Challenges for implementing generative artificial intelligence (GenAI) into clinical healthcare.

Generative artificial intelligence (GenAI) is a form of deep learning AI based on inference that offers significant potential in healthcare. It has versatile capabilities: GenAI excels in complex human language communication, synthesising information from large and diverse datasets and performing br...

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Publicado en:Internal Medicine Journal Vol. 55; no. 7; pp. 1063 - 1070
Autores principales: Roberts, Lynden J., Jayasena, Rajiv, Khanna, Sankalp, Arnott, Leslie, Lane, Paul, Bain, Chris
Formato: review tables/charts Journal Article
Publicado: Wiley-Blackwell Jul2025
Acceso en línea:Ver este registro en EBSCOhost
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      dt: Jul2025
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      pub: Wiley-Blackwell
      place: Malden, Massachusetts
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        atl: Challenges for implementing generative artificial intelligence (GenAI) into clinical healthcare.
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        au:
          Roberts, Lynden J.
          Jayasena, Rajiv
          Khanna, Sankalp
          Arnott, Leslie
          Lane, Paul
          Bain, Chris
        affil: Department of Clinical Informatics, Monash Health, Melbourne Victoria,, Australia
      sug:
        subj:
          Artificial Intelligence, Generative Utilization
          Health Care Delivery
          Implementation Science Methods
          Decision Support Systems, Clinical Utilization
          Quality Assessment
          Data Security
          Workforce Education
          Ethics
          Trust
      ab: Generative artificial intelligence (GenAI) is a form of deep learning AI based on inference that offers significant potential in healthcare. It has versatile capabilities: GenAI excels in complex human language communication, synthesising information from large and diverse datasets and performing broad, complex tasks reliably. Other important capabilities include scalability, 'always on' and cost effectiveness. Taken together, GenAI technology appears to possess considerable potential for healthcare. However, the implementation poses several challenges, including technological problems, regulatory considerations, workforce impact and building trust. Using evidence and expert opinion to explore these issues, the review aims to inform clinical experts about this rapidly evolving field.
      pubtype: Academic Journal
      doctype:
        review
        tables/charts
        Journal Article
      ougenre: Article
    language: English
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