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Artificial intelligence in biomedical team science: perceptions, practices, and training needs.

Emily Slade1, Kelsey Karnik1, Caitline Phan1

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Researchers show varied use of artificial intelligence (AI) in collaborative biomedical research. Findings highlight a need for clear guidelines and training to ensure responsible AI integration in team science.

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Area of Science:

  • Biomedical Research
  • Collaborative Science
  • Artificial Intelligence Applications

Background:

  • Artificial intelligence (AI) is increasingly prevalent in biomedical research.
  • Limited understanding exists regarding AI tool usage and researcher roles within collaborative teams.
  • This study investigates researchers' experiences and attitudes toward AI in team-based research environments.

Purpose of the Study:

  • To examine how researchers utilize AI tools in collaborative research settings.
  • To understand researchers' perceptions of AI's benefits and drawbacks in team science.
  • To identify needs for training and frameworks supporting AI integration in research teams.

Main Methods:

  • A cross-sectional survey was distributed to 178 investigators at the University of Kentucky.
  • The survey assessed AI use in research and communication, team decision-making, perceived benefits/concerns, and training preferences.
  • Data were collected from 39 participants, representing a 22% response rate.

Main Results:

  • AI adoption varied significantly, with 26% never using AI and 42% using it weekly or daily.
  • Nearly half of teams did not discuss AI use before implementation.
  • Concerns included misinformation, bias, and overreliance, alongside benefits like reduced repetitive tasks.

Conclusions:

  • Significant variability exists in AI tool adoption and researcher attitudes within collaborative research teams.
  • A gap is evident between AI's perceived benefits and current integration practices.
  • Evidence-based training and structured frameworks are crucial for responsible and effective AI use in team science.