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A Transparency System for ICU Using Machine Learning and AI
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Author(s): Pancham Singh (Ajay Kumar Garg Engineering College, Ghaziabad, India), Mrignainy Kansal (Ajay Kumar Garg Engineering College, Ghaziabad, India), Shirshendu Lahiri (Ajay Kumar Garg Engineering College, Ghaziabad, India), Harshit Vishnoi (Ajay Kumar Garg Engineering College, Ghaziabad, India)and Lakshay Mittal (Ajay Kumar Garg Engineering College, Ghaziabad, India)
Copyright: 2024
Pages: 19
Source title:
Enhancing Medical Imaging with Emerging Technologies
Source Author(s)/Editor(s): Avinash Kumar Sharma (Sharda University, India), Nitin Chanderwal (University of Cincinnati, USA), Shobhit Tyagi (Sharda University, India), Prashant Upadhyay (Sharda University, India)and Amit Kumar Tyagi (National Institute of Fashion Technology, New Delhi, India)
DOI: 10.4018/979-8-3693-5261-8.ch004
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Abstract
Patients in ICUs risk death. Years of opacity, miscommunication, and lack of real-time oversight have compounded medical errors and damaged stakeholder trust in this vital situation. The new ICU transparency system uses AI and deep learning to fix these concerns. Healthcare providers and patients face many unknowns. Medication errors, unmonitored vital signs, and lack of real-time medical data have harmed patient care and confidence. The ICU transparency system handles them well. This novel method offers real-time monitoring, accurate medication recording, and transparency. Guardians and healthcare providers can quickly access patient data for decisions. Vital sign analysis employing AI-driven algorithms detects health issues early. A transparent, collaborative, error-reducing healthcare environment boosts confidence and saves lives. The authors revisit systemic issues and the AI-powered critical care transformation approach in this study.
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