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Future Healthcare in Generative AI with Real Metaverse
Stockholm University, Faculty of Social Sciences, Department of Computer and Systems Sciences.ORCID iD: 0000-0001-5924-5457
Stockholm University, Faculty of Social Sciences, Department of Computer and Systems Sciences.
Number of Authors: 32024 (English)In: Procedia Computer Science, E-ISSN 1877-0509, Vol. 251, no 2024, p. 487-493Article in journal (Refereed) Published
Abstract [en]

The Metaverse offers a simulated environment that could transform healthcare by providing immersive learning experiences through Internet applications and social forms that integrate a network of virtual reality environments. The Metaverse is expected to contribute to a new way of socializing, where users can enter a verse as avatars. The concept allows avatars to switch between verses seamlessly. Virtual Reality (VR) in healthcare has shown promise for social-skill training, especially for individuals with Autism Spectrum Disorder (ASD), and social challenge training for patients with Post-Traumatic Stress Disorder (PTSD) requiring adaptable support. The problem lies in the limited adaptability and functionality of existing Metaverse implementations for individuals with ASD and PTSD. While studies have explored various implementation ideas, such as VR platforms for training social skills, social challenge, and context-aware Augmented Reality (AR) systems for daily activities, many lack adaptability of user input and output. A proposed solution involves a context-aware system using AI, Large Language Models (LLMs), and generative agents to support independent living for individuals with ASD and a tool to enhance emotional learning with PTSD.

Place, publisher, year, edition, pages
2024. Vol. 251, no 2024, p. 487-493
Keywords [en]
Real Metaverse, Helathcare, Immersive, Leraning, AI, LLM, Edge Intelligence
National Category
Computer Engineering
Research subject
Computer and Systems Sciences
Identifiers
URN: urn:nbn:se:su:diva-237466DOI: 10.1016/j.procs.2024.11.137Scopus ID: 2-s2.0-85214986921OAI: oai:DiVA.org:su-237466DiVA, id: diva2:1924049
Available from: 2025-01-02 Created: 2025-01-02 Last updated: 2025-02-25Bibliographically approved

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Rahmani Chianeh, RahimWestin, Thomas

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