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AI Research
arXiv CS.AI·May 26·5 min read
When Reasoning Hurts: Source-Aware Evaluation of Frontier LLMs for Clinical SOAP Note Generation
TL;DR
Existing reasoning-enabled large language models (LLMs) show strong performance on medical reasoning tasks, but their effectiveness in generating structured clinical documentation, like SOAP notes, is uncertain. This study evaluates models including GPT-5.4 and DeepSeek-V4-Flash using a source-aware benchmark to assess their performance in generating SOAP notes.
✦ Why It Matters
Engineers should consider task-specific evaluations when implementing reasoning in LLMs for clinical documentation.
Key Takeaways
- Evaluate LLMs with task-specific benchmarks for clinical applications.
- Consider non-reasoning configurations for better SOAP note quality.
- Understand that reasoning capabilities may not enhance all tasks.
- Use multiple evaluation metrics for comprehensive model assessment.
Full Summary
Large language models (LLMs) have shown strong performance in medical reasoning benchmarks, yet their application in generating structured clinical documentation, specifically SOAP notes, remains underexplored. This research investigates the effectiveness of models like GPT-5.4, DeepSeek-V4-Flash, and Gemma-4-E4B in generating SOAP notes from clinical dialogues using a source-aware benchmark that includes datasets from OMI Health, ACI-Bench, and PriMock57.
A controlled experimental design toggled between provider-native reasoning and retrieval-augmented generation (RAG) from the same source. Evaluation metrics included seven automatic measures and assessments from two reference-aware LLM judges.
Findings reveal that the non-reasoning configuration of GPT-5.4 achieved the highest overall quality, while DeepSeek-V4-Flash excelled among reasoning-enabled models. Notably, enabling reasoning in GPT-5.4 led to significant performance degradation across all datasets, suggesting that enhanced reasoning capabilities do not necessarily translate to improved accuracy in SOAP note generation.