NICE EVALUATIONS OF AI AND DIGITAL TECHNOLOGIES
Author(s)
Suvi Harmala, MRes, Vera T. Unwin, PhD, Martin W. Njoroge, PhD, Thomas Lawrence, Mr, Frances Nixon, BSc.
National Institute for Health and Care Excellence (NICE), Manchester, United Kingdom.
National Institute for Health and Care Excellence (NICE), Manchester, United Kingdom.
OBJECTIVES: NICE produces HealthTech guidance on diagnostics, medical devices and digital technologies including AI. Technologies that are early in their evidence generation are evaluated within the Early Use Assessment process, whilst those that are further along are routed to the Routine Use process. The final recommendations are based on the assessment of clinical and cost effectiveness. This descriptive summary provides insights around the AI and digital technologies included in NICE assessments.
METHODS: We reviewed NICE HealthTech guidance with AI and digital technologies between 2010 and 2026. Summary statistics were used to describe the technologies and recommendations made, as well as the evidence gaps identified where further evidence is required.
RESULTS: NICE has assessed 236 digital technologies across 33 evaluations (of which 6 are ongoing). Most of the evaluations were assessed using the Early Use Assessment process (85%, n=28). The assessments covered a range of technologies including AI-derived algorithms, smartphone apps and web-based platforms. Many topics were centred around mental health (27%, n=9) including behavioural or neurodevelopmental conditions, or cancer (21%, n=7). The intended purpose of the technologies was largely split between treatment (52%, n=17), and diagnostic and/or monitoring uses (48%, n=16). Nearly all the published topics (85%, n=23) recommend early (conditional) or routine access to some or all the evaluated technologies. The most common evidence gap categorised as an ‘essential’ is effectiveness data, either compared to standard of care or with an insufficient time horizon (56%, n=15).
CONCLUSIONS: NICE has assessed and recommended a wide range of AI and digital technologies. Many technologies are early in their lifecycle, and so may not yet have the evidence needed to assess their effect on patient outcomes and the NHS. This review highlights the breadth of technologies NICE considers, and areas where further evidence for AI and digital technologies is often needed.
METHODS: We reviewed NICE HealthTech guidance with AI and digital technologies between 2010 and 2026. Summary statistics were used to describe the technologies and recommendations made, as well as the evidence gaps identified where further evidence is required.
RESULTS: NICE has assessed 236 digital technologies across 33 evaluations (of which 6 are ongoing). Most of the evaluations were assessed using the Early Use Assessment process (85%, n=28). The assessments covered a range of technologies including AI-derived algorithms, smartphone apps and web-based platforms. Many topics were centred around mental health (27%, n=9) including behavioural or neurodevelopmental conditions, or cancer (21%, n=7). The intended purpose of the technologies was largely split between treatment (52%, n=17), and diagnostic and/or monitoring uses (48%, n=16). Nearly all the published topics (85%, n=23) recommend early (conditional) or routine access to some or all the evaluated technologies. The most common evidence gap categorised as an ‘essential’ is effectiveness data, either compared to standard of care or with an insufficient time horizon (56%, n=15).
CONCLUSIONS: NICE has assessed and recommended a wide range of AI and digital technologies. Many technologies are early in their lifecycle, and so may not yet have the evidence needed to assess their effect on patient outcomes and the NHS. This review highlights the breadth of technologies NICE considers, and areas where further evidence for AI and digital technologies is often needed.
Conference/Value in Health Info
2026-11, ISPOR Europe 2026, Vienna, Austria
Value in Health, Volume 29, Issue 12S
Code
MT41
Topic
Health Technology Assessment, Medical Technologies
Topic Subcategory
Digital Health
Disease
Mental Health (including addiction), Oncology