Develop AI

External Resource

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

External Resource

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

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

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

Report

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 the AI and digital regulations 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 AI and digital regulations service on generating the right evidence for your AI product for health and care, and mastering the PICO statement.

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.