The NHS AI Lab

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Artificial Intelligence: How to get it right

Putting policy into practice for safe data-driven innovation in health and care.

Resource Collection

AI in adult social care

A collection of case studies that show the potential for AI to improve outcomes and increase choices for adult social care service users

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Creating an international approach to AI for healthcare

White paper: AI for healthcare: Creating an international approach together.

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European Respiratory Journal: using imaging to combat a pandemic

The National COVID-19 Chest Imaging Database (NCCID) is a repository of chest radiographs, CT and MRI images and clinical data from COVID-19 patients across the UK, to support research.

How data is supporting the COVID-19 response

This page summarises the ways data is supporting the Covid-19 response and measures taken by NHSX to ensure data is used lawfully

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CDEI AI Barometer

The Centre for Data Ethics and Innovation (CDEI) AI Barometer is an analysis of the most urgent opportunities, risks, and governance challenges associated with AI and data use in the UK

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Machine learning in diagnostic and screening services

The Care Quality Commission report looks at what care providers and tech suppliers need to do so that people receive high-quality care through machine learning applications for diagnostic purposes

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Data-driven healthcare: regulation & regulators

Reform has been working with NHSX to produce easily understood information on the regulation of data-driven technologies in healthcare

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The Dstl Biscuit Book

The Biscuit Book, by data science experts at the Defence Science and Technology Laboratory (Dstl) AI Lab, helps customers understand AI

Case Study

Cogstack

Cogstack is an open source real-time data warehousing tool meeting the need for an efficient way of coding that improves financial and operational efficiencies for NHS providers.

Case Study

Precision medicine

Precision medicine considers variability in genes, environment, and lifestyle to decide disease treatment and prevention, more accurately predicting which treatment and prevention strategies will work best for a particular patient.

Case Study

EMRAD

Emrad is a partnership of seven NHS trusts spread over 11 hospitals, covering more than five million patients, to create a new, common digital radiology system.

Case Study

Kortical

Kortical is building artificial intelligence models to predict demand and supply levels in, for example different blood types, allowing for patient needs to be met more precisely.

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Artificial Intelligence Sector Deal

The Government's AI Sector Deal outlines a package of support for the AI and big data sector from government, industry and academic organisations

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The Industrial Strategy AI Mission

The Grand Challenges set out the government’s mission in developing AI and data driven technologies, including the prevention, early diagnosis and treatment of chronic diseases

Case Study

Remote intelligent monitoring and predictive analytics to support people in their optimal care setting

London Borough of Bexley

Case Study

Acoustic monitoring integrated with electronic care planning

Friends of the Elderly, Allycare and KareInn

Case Study

Predictive analytics to assess risk and trigger care interventions

Cera Care - Concern Predictor

Case Study

Intelligent monitoring for proactive care

Birdie & Medacs Healthcare

Case Study

Remote monitoring of vital signs

Lifelight and Hampshire Hospitals NHS Foundation Trust

Case Study

Remote monitoring for early detection of respiratory conditions

Feebris, Havering Health and Care City

Case Study

Assessing pain in people with dementia who cannot self-report

PainChek case study

Case Study

User-centred service design via free text analytics

London Borough of Islington

Case Study

Predictive analytics: responding to a post-COVID-19 world

Worcestershire County council

Case Study

Joined-up demand analysis for health and care

City of Wolverhampton Council

Case Study

Predictive analytics triggering workflows to support carers, prevent falls and reduce loneliness

Nottinghamshire County Council

Case Study

Analytics and decision support to improve workforce retention

Skills for Care

Case Study

Decision support for care workers about their next best actions

Cera Care - Dynamic Tasks

Case Study

Matching demand for social support with supply through geospatial mapping and a digital marketplace

The Tribe Project

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The NHS AI Lab 1st anniversary event

The NHS AI Lab turned one in 2020. We held an event on 24 September to outline progress one year on and priorities moving forward; as well as showcasing AI Award winners and addressing AI hot topics such as regulation and evidencing effectiveness.

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Artificial Intelligence (AI) funding streams

This page provides information on the funding streams available for AI-related projects in the health and social care sector in the UK.

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Case Study

SMART box boosts regional data to the NCCID

As part of a programme to boost data collection to the National COVID-19 Chest Imaging Database, a new SMART box delivers regional data with one simple installation.

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Case Study

Healthy.io: Smartphone albuminuria urine self-testing

Patients with diabetes and high blood pressure are benefiting from pioneering artificial intelligence (AI) that turns a smartphone camera into a clinical-grade tool to detect early kidney disease.

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Mia mammography intelligent assessment

Applying deep learning artificial intelligence to breast cancer screening.

Case Study

Using AI to support NHS Resolution with negligence claims prediction

Exploring how AI might help the NHS to understand and identify risk - preventing harm and saving valuable resources.

Resource Collection

Understanding AI regulation

A collection of resources to help developers of AI for health and care

Case Study

Using AI to improve back office efficiency in the NHS

This case study details how AI technologies can help manage, plan and schedule health and social care ensuring the right people are in the right place, helping patients receive the care they need and save hospitals time and money.

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Sharing AI skills and experience through deep dive workshops with University Hospital Southampton

As part of a shared learning initiative from the NHS AI Lab Skunkworks, the Digital team at University Hospital Southampton undertook a 5-part series of AI workshops to help them explore the ethical and safety considerations of applying AI in their work.

Develop AI

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Are you trying to develop AI for use in health and care?

Find resources to help you design and build AI solutions that meet the requirements of the NHS and social care.

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The Guide to Good Practice for data-driven health and care technology

The guide sets out the behaviours the government expects from those deploying and using data-driven technologies in health and care

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Explaining decisions made with AI

This guidance by the ICO and The Alan Turing Institute aims to give organisations practical advice to help explain the processes, services and decisions delivered or assisted by AI, to the individuals affected by them

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Explainability in data-driven health and care technology

Future Advocacy was commissioned by NHSX to produce this guidance on Principle 7 of the NHS Code of Conduct on data-driven health and care technology, which relates to the explainability of algorithms

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Evidence standards framework for digital health technologies

NICE’s standards aim to make it easier for innovators and commissioners to understand what good levels of evidence for digital healthcare technologies look like

Case Study

Data Lens: a fast-access data search in multiple languages

A pilot project for the NHS AI Lab Skunkworks team, Data Lens brings together information about multiple databases, providing a fast-access search in multiple languages

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Case Study

Using machine learning to identify patients at risk of long term hospital stays

Case study: Investigating the benefits of artificial intelligence applied to hospital admissions data

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AI Award research contract

This document provides the NHSX funding agreement for AI in Health and Care Award Phases 3 and 4.

Guidance

AI regulation guide: considerations when developing AI products and tools

A guide from the multi-agency advisory service on the importance of the value proposition, determining a product’s intended use, and planning ahead to navigate the path to market.

Guidance

AI regulation guide: using PICO to generate evidence for AI development

A guide from the multi-agency advisory service on generating the right evidence for your AI product for health and care, and mastering the PICO statement.

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Digital Technology Assessment Criteria (DTAC)

The DTAC brings together legislation and good practice. The DTAC helps healthcare organisations assess suppliers to make sure new digital technologies meet our standards. For developers, it sets out what is expected for entry into the NHS

Case Study

Using AI to identify tissue growth from CT scans

This 12-week artificial intelligence project investigated whether it was possible to identify organs and growths from CT scans and report on any changes to support radiologists.

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Blog: Lessons learned from the multi-agency advisory service – helping developers of AI and data-driven tech to navigate the regulatory pathway

This blog post from NICE outlines the common challenges that developers of artificial intelligence tech face when navigating their way to the health and care market.

Case Study

Improving hospital bed allocation using AI

This 14-week project investigated the AI techniques that could be used to generate options for moving patients in a way that supports the human team to make the best decisions. The project aimed to provide a proof of concept tool.

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Resource Collection

AI in imaging: resource collection

A collection of resources about the use of medical imaging data in artificial intelligence tools for health and social care.

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An overview of the National COVID-19 Chest Imaging Database: data quality and cohort analysis

An academic paper published 25 November 2021

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Towards nationally curated data archives for clinical radiology image analysis at scale

Academic paper giving learnings from national data collection in response to the COVID-19 pandemic. Published 23 November 2021.

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Algorithmic impact assessment: a case study in healthcare

This Ada Lovelace Institute report sets out the first-known detailed proposal for the use of an algorithmic impact assessment for data access in a healthcare context.

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Generating high-fidelity synthetic patient data for assessing machine learning healthcare software

An article exploring the production of realistic synthetic data based on UK primary care patient data. Published in Nature.com, November 2020.

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Practical lessons from generating synthetic healthcare data with Bayesian networks

Academic paper from the MHRA and the Intelligent Data Analysis Group at Brunel University.

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Good machine learning practice for medical device development: guiding principles

These guiding principles from the MHRA will help promote safe, effective, and high-quality medical devices that use artificial intelligence and machine learning.

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Generating and evaluating cross-sectional synthetic electronic healthcare data

Academic paper from the MHRA and Brunel University on preserving data utility and patient privacy with synthetic data.

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BayesBoost: Identifying and handling bias using synthetic data generators

External research paper from MHRA and Brunel University exploring attempts to create synthetic data that lead to predictive models with better performance.

Case Study

NCCID case study: Setting standards for testing Artificial Intelligence

This blueprint assessment process is a valuable example of the quality testing that needs to take place for all AI algorithms designed for health and care.

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Case Study

Exploring how to create mock patient data (synthetic data) from real patient data

This case study looks at one process for creating “synthetic data” - where new “fake” data is generated from real patient data using a specifically designed model, in a way that maintains several characteristics of the original data.

Adopt AI

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Are you trying to adopt AI for use in health and care?

Understand best practice in commissioning AI and get inspired by learning how organisations overcome challenges they faced adopting AI.

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A buyer's guide to AI in health and care

This summary of the "Buyer's guide to AI in health and care" gives an overview of the things you need to consider to make well-informed decisions about buying AI products.

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Reporting guidelines for AI clinical trials - the SPIRIT-AI and CONSORT-AI extensions

SPIRIT-AI are guidelines for clinical trial protocols and CONSORT-AI are for clinical trial reports for interventions involving artificial intelligence.

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Understanding AI ethics and safety

The Alan Turing Institute's guide for the responsible design and implementation of AI systems in the public sector

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A guide to using artificial intelligence in the public sector

This GDS and Office for AI guide sets out rules on ethics and safety for everyone involved in the design, production, and deployment of AI

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Guidelines for AI procurement

A summary from the Office for AI of best practice addressing the specific challenges in acquiring artificial intelligence technologies in government

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Assessing if AI is the right solution

Guidance from GDS and the Office for AI to help choose the right AI technology for a public sector challenge

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Preparing the healthcare workforce to deliver the digital future

The Topol Review outlined recommendations to ensure the NHS is the world leader in using digital technologies to benefit patients

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