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.

People tend to make snap judgments on each other in a single look and now an algorithm claims to have the same ability to determine trustworthiness for obtaining a loan in just two minutes. Tokyo-based DeepScore unveiled its facial and voice recognition app last week at the Consumer Electronics Show that is touted as a'next-generation scoring engine' for loan lenders, insurance companies and other financial institutions. While a customer answers 10 question, the AI analyzes their face and voice to calculate a'True Score' that can be help companies with the decision to deny or approve. DeepScore says its AI can determine lies with 70 percent accuracy and a 30 percent false negative rate, and will alert companies that fees need to be increased if dishonesty is detected. However, scientists raise concerns about bias saying the app is likely to discriminate against people with tics or anxiety, resulting in these individuals not receiving necessary funds or coverage, Motherboard reports.