AI in practice

AI deployment fundamentals for medical imaging

RCR's minimum standards for providers to deploy artificial intelligence (AI) solutions in radiology.

Artificial Intelligence (AI) in Pharmacy

Royal Pharmaceutical Society policy to outline the opportunities, challenges and potential applications of artificial intelligence (AI) within pharmacy now and in the future.

Auto-contouring in radiotherapy

RCR guidance developed to aid all healthcare professionals involved in the use of auto-contouring systems for radiotherapy treatment and explains the different stages of selection, implementation and ongoing monitoring of an auto-contouring system to support the safe clinical use of this new technology.

Guidelines for AI procurement

A summary of best practice addressing specific challenges of acquiring Artificial Intelligence technologies in government.

Guidance for Anonymisation

28/3/2025 Updated guidance for all organisations that want to anonymise personal data and identify the issues which should be considered to effectively use anoymisation techniques.

Guidance on the Impact Evaluation of AI Interventions

Guidance on the impact evaluation of AI interventions, complementing HMT's Magenta Book (guidance on evaluation more broadly).

Guidance on the use of AI-enabled ambient scribing products in health and care setting

This guidance outlines key considerations for NHS technology leaders implementing ambient scribing products across health and care settings in England. Ambient scribing products, when combined with generative Artificial Intelligence (AI), can convert speech directly into structured medical documentation, such as notes and letters. The products are sometimes referred to as ambient scribes or AI scribes and include advanced ambient voice technologies (AVTs) used for clinical or patient documentation and workflow support.

How would clinicians use time freed up by technology?

Explores how time freed up by technology is used in practice and clinician perspectives on how such time could be used in the NHS.

Practical Lessons from Generating Synthetic Healthcare Data with Bayesian Networks

We explore the integration of resampling, probabilistic graphical modelling, latent variable identification, and outlier analysis for producing realistic synthetic data based on UK primary care patient data.

Tackling algorithmic bias and promoting transparency in health datasets: the STANDING Together consensus recommendations

The STANDING Together recommendations aim to encourage transparency regarding limitations of health datasets and proactive evaluation of their effect across population groups. We hope that adoption of the STANDING Together recommendations by stakeholders across the AI health technology lifecycle will enable everyone in society to benefit from technologies which are safe and effective.

Tech to save time: how the NHS can realise the benefits

Drawing on the Health Foundation’s own research and a rapid evidence review by The Evidence Centre, this analysis explores situations where health technologies have had no or negative impact on staff time and investigates why.

Using machine learning in diagnostic and screening services

CQC review of the use of machine learning applications for diagnostic purposes in healthcare services.

What do technology and AI mean for the future of work in health care?

Recent developments in artificial intelligence (AI) have sparked fears about the potential threat to jobs in many industries, but what will it mean for health care? This long read explores the potential.

Guidance and case studies for teams adopting AI technologies in the public sector (Ai.Gov.uk knowledge hub)

Guidance and case studies for teams adopting AI technologies in the public sector.

Understanding healthcare workers’ confidence in artificial intelligence (AI)

1 April 2023. This report explores the factors influencing healthcare workers’ confidence in artificial intelligence-driven (AI) technologies.