NICE is aware that companies are reviewing their CE marking in response to changing regulations and advances in digital health technologies.
The following artificial intelligence (AI)-derived software can be used in the NHS while more evidence is generated, to support review and reporting of CT brain scans for people who have had a suspected stroke:
e-Stroke
RapidAI.
These technologies can only be used once they have appropriate Digital Technology Assessment Criteria (DTAC) approval.
The software should only be used with healthcare professional review and centres should maintain existing scan reporting protocols to reduce the risk of incorrect results. Centres should ensure that images shared between different stroke centres can be remotely reviewed to help with decision making by healthcare professionals at a different site.
More research is needed on the following AI-derived software to support review and reporting of CT brain scans for people who have had a suspected stroke:
Accipio
Aidoc
BioMind
BrainScan CT
Cercare Perfusion
CINA Head
CT Perfusion 4D
icobrain ct
Neuro Solution
qER.
Access to the technologies in section 1.3 should be through company or research funding (non-core NHS funding).
Evidence generation and more research is needed on:
the impact of the addition of AI-derived software on a healthcare professional's ability to identify people for whom thrombolysis and thrombectomy is suitable (see section 3.7)
how often the software is unable to analyse CT brain scans, with reasons for this (see section 3.12)
the impact of using the software on time to thrombolysis or thrombectomy (see section 3.9)
the impact of using the software on how many people have thrombolysis or thrombectomy (see section 3.10).
The evidence generation plan gives further information on the prioritised evidence gaps and outcomes, ongoing studies and potential real-world data sources. It includes how the evidence gaps could be resolved through real-world evidence studies.
Stroke adversely affects quality of life for many people who survive it. Faster and greater access to treatment could improve clinical outcomes and so quality of life after stroke. AI-derived software used alongside healthcare professional interpretation of CT brain scan images could guide and speed up decision making in stroke, for example, decisions on thrombolysis and thrombectomy treatment. The AI-derived software uses fixed (or static) algorithms in clinical practice, with AI used to derive new versions of the algorithm.
Clinical evidence on the software is limited in quality. There is no evidence on their diagnostic accuracy when used alongside healthcare professional review that met review inclusion criteria. Some studies on 2 technologies (e-Stroke and RapidAI) in clinical practice suggest that people had faster or greater access to treatment after using the software, but it is unclear to what extent this is an effect of the software. In the economic model, a small increase in the number of people having thrombectomies because of AI-derived software would likely make the software cost effective.
The software is already widely used in the NHS, and should always be used with healthcare professional review. Because an important potential benefit of the technologies is improving image sharing between centres to help with time-sensitive decision making, centres should ensure that shared images are of sufficient quality to allow remote image review. Healthcare professionals should be cautious when changing their findings based on software results. Existing reporting protocols (or those used before the AI-derived software was adopted in a centre) should be maintained in centres using these technologies.
e-Stroke and RapidAI can be used in the NHS while further evidence is generated to help better determine their cost effectiveness. Other technologies that have no evidence on how they impact time or access to treatment should only be used in research.