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

Lynden J Roberts1, Rajiv Jayasena2, Sankalp Khanna3

  • 1Department of Clinical Informatics, Monash Health, Melbourne, Victoria, Australia.

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Generative artificial intelligence (GenAI) offers significant healthcare potential through its advanced capabilities. However, successful implementation requires addressing key challenges in technology, regulation, workforce adaptation, and trust-building.

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Area of Science:

  • Artificial Intelligence
  • Deep Learning
  • Healthcare Technology

Background:

  • Generative artificial intelligence (GenAI) is a sophisticated deep learning AI.
  • GenAI demonstrates strong capabilities in natural language processing and data synthesis.
  • Its potential applications in healthcare are extensive, offering scalability and cost-effectiveness.

Purpose of the Study:

  • To explore the potential of GenAI in healthcare.
  • To identify and analyze the challenges associated with GenAI implementation in clinical settings.
  • To inform clinical experts on this evolving technology.

Main Methods:

  • Review of evidence and expert opinion.
  • Exploration of technological, regulatory, workforce, and trust-related issues.
  • Synthesis of current understanding and future considerations.

Main Results:

  • GenAI possesses versatile capabilities beneficial for healthcare, including advanced communication and data analysis.
  • Significant challenges impede widespread adoption, such as technical hurdles, regulatory frameworks, workforce integration, and establishing user trust.
  • The technology's potential is substantial but contingent on overcoming these implementation barriers.

Conclusions:

  • GenAI presents a transformative opportunity for the healthcare sector.
  • Addressing implementation challenges is crucial for realizing GenAI's full potential in medicine.
  • Continuous evaluation and expert guidance are necessary for navigating GenAI's integration into healthcare.