Ambient AI Scribing Support: Comparing the Performance of Specialized AI Agentic Architecture to Leading Foundational Models

• URL: http://arxiv.org/abs/2411.06713v1
• Date: Mon, 11 Nov 2024 04:45:48 GMT
• Title: Ambient AI Scribing Support: Comparing the Performance of Specialized AI Agentic Architecture to Leading Foundational Models
• Authors: Chanseo Lee, Sonu Kumar, Kimon A. Vogt, Sam Meraj,
• Abstract summary: Sporo Health's AI Scribe is a proprietary model fine-tuned for medical scribing. We analyzed de-identified patient transcripts from partner clinics, using clinician-provided SOAP notes as the ground truth. Sporo outperformed all models, achieving the highest recall (73.3%), precision (78.6%), and F1 score (75.3%) with the lowest performance variance.
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• Abstract: This study compares Sporo Health's AI Scribe, a proprietary model fine-tuned for medical scribing, with various LLMs (GPT-4o, GPT-3.5, Gemma-9B, and Llama-3.2-3B) in clinical documentation. We analyzed de-identified patient transcripts from partner clinics, using clinician-provided SOAP notes as the ground truth. Each model generated SOAP summaries using zero-shot prompting, with performance assessed via recall, precision, and F1 scores. Sporo outperformed all models, achieving the highest recall (73.3%), precision (78.6%), and F1 score (75.3%) with the lowest performance variance. Statistically significant differences (p < 0.05) were found between Sporo and the other models, with post-hoc tests showing significant improvements over GPT-3.5, Gemma-9B, and Llama 3.2-3B. While Sporo outperformed GPT-4o by up to 10%, the difference was not statistically significant (p = 0.25). Clinical user satisfaction, measured with a modified PDQI-9 inventory, favored Sporo. Evaluations indicated Sporo's outputs were more accurate and relevant. This highlights the potential of Sporo's multi-agentic architecture to improve clinical workflows.

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