Foundations of AI for future physicians: A practical, accessible curriculum

Area of Science:

• Medical Education
• Artificial Intelligence in Healthcare

Background:

• Machine learning (ML) and large language models (LLMs) are revolutionizing healthcare delivery.
• Clinicians often lack foundational knowledge of AI, leading to potential overreliance and oversight.
• Current medical curricula largely omit practical AI education, creating an educational gap.

Purpose of the Study:

• To address the educational gap in AI literacy for clinicians.
• To propose a practical, accessible curriculum for teaching foundational AI concepts.
• To foster critical engagement with AI tools in clinical practice.

Main Methods:

• Developed a modular curriculum utilizing Google Colaboratory (Colab) notebooks.
• The curriculum covers data science, ML model building, and LLM interaction.
• Emphasized a hands-on, interactive approach suitable for non-data scientists.

Main Results:

• The curriculum is designed with five interconnected modules, progressing from basic data science to advanced ML and LLMs.
• Colab's accessible, cloud-based platform facilitates practical learning and exploration of AI applications.
• The modular design allows for integration into existing medical school data science threads.

Conclusions:

• A modular, hands-on curriculum delivered via Colab notebooks can effectively teach foundational AI concepts to clinicians.
• This approach promotes critical engagement with AI tools, essential for safe and effective healthcare integration.
• The curriculum is adaptable and scalable for diverse educational settings, including low-resource environments.