Introduction

The rapid advancement of artificial intelligence (AI) is transforming various sectors, including healthcare and medical education. AI offers a wide range of applications that can enhance teaching, learning, and assessment in medical education.

This repository will explore the fundamentals of AI, focus on practical applications, and collect resources and links to help medical educators get started with AI.

Key Characteristics of AI in Education

  1. Undetectable: Currently, it's challenging to reliably determine whether a student is using AI for academic tasks unless they make obvious blunders. This poses implications for academic integrity and plagiarism, demanding a re-evaluation of traditional approaches to assessment.
  2. Ubiquitous: AI is becoming increasingly accessible with widespread availability at low or no cost for individuals with Internet access. This democratizes access to powerful tools, making familiarity with AI essential for future healthcare professionals.
  3. Transformative: AI has the potential to revolutionize medical education in unforeseen ways. Educators need to proactively engage with AI, understand its capabilities and limitations, and shape its integration into the curriculum to maximize its benefits for learners.

Why Should I Use AI?

There are a lot of possibilities to consider for how AI can be beneficial to medical education. You can consider improving learning experiences, automating administrative tasks, preparing learners for future practice that incorporates AI, new advanced forms of simulation, assistance with writing, etc.

The bottom line is that AI will be a part of medical practice and medical education in the future. Your learners will be using this technology, so we as educators have the opportunity to help them use it effectively and safely. On an exciting note, AI opens up a whole new realm of possibilities to shape the future of medical education. From new forms of teaching, to analyzing data that was previously inaccessible, there are a lot of exciting possibilities. But educators who are aware of the challenges they encounter in their learning environment, aware of the pedagogy and theory that informs good teaching, and aware of the things their learners struggle with, need to be at the forefront of the conversation.

Defining AI in Medical Education

  1. Artificial Intelligence for Teaching and Learning Practices
    • Enhancing or reinventing existing teaching practices
    • Examples:
      • Using generative AI for multiple choice questions
      • Producing didactic content
      • Conducting simulations
      • Assisting in writing tasks (e.g., recommendation letters, manuscripts)
  2. Artificial Intelligence for Clinical Applications
    • Emerging AI applications in clinical environments
    • Examples:
      • Prediction models (e.g., sepsis identification, admission risk)
      • EHR-integrated tools (e.g., note generation, summarization)
      • Clinical reasoning aids
      • Point-of-care assistance tools
    • Educational role:
      • Equipping trainees to use these technologies effectively and safely
      • Teaching critical evaluation of AI outputs
  3. Medical Education Informatics
    • Using information technology to enhance medical education outcomes
    • Focus: Applying data science techniques (with or without AI) for educational benefit
    • Examples:
      • Augmenting application processes for medical school or residency
      • Developing metrics to assess trainee behaviors in clinical environments
      • Using data to improve educational practices and outcomes

This repository will primarily focus on AI for Teaching and Learning Practices, as we feel this is where the majority of educators will be getting started with AI.