mlsanityData to 7 Oct 2026

DeepScore: A Comprehensive Approach to Measuring Quality in AI-Generated Clinical Documentation

arXiv 2409.16307 ↗First posted 10 Sep 2024cs.CL cs.AI stat.APcited by 2

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Abstract (arXiv)

Medical practitioners are rapidly adopting generative AI solutions for clinical documentation, leading to significant time savings and reduced stress. However, evaluating the quality of AI-generated documentation is a complex and ongoing challenge. This paper presents an overview of DeepScribe's methodologies for assessing and managing note quality, focusing on various metrics and the composite "DeepScore", an overall index of quality and accuracy. These methodologies aim to enhance the quality of patient care documentation through accountability and continuous improvement.

Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 2 of its 2 references that are in the map. First posted on arXiv 10 Sep 2024.

Builds on

2 of 2 references.

References · 2

  1. 2020Towards an Automated SOAP Note: Classifying Utterances from Medical Conversationscited by 10
  2. 2024Improving Clinical Note Generation from Complex Doctor-Patient Conversationcited by 7

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Citing papers per year: 2025: 1; 2026: 1.

2 papers.