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Updated: Sep 10, 2025

Implementation of a Real-Time Psychosis Risk Detection and Alerting System Based on Electronic Health Records using CogStack
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Implementation of a Real-Time Psychosis Risk Detection and Alerting System Based on Electronic Health Records using CogStack

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AIと電子医療記録に基づく初等医療の診断予測モデル:体系的なレビュー

Liesbeth Hunik1, Asma Chaabouni1, Twan van Laarhoven2

  • 1Department of Primary and Community Care, Research Institute for Medical Innovation, Radboudumc, Geert Grooteplein Zuid 21, Nijmegen, 6525 GA, The Netherlands, 31 243618181.

JMIR medical informatics
|August 22, 2025
PubMed
まとめ

電子医療記録を使用する人工知能モデルは,初等医療診断に有望ですが,さらなる開発が必要です. 現在のAIモデルはバイアスのリスクが高く 臨床使用にはまだ準備ができていません

キーワード:
AI についてAIベースの診断EHR について適用性について人工知能評価ツール意思決定電子医療記録プライマリケア体系的なレビュー

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The issues and trends in healthcare delivery are constantly changing. The COVID-19 pandemic is one recent issue that wreaked havoc on healthcare systems, causing a shortage of healthcare workers, high demand for medicines and supplies, and increased medical expenditure due to a lack of insurance. Other issues include rising healthcare costs and care fragmentation.
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In healthcare diagnostics, laboratory tests play a crucial role in identifying and diagnosing a wide range of medical conditions. However, interpreting test results is not always straightforward. An abnormal test result does not always confirm the presence of a disease, just as a normal result does not guarantee its absence. To assess the reliability of these diagnostic tools, healthcare practitioners rely on two key statistical indicators: sensitivity and specificity.
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