Education Hub

Learn AI. Applied to medicine.

Webinar recordings, workshop slides, specialty AI resources, recommended reading, and the IAMSA curriculum framework โ€” all in one place.

Recommended Reading

Essential papers & books.

The IAMSA reading list โ€” curated for clinical students with no prior AI background.

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Topol Review โ€” Preparing the Healthcare Workforce to Deliver the Digital Future (2019)
NHS England ยท Foundational policy document ยท Free PDF
Read โ†’
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High-performance medicine: the convergence of human and artificial intelligence โ€” Topol (Nature Medicine 2019)
Nature Medicine ยท Landmark review ยท Open access
Read โ†’
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A guide to deep learning in healthcare โ€” Rajpurkar et al. (Nature Medicine 2019)
Nature Medicine ยท Technical but accessible ยท Open access
Read โ†’
๐Ÿ“š
Deep Medicine โ€” Eric Topol (2019)
Book ยท Best accessible intro for clinical students ยท Available on Amazon
Recommended
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NHS AI Lab: A National Strategy for AI in Health and Social Care (2021)
NHSX ยท Policy ยท Free PDF
Read โ†’
Curriculum Framework

The IAMSA AI curriculum framework.

A structured, competency-based framework for AI education in medical schools โ€” designed to be adopted by any institution.

01

Foundations of Clinical AI

Core concepts, terminology, and how AI tools are developed and validated for clinical use. No coding required.

  • What is machine learning? A clinical primer
  • How AI tools are trained, validated, and approved
  • Understanding sensitivity, specificity, AUC
  • Bias in clinical AI โ€” causes and consequences
  • MHRA and FDA approval pathways for AI devices
02

AI Across Specialties

Specialty-specific AI tools and evidence โ€” from radiology to primary care, with real clinical examples.

  • Radiology: chest X-ray, CT, mammography
  • Pathology: digital slides and cancer grading
  • Cardiology: ECG AI and heart failure prediction
  • Primary care: sepsis, deterioration, risk scoring
  • Oncology: genomics and treatment response
03

Ethics, Safety & Governance

The ethical, legal, and professional dimensions of AI in clinical practice โ€” aligned with GMC guidance.

  • Algorithmic bias and health inequalities
  • Informed consent in AI-assisted decisions
  • Explainability and the black-box problem
  • Data governance: GDPR, NHS DSPT, ICO guidance
  • Professional accountability and the GMC position
04

Research & Critical Appraisal

How to read, appraise, and contribute to clinical AI research โ€” including the TRIPOD+AI reporting framework.

  • Reading a clinical AI paper: a step-by-step guide
  • TRIPOD+AI and CONSORT-AI reporting standards
  • Systematic reviews in AI: challenges and methods
  • How to get involved in AI research as a student
  • IAMSA research working group pathway
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