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1 July 20265 min read
Medicine
NICE Issues Guidance on Evaluating AI Clinical Decision Support Tools
The National Institute for Health and Care Excellence has published a framework for assessing AI-powered clinical decision support tools, setting out the evidence standards it expects before recommending such technologies for NHS use.
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The National Institute for Health and Care Excellence has published guidance on how it will evaluate AI-powered clinical decision support tools for potential NHS recommendation. The framework sets out the evidence standards NICE expects developers and commissioners to meet.
NICE says it will require evidence of clinical validity — that the tool accurately identifies the condition or risk it claims to detect — and clinical utility, meaning that using the tool leads to better patient outcomes than not using it. The agency notes that many AI tools have been evaluated only for technical performance, without evidence that they change clinical behaviour or improve care.
The guidance addresses the challenge of evaluating tools that may perform differently across patient subgroups. NICE says developers should provide disaggregated performance data by age, sex, ethnicity and other relevant characteristics, and that tools showing significant performance disparities may require additional evidence before recommendation.
For tools that use machine learning models that may change over time, NICE sets out requirements for post-deployment monitoring and re-evaluation when models are updated. The agency says it expects developers to have plans for ongoing performance surveillance as part of any recommendation.
NICE guidance applies to tools being considered for NHS recommendation. It does not prevent NHS trusts from procuring and using AI tools independently, though the guidance may influence local procurement decisions.
Why It Matters
NICE recommendations carry significant weight in NHS procurement. By setting out explicit evidence standards for AI clinical decision support, NICE is creating a practical bar that developers must clear to reach the NHS market at scale. The emphasis on clinical utility — not just technical accuracy — is important: it pushes back against the common practice of evaluating AI tools only on their ability to match expert labels, without demonstrating that they change outcomes.
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Topics
NICEClinical Decision SupportNHSAI EvaluationHealthcare AI