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Introduction of an Integrated Pathology Image Management, Artificial Intelligence, and Reporting System

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Published on: July 11, 2025

What do medical students actually need to know about artificial intelligence?

Liam G McCoy^1,2, Sujay Nagaraj^1,3, Felipe Morgado^1,4

¹Faculty of Medicine, University of Toronto, Medical Sciences Building, 1 King's College Cir, Toronto, ON M5S 1A8 Canada.

NPJ Digital Medicine

|June 25, 2020

Summary

Medical schools must train physicians on artificial intelligence (AI) clinical applications, focusing on data science and leadership. This ensures responsible AI integration in healthcare, addressing explainability, equity, and security concerns.

Keywords:

Health care Medical ethics

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Area of Science:

Medical Education
Artificial Intelligence in Healthcare
Data Science

Background:

Artificial intelligence (AI) innovations are set to transform medical practice.
There is a growing need to educate physicians on AI technologies.
Key concerns include AI explainability, health equity, and data security.

Purpose of the Study:

To determine essential AI competencies for medical students.
To propose a framework for integrating AI education into medical curricula.
To foster leadership in AI within the medical community.

Main Methods:

Analysis of experiences from the University of Toronto Faculty of Medicine.
Incorporation of insights from MIT Critical Data's "datathons".
Advocacy for a dual-focused educational approach.

Main Results:

Competencies for AI clinical use share similarities with other technologies.
Qualitative differences in AI necessitate specific training on explainability, equity, and security.
A combined approach of curriculum enhancement and extracurricular programs is recommended.

Conclusions:

Medical education must evolve to include robust data science components.
Extracurricular activities can cultivate leadership in AI for healthcare.
A proactive educational strategy is crucial for responsible AI adoption in medicine.