Challenges and directions for digital twin implementation in otorhinolaryngology.

Artificial intelligence Digital twin Otorhinolaryngology Personalized medicine Prediction

Journal

European archives of oto-rhino-laryngology : official journal of the European Federation of Oto-Rhino-Laryngological Societies (EUFOS) : affiliated with the German Society for Oto-Rhino-Laryngology - Head and Neck Surgery

ISSN: 1434-4726

Titre abrégé: Eur Arch Otorhinolaryngol

Pays: Germany

ID NLM: 9002937

Informations de publication

Date de publication:
04 May 2024

Historique:

received: 12 02 2024

accepted: 05 04 2024

medline: 5 5 2024

pubmed: 5 5 2024

entrez: 4 5 2024

Statut: aheadofprint

Résumé

Digital twin technology heralds a transformative era in Otorhinolaryngology (ORL), merging the physical and digital worlds to offer dynamic, virtual models of physical entities or processes. These models, capable of simulating, predicting, and optimizing real-world counterparts, are evolving from static replicas to intelligent, adaptive systems. Fueled by advancements in communication, sensor technology, big data analytics, Internet of Things (IoT), and simulation technologies, artificial intelligence (AI), digital twins in ORL promise personalized treatment planning, virtual experimentation, and therapeutic intervention optimization. Despite their potential, the integration of digital twins in ORL faces challenges including data privacy and security, data integration and interoperability, computational demands, model validation and accuracy, ethical and regulatory considerations, patient engagement, and cost and accessibility issues. Overcoming these challenges requires robust data protection measures, seamless data integration, substantial computational resources, rigorous validation studies, ethical transparency, patient education, and making the technology accessible and affordable. Looking ahead, the future of digital twins in ORL is bright, with advancements in AI and machine learning, omics data integration, real-time monitoring, virtual clinical trials, patient empowerment, seamless healthcare integration, longitudinal data analysis, and collaborative research. These developments promise to refine diagnostic and treatment strategies, enhance patient care, and facilitate more efficient and tailored ORL research, ultimately leading to more effective and personalized ORL management.

Sections du résumé

BACKGROUND BACKGROUND

Digital twin technology heralds a transformative era in Otorhinolaryngology (ORL), merging the physical and digital worlds to offer dynamic, virtual models of physical entities or processes.

PURPOSE OBJECTIVE

These models, capable of simulating, predicting, and optimizing real-world counterparts, are evolving from static replicas to intelligent, adaptive systems.

METHODS METHODS

Fueled by advancements in communication, sensor technology, big data analytics, Internet of Things (IoT), and simulation technologies, artificial intelligence (AI), digital twins in ORL promise personalized treatment planning, virtual experimentation, and therapeutic intervention optimization. Despite their potential, the integration of digital twins in ORL faces challenges including data privacy and security, data integration and interoperability, computational demands, model validation and accuracy, ethical and regulatory considerations, patient engagement, and cost and accessibility issues.

RESULTS RESULTS

Overcoming these challenges requires robust data protection measures, seamless data integration, substantial computational resources, rigorous validation studies, ethical transparency, patient education, and making the technology accessible and affordable. Looking ahead, the future of digital twins in ORL is bright, with advancements in AI and machine learning, omics data integration, real-time monitoring, virtual clinical trials, patient empowerment, seamless healthcare integration, longitudinal data analysis, and collaborative research.

CONCLUSION CONCLUSIONS

These developments promise to refine diagnostic and treatment strategies, enhance patient care, and facilitate more efficient and tailored ORL research, ultimately leading to more effective and personalized ORL management.

Identifiants

DOI: 10.1007/s00405-024-08662-5 PMID: 38703196

pubmed: 38703196

doi: 10.1007/s00405-024-08662-5

pii: 10.1007/s00405-024-08662-5

doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

Informations de copyright

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