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This summary is machine-generated.

Graduate students explored large language models (LLMs) in healthcare via a global Master Class. The project highlighted the importance of international collaboration for developing technical and soft skills in health informatics.

Keywords:
EducationEducation and Trainingcultural exchange

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Area of Science:

  • Health Informatics
  • Medical Education
  • Artificial Intelligence in Healthcare

Background:

  • The International Partnership for Health Informatics Education (IPHIE) promotes global cooperation in health informatics.
  • The 2024 IPHIE Master Class convened graduate students and faculty to discuss large language models (LLMs) in healthcare.

Purpose of the Study:

  • To examine the use, perceptions, and impact of LLMs in healthcare settings.
  • To assess the educational value of international collaboration in health informatics.

Main Methods:

  • Students collaboratively designed, piloted, and analyzed a survey on LLM utilization by healthcare providers and patients.
  • A Master Class format facilitated international exchange and skill development.

Main Results:

  • The survey explored current LLM applications and user perspectives in healthcare.
  • Despite logistical challenges, students enhanced leadership, problem-solving, and communication abilities.
  • The collaborative international experience proved valuable for skill acquisition.

Conclusions:

  • Global collaboration is crucial for advancing health informatics education and practice.
  • Developing both technical expertise and soft skills prepares professionals for multidisciplinary healthcare teams.
  • Further research into LLM integration in healthcare is warranted.