting methods showed potential in developmental screening.
Conclusions:
- ChatGPT-4o, via Direct and Sequential Prompting, can enhance paediatric developmental screening efficiency and accessibility.
- Direct Prompting exhibited higher sensitivity, whereas Sequential Prompting offered greater specificity.
- Further research is warranted to optimize LLM integration in clinical practice.
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