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NHS England - Transformation Directorate
Find guidance to develop your AI. Get inspired by challenges facing individuals and organisations developing AI and how they overcame them.
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
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
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.
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.
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
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.
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.
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.
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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