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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 documentation.
- Non-reasoning configurations may yield better results than reasoning-enabled ones.
- Consider model-dependent improvements when using retrieval-augmented generation.
- Challenge assumptions about reasoning capabilities enhancing document fidelity.
Full Summary
The research investigates how well reasoning-enabled large language models (LLMs) perform in generating SOAP notes, a structured format used in clinical documentation. Using a source-aware benchmark that includes datasets from OMI Health, ACI-Bench, and PriMock57, the study evaluates models like GPT-5.4, DeepSeek-V4-Flash, and Gemma-4-E4B.
A controlled experimental design toggles between provider-native reasoning and retrieval-augmented generation (RAG) from the same source. Results show that a non-reasoning configuration of GPT-5.4 achieves the highest quality, while DeepSeek-V4-Flash excels among reasoning-enabled models.
Notably, enabling reasoning degrades GPT-5.4's performance across all datasets, and improvements from RAG are model-dependent. These findings challenge the assumption that stronger reasoning capabilities enhance the fidelity of SOAP note generation, emphasizing the need for task-specific evaluations.