The growing sophistication of Artificial Intelligence (AI) and machine learning technologies presents exciting possibilities for advancements in healthcare diagnostics and monitoring. This paper explores our research activities at the Augmented Reality for Health Monitoring Laboratory (ARHeMLab) at the Università di Napoli Federico II. The focus is on our integration of AI, machine learning, and augmented reality technologies to improve healthcare practices. Our research encompasses a broad spectrum of areas. We are developing advanced EEG-based systems for real-time monitoring of cognitive function. Additionally, we are investigating the application of machine learning algorithms to enhance the accuracy of blood perfusion assessment during laparoscopic surgeries. Furthermore, we are exploring the potential of AI to personalise non-invasive treatments like transcranial Electrical Stimulation (tES) for neurological conditions. This paper outlines our core research areas, the methodologies we employ, and the potential impact of our work on improving healthcare practices. By presenting our current projects and initiatives, the paper illustrates ARHeMLab’s commitment to advancing medical technology. Ultimately, our goal is to enhance patient outcomes and contribute to a more responsive healthcare system.

Advancing Healthcare Through AI: Innovations in Monitoring and Diagnostic Technologies at the Augmented Reality for Health Monitoring Laboratory (ARHeMLab)

Raimo S.
2024-01-01

Abstract

The growing sophistication of Artificial Intelligence (AI) and machine learning technologies presents exciting possibilities for advancements in healthcare diagnostics and monitoring. This paper explores our research activities at the Augmented Reality for Health Monitoring Laboratory (ARHeMLab) at the Università di Napoli Federico II. The focus is on our integration of AI, machine learning, and augmented reality technologies to improve healthcare practices. Our research encompasses a broad spectrum of areas. We are developing advanced EEG-based systems for real-time monitoring of cognitive function. Additionally, we are investigating the application of machine learning algorithms to enhance the accuracy of blood perfusion assessment during laparoscopic surgeries. Furthermore, we are exploring the potential of AI to personalise non-invasive treatments like transcranial Electrical Stimulation (tES) for neurological conditions. This paper outlines our core research areas, the methodologies we employ, and the potential impact of our work on improving healthcare practices. By presenting our current projects and initiatives, the paper illustrates ARHeMLab’s commitment to advancing medical technology. Ultimately, our goal is to enhance patient outcomes and contribute to a more responsive healthcare system.
2024
AI in Healthcare
Diagnostic Technologies
Patient Monitoring Systems
Precision Medicine
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/20.500.12317/101577
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