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    Has all of the relevant evidence been taken into account?

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    Are the summaries of clinical and cost effectiveness reasonable interpretations of the evidence?

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    Are the recommendations sound and a suitable basis for guidance to the NHS?

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    Are there any equality issues that need special consideration and are not covered in the medical technology consultation document?

HealthTech draft guidance

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1 Recommendations

For people who do not have diagnosed IBD or Lynch syndrome

Can be used during the evidence generation period to help detect colorectal polyps

1.1

Five artificial intelligence (AI) technologies can be used in the NHS during the evidence generation period as options to help detect colorectal polyps during colonoscopy, for people who do not have diagnosed inflammatory bowel disease (IBD) or Lynch syndrome. The technologies are:

1.2

The companies must confirm that agreements are in place to generate the evidence. NICE will contact the companies annually to confirm that evidence is being generated and analysed as planned. NICE may revise or withdraw the guidance if these conditions are not met.

1.3

At the end of the evidence generation period (4 years), the companies should submit the evidence to NICE in a format that can be used for decision making. NICE will review the evidence and assess if the technology can be routinely adopted in the NHS.

More research is needed to help detect colorectal polyps

1.4

More research is needed on 5 AI technologies used to help detect colorectal polyps during colonoscopy. The technologies are:

  • Argus

  • CADDIE

  • Discovery

  • ENDOANGEL

  • Endoscopic Multimedia Information System (EMIS).

More research is needed to help characterise colorectal polyps

1.5

More research is needed on 4 AI technologies used to help characterise colorectal polyps. The technologies are:

  • CADDIE

  • CAD EYE

  • GI Genius

  • MAGENTIQ-COLO.

For people with diagnosed IBD or Lynch syndrome

1.6

More research is needed on 10 AI technologies to help detect or characterise colorectal polyps for people with diagnosed IBD or Lynch syndrome. The technologies are:

  • Argus (for detecting)

  • CAD EYE (for detecting and characterising)

  • CADDIE (for detecting and characterising)

  • Discovery (for detecting)

  • EMIS (for detecting)

  • ENDO-AID (for detecting)

  • ENDOANGEL (for detecting)

  • EndoScreener (for detecting)

  • GI Genius (for detecting and characterising)

  • MAGENTIQ-COLO (for detecting and characterising).

What this means in practice

For AI technologies that can be used during the evidence generation period

The 5 AI technologies listed in recommendation 1.1 can be used in the NHS as options to help detect colorectal polyps during the evidence generation period (4 years) and paid for using core NHS funding. During this time, more evidence will be collected to address any uncertainties. Companies are responsible for organising funding for evidence generation activities.

After this, NICE will review this guidance and the recommendations may change. Take this into account when negotiating the length of contracts and licence costs.

Potential benefits of use in the NHS during the evidence generation period

  • Clinical benefit: Clinical evidence shows that AI technologies can help endoscopists detect adenomatous polyps (which can develop into colorectal cancer) during colonoscopy which might have otherwise been missed. Early detection means earlier access to appropriate treatment or surveillance pathways as needed. This could reduce the number of future colorectal cancer cases and improve clinical outcomes.

  • Resources: AI technologies could reduce the need for more intensive healthcare resources later, such as for emergency presentations or more advanced stages of cancer.

Managing the risk of use in the NHS during the evidence generation period

  • Costs: Results from economic analyses suggest that the AI technologies could be cost effective. But these results are uncertain. By collecting more evidence on how effective the AI technologies are, and the numbers of colorectal cancers missed and prevented in the future, this uncertainty could be reduced. Trusts should consider the costs of the AI technologies, particularly upfront costs, when implementing them.

  • Clinical risk: The AI technologies should only be used as an aid to detect colorectal polyps. The technologies cannot replace endoscopist review. AI technologies for characterising colorectal polyps should only be used in research because evidence for characterisation is more limited.

  • Equality: Evidence generation could help address equality by collecting data on differences in how effective the AI technologies are for some groups of people compared with others.

For AI technologies that need more research

There is not enough evidence to support NHS funding of the:

  • 5 AI technologies to help detect colorectal polyps (see recommendation 1.4)

  • 4 AI technologies to help characterise colorectal polyps (see recommendation 1.5)

  • 10 AI technologies to help detect or characterise colorectal polyps for people with IBD or Lynch syndrome (see recommendation 1.6).

When more research is needed, access to the AI technologies should be through company, research or non-core NHS funding, and clinical or financial risks should be managed appropriately.

What evidence generation is needed

More evidence needs to be generated on the effect of the AI technologies on:

  • polyp detection rate by type and size

  • post-colonoscopy colorectal cancer rates

  • clinical management.

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.

What research is needed

More research is needed:

  • on the effect of the AI technologies on polyp detection rate:

    • by type and size

    • for people with IBD or Lynch syndrome.

  • for AI technologies that can characterise colorectal polyps, on the:

    • sensitivity and specificity of using the AI technologies alongside endoscopist judgement to characterise different types of polyps

    • ability to differentiate between potentially cancerous polyps (including sessile serrated lesions; SSLs) and polyps that are not.

Why the committee made these recommendations

Clinical evidence shows that 5 AI technologies (CAD EYE, ENDO-AID, EndoScreener, GI Genius and MAGENTIQ-COLO) increase the percentage of colonoscopies in which one or more of a type of potentially cancerous polyp, called an adenoma, is detected. But evidence suggests that the technologies mostly increase the number of smaller adenomas found, which are less likely than large polyps to develop into bowel cancer. It is uncertain whether the technologies increase the number of large adenomas found. There is also not enough evidence to show how effective the technologies are at helping to detect SSLs, which are another type of potentially cancerous polyp. So, it is uncertain in practice whether using AI technologies increases the number of cancer cases detected during colonoscopy. This means that their cost effectiveness is also uncertain. These 5 AI technologies can be used while evidence is generated.

There is limited evidence on the effectiveness of Argus, CADDIE, Discovery, ENDOANGEL and EMIS in detecting colorectal polyps. So, it is uncertain whether they increase the percentage of colonoscopies in which one or more adenoma is found. Also, it was not possible to estimate the cost-effectiveness for CADDIE or ENDOANGEL. So, more research is needed on these 5 AI technologies.

People with IBD or Lynch syndrome were excluded from a lot of the clinical trials and their bowels may look different, or they may have different types of polyps. So, it's uncertain whether the AI technologies will work as well in people with these conditions. So, more research is needed for these people.

Some AI technologies include a function to help the endoscopist characterise the polyp and decide if it is potentially cancerous without removing it or examining it in a laboratory. There is less evidence for this function, and the research is of lower quality. So, more research is needed on AI technologies to help characterise colorectal polyps.