A guide to artificial intelligence in healthcare

An Owkin guide to the

A-Z of AI in Healthcare

Explore here
Discover our interactive guide of bite-sized and advanced explanations of the core concepts in Artificial Intelligence for healthcare, including examples of how it’s used in action today. Explore this library of content ranging from practical uses for AI in medical imaging to generative AI and the future of AI in healthcare.
Browse the entire series or choose one of the categories below to get started.

You can view this page in other languages, thanks to AI translations.

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Algorithm
Algorithm
A set of instructions for completing a task or solving a problem.
AI basics
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Bias
Bias
A tendency to support or oppose a person, group or thing in an unfair way.
Fairness & responsibility
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C-Index
C-Index
A mathematical test that can be used to assess an algorithm's performance.
AI basics
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Digital Twin
Digital Twin
An exact digital replica of a real-life person, thing or process.
Applied AI
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Ethics
Ethics
The moral guidelines that guide decisions about what should be done (or not).
Fairness & responsibility
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Federated Learning
Federated Learning
A decentralized technique to train algorithms with multiple data providers.
AI basics
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Generalizability
Generalizability
A way to determine how well an algorithm works in a new setting.
Applied AI
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Harmonization
Harmonization
The process of ensuring data from different sources can be used to train AI.
Data
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Imaging Data
Imaging Data
The images produced from medical tests like ultrasound, CT, MRI, PET and X-ray.
Data
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Job Enhancement
Job Enhancement
Strategies used to improve efficiency, add more meaning and challenge to jobs.
Fairness & responsibility
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Knowledge Graphs
Knowledge Graphs
A way or organising data from different sources to clarify their relationships.
Applied AI
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Large Language Model
Large Language Model
A type of AI trained on large datasets that generates text or images.
Applied AI
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Multimodal data
Multimodal data
Data that is captured in different sources and formats and then joined together.
Data
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Neural Networks
Neural Networks
A type of AI that uses interconnected networks of neurons, like a human brain.
AI basics
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Overfitting
Overfitting
An error that occurs when an algorithm becomes too familiar with training data.
Data
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Prediction
Prediction
Using AI to estimate an individual's risk of a specific healthcare outcome.
Applied AI
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Quality vs quantity of data
Quality vs quantity of data
How much data is available to train AI vs how suitable that data is for training
Data
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Representation
Representation
The format in which data is stored, analysed and processed by devices.
Data
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Subgroup determination
Subgroup determination
Understanding which groups of patients respond to drugs and in what way.
Applied AI
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Training
Training
The process of teaching an algorithm how to do its job.
AI basics
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Unsupervised vs Supervised Learning
Unsupervised vs Supervised Learning
Two common methods for training AI algorithms in the context of healthcare.
AI basics
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Validation
Validation
The process of testing to see whether an algorithm works well.
AI basics
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White box vs black box
White box vs black box
Algorithms that are transparent by design vs those that are not explainable.
Fairness & responsibility
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XAI
XAI
A field of research that aims to make AI algorithms easier to understand.
Fairness & responsibility
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You
You
How healthcare professionals are essential to the responsible use of AI.
Fairness & responsibility
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Zero Shot Learning
Zero Shot Learning
The ability of an algorithm to classify an image that it has not seen before.
AI basics
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A to Z of AI in Healthcare: Your Interactive Guide
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Explore Owkin’s A to Z of AI in Healthcare—an interactive guide offering bite-sized explanations of AI concepts. Toggle between simple and advanced insights to discover how AI uses patient data to drive healthcare innovations.

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This project was inspired by The A-Z of AI which is a collaboration between the Oxford Internet Institute (OII) at the University of Oxford and Google.