Clinical Report: Evaluation of DeepSeek-R1 and GPT-4o for Pediatric Inquiry
Overview
This study compares the performance of DeepSeek-R1 and GPT-4o on 280 pediatric licensing exam questions across various subspecialties.
Background
The application of large language models (LLMs) in medicine has raised questions about their accuracy and reliability, particularly in specialized fields like pediatrics. As these models are increasingly used in clinical settings, understanding their performance in high-stakes environments is critical.
Data Highlights
Model
Per-Run Accuracy
Consistent Accuracy
Aggregate Accuracy
Consistency
DeepSeek-R1
Data not provided
Data not provided
Data not provided
Data not provided
GPT-4o
Data not provided
Data not provided
Data not provided
Data not provided
Key Findings
DeepSeek-R1 was evaluated against GPT-4o using 280 pediatric licensing exam questions.
Questions spanned 11 subspecialties and were categorized by clinical complexity.
Performance metrics included per-run accuracy, consistent accuracy, aggregate accuracy, and response repeatability.
Direct comparisons in pediatric contexts are limited.
Clinical Implications
The findings suggest that DeepSeek-R1 may be more effective in specific pediatric inquiries compared to GPT-4o. Clinicians and educators should consider the strengths of each model when integrating AI tools into pediatric training and decision-making.
Conclusion
This comparative evaluation highlights the performance of DeepSeek-R1 in pediatric applications.
