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Accepted for/Published in: JMIR Medical Informatics

Date Submitted: Oct 18, 2023
Open Peer Review Period: Oct 18, 2023 - Oct 23, 2023
Date Accepted: Dec 2, 2023
(closed for review but you can still tweet)

The final, peer-reviewed published version of this preprint can be found here:

Introducing the “AI Language Models in Health Care” Section: Actionable Strategies for Targeted and Wide-Scale Deployment

Castonguay A, Lovis C

Introducing the “AI Language Models in Health Care” Section: Actionable Strategies for Targeted and Wide-Scale Deployment

JMIR Med Inform 2023;11:e53785

DOI: 10.2196/53785

PMID: 38127431

PMCID: 10767624

Introducing the "AI Language Models in Healthcare" Section: Actionable Strategies for Targeted and Wide-Scale Deployment.

  • Alexandre Castonguay; 
  • Christian Lovis

ABSTRACT

The realm of healthcare is on the cusp of a significant technological leap, courtesy of the advancements in AI language models. These models have demonstrated remarkable prowess in interpreting and generating human-like text, paving the way for enhanced patient engagement, clinical decision support, and a plethora of other applications that were once considered beyond reach. However, the journey from potential to real-world application is laden with challenges ranging from ensuring reliability and transparency to navigating a complex regulatory landscape. This editorial introduces the new section, titled "AI Language Models in Healthcare." This section seeks to create a platform for academics, practitioners, and innovators to share their insights, research findings, and real-world applications of AI language models in healthcare. The aim is to foster a community that is not only excited about the possibilities but is also critically engaged with the ethical, practical, and regulatory challenges that lie ahead.


 Citation

Please cite as:

Castonguay A, Lovis C

Introducing the “AI Language Models in Health Care” Section: Actionable Strategies for Targeted and Wide-Scale Deployment

JMIR Med Inform 2023;11:e53785

DOI: 10.2196/53785

PMID: 38127431

PMCID: 10767624

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