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Fostering Multidisciplinary Collaboration in Artificial Intelligence and Machine Learning Education: Tutorial Based

Taiki W Nishihara1, Fritz Gerald P Kalaw1,2, Adelle Engmann3

  • 1Viterbi Family Department of Ophthalmology and Shiley Eye Institute, Hamilton Glaucoma Center, Division of Ophthalmology Informatics and Data Science, University of California, San Diego, 9415 Campus Point Drive, La Jolla, CA, 92093, United States, 1 858-534-8413.

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Summary
This summary is machine-generated.

The AI-READI Bootcamp successfully trained biomedical professionals in artificial intelligence (AI) and machine learning (ML) using real-world data. This program bridges the gap between clinical and computational expertise for future health innovations.

Keywords:
artificial intelligencebiomedical researchcurriculum developmentdata scienceinterdisciplinary trainingmachine learningmedical educationtranslational research

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Area of Science:

  • Biomedical research
  • Artificial Intelligence
  • Machine Learning

Background:

  • Limited interdisciplinary training exists for AI/ML in biomedical research, creating a gap between data scientists and clinicians.
  • The NIH's Bridge2AI initiative launched AI-READI to create a multimodal, FAIR dataset for diabetes research.
  • AI-READI aims to train a workforce proficient in computational methods and clinical applications.

Purpose of the Study:

  • To describe the design, refinement, and outcomes of the AI-READI Bootcamp.
  • To share lessons learned for developing future multidisciplinary AI/ML training programs in biomedical research.

Main Methods:

  • An 80-hour bootcamp combining lectures, coding, and mentorship, refined annually based on participant feedback.
  • Year 1 focused on foundational Python and ML techniques; Year 2 integrated the AI-READI dataset and added modules on large language models and FAIR data principles.
  • Participant satisfaction and characteristics were assessed via pre/post surveys, with qualitative feedback analyzed thematically.

Main Results:

  • High participant satisfaction reported across both years, with Year 2 showing improvements due to smaller cohorts and applied learning.
  • Year 2 achieved perfect scores for instructor effectiveness, staff support, and overall enjoyment.
  • Participants valued working with multimodal biomedical datasets, peer collaboration, and the applicability of learned skills.

Conclusions:

  • The AI-READI Bootcamp demonstrates a successful model for bridging technical and clinical expertise in biomedical AI through feedback-driven, multidisciplinary training.
  • Key elements include diverse cohorts, applied learning with relevant datasets, and sustained mentorship.
  • Future iterations will include pre-bootcamp modules, objective skill assessments, and long-term tracking of research productivity.