Digital Health

AI in Healthcare

British Institute of Radiology (BIR) AI Essentials

The British Institute of Radiology in partnership with Health Education England NHS Digital Academy provides a freely available webinar series Artificial Intelligence Education Essentials aimed at providing all healthcare workers with a basic understanding of AI. This webinar series follows the competency framework from Health Education England and covers the following sections:

  1. Introduction to AI
  2. Governance
  3. Implementation
  4. Clinical use
  5. Bias
  6. Additional topics of interest

NHS Digital, Artificial Intelligence and Robotics (DART-Ed) webinar series

The DART-Ed program is a response to the Topol Review which set out the important contribution that digital healthcare technologies can make in improving patient care. The program aims to educate and train the current and future workforce in AI and Robotic technologies so that more time may be given to caring for patients. The series is broken down into five webinars and covers:

The Alan Turing Institute - Fairness and Responsibility in Human-AI interaction in medical settings

The course explores the issues affecting the fair, safe and ethical use of AI in healthcare, including novel scenario-based learning to see the effect of decisions made through the AI design, and implementation phases of a healthcare AI project. The course has been developed by NHS England’s Workforce, Training and Education Directorate in collaboration with the University of Manchester and Logica. The Digital Academy’s Digital, Artificial Intelligence and Robotics in Education (DART-Ed) programme has recently published 2 reports highlighting the need for and routes towards the workforce’s preparedness for clinical AI adoption. This course is a first step towards turning the findings and learnings of the reports into practical real world educational resources for the health and care workforce, both within the NHS and globally.

The Alan Turing Institute - Introduction to Transparent Machine Learning

The course aims to address the priority of transparency in responsible AI. It introduces the essentials of transparent machine learning for learners of diverse backgrounds to understand and apply transparent machine learning in real-world applications with confidence and trust. The course covers both transparent machine learning systems/models and transparent machine learning processes. It adapts classical machine learning textbooks and materials under this framework to give a fresh treatment that will be more accessible for learners from multiple disciplines, including engineering, science, social sciences, medical science, and humanities.

The Wicklow AI in Medicine Research Initiative

A collection of talks which demonstrate machine learning in medical research. It’s supported by some of the most influential people in machine learning.

Healthcare Data

Fast Healthcare Interoperability Resource (FHIR) Public Test Servers

This page lists FHIR servers that are publicly available for testing. These are public services provided by volunteers and HL7 makes no representations concerning their safety or reliability.

MHRA’s Synthetic Data Examples

Clinical Practice Research Datalink (CPRD) is a real-world research service supporting retrospective and prospective public health and clinical studies. CPRD has generated a number of synthetic datasets that can be used for training purposes or to improve algorithms or machine learning workflows.

The Cancer Imaging Archives

The Cancer Imaging Archives (TCIA) is a service which de-identifies and hosts a large archive of medical images of cancer accessible for public download. The data are organized as “collections”; typically patients’ imaging is related by a common disease (e.g. lung cancer), image modality or type (MRI, CT, digital histopathology, etc) or research focus.

Python FHIR Package

Python package for FHIR (Fast Healthcare Interoperability Resources) which is a specification for exchanging healthcare information electronically. It is designed to facilitate the exchange of data between different healthcare systems and applications, and is commonly used to build APIs (Application Programming Interfaces) for healthcare data.


Cambridge Spark - Data and AI Apprenticeships for the NHS

Cambridge Spark delivers data and AI apprenticeships that provide the NHS workforce with the skills needed to reshape health and social care with data. Working with a range of partners, including the NHS Leadership Academy, Microsoft, AnalystX and HDR UK, every NHS employee has an opportunity to develop the data and digital skills they need to succeed.

Health Education England - Topol Digital Fellowships

The Topol Digital Fellowship is a 12 month programme which provides health and social care professionals with time, support and training to lead digital health transformations and innovations in their organisations.


Data Science

Python Data Science Handbook

One of the best books for learning more Python skills for dealing with data, at is available online for free! With this handbook, you’ll learn how to use:


NHS Python Community

Led by enthusiasts and advocates, the NHS Python Community for Healthcare is an open community of practice that champions the use of the Python programming language and open code in the NHS and healthcare sector.

Machine Learning


CNN Explainer

A detailed and interactive description of convolution neural networks (CNNs) one of the most utilised tools for machine learning. This gives a solid foundation for any person trying to build a good understanding.

Fast AI’s Practical Deep Learning for Coders

A free course designed for people with some coding experience, who want to learn how to apply deep learning and machine learning to practical problems.

Google’s generative AI learning path

his learning path provides an overview of generative AI concepts, from the fundamentals of large language models to responsible AI principles.

Machine Learning Yearning - Andrew Ng’s Free EBook

This is an introductory book about developing ML algorithms. You will learn to diagnose errors in an ML project, prioritize the most promising directions, work within complex settings like mismatched training/test sets, and know when and how to apply various techniques.

Probabilistic Machine Learning: An Introduction - Free EBook

This book offers a detailed and up-to-date introduction to machine learning (including deep learning) through the unifying lens of probabilistic modeling and Bayesian decision theory. The book covers mathematical background (including linear algebra and optimization), basic supervised learning (including linear and logistic regression and deep neural networks), as well as more advanced topics (including transfer learning and unsupervised learning). End-of-chapter exercises allow students to apply what they have learned, and an appendix covers notation.

Machine Learning Engineering - Free EBook

A comprehensive book on the engineering aspects of building reliable AI systems. This is less for understanding the theory and more the practical aspects of doing.

Stanford’s - Machine Learning Specialization (Coursera)

One of the most taken ML courses. This is a strong introduction to ML which comprises of three courses. Course can be audited for free or purchased for well-recongnised certifications.

Medical Physics

McMedHacks Annual Course

Paid annual course on medical image analysis and deep learning in Python. The course is generally around 8 weeks long and aims to teach students, researchers and clinicians the fundamentals. It consists of a series of in-depth demos, in the form of Google Colab Notebooks and seminars delivered by a variety of lecturers.

Language Models

🤗 Natural Language Processing Course

This course will teach you about natural language processing (NLP) using libraries from the Hugging Face ecosystem — 🤗 Transformers, 🤗 Datasets, 🤗 Tokenizers, and 🤗 Accelerate — as well as the Hugging Face Hub. It’s completely free and without ads.

Google’s Introduction to Large Language Models

This is an introductory level micro-learning course that explores what large language models (LLM) are, the use cases where they can be utilized, and how you can use prompt tuning to enhance LLM performance. It also covers Google tools to help you develop your own Gen AI apps.

Andrej Karpathy’s - Let’s build GPT: from scratch, in code, spelled out.

One of the best teachers in Machine Learning going through step-by-step building a LLM. A fanstastic resource for those who learn best by coding.

Reinforcement Learning

Deep Mind x UCL RL Lecture Series

Lecture series from Deep Mind and UCL on reinforcement learning. Includes teaching from the most experienced people in this fascinating sub-field of machine learning. Excellent to build a strong foundational knowledge on the topic.

🤗 Deep Reinforcement Learning Course

This course will teach you about Deep Reinforcement Learning from beginner to expert. It’s completely free and open-source!


Nvidia Launchpad

NVIDIA LaunchPad provides free access to enterprise NVIDIA hardware and software through an internet browser. Users can experience the power of AI with end-to-end solutions through guided hands-on labs or as a development sandbox. Test, prototype, and deploy your own applications and models against the latest and greatest that NVIDIA has to offer. A specific mention of the Annotate and Adapt Medical Imaging Models with MONAI course for being application to the medical field.

AI Guidance and Regulation


What’s new - AI regulation Service - NHS

Find out what changes have been made to the AI and Digital Regulations Service. One of the best ways to keep on top of this ever shifting landscape.

Medical Physics

Artifical Intelligence in Medical Physics

Roles, Responsibilities, Education and Training of Clinically Qualified Medical Physicists.