How are you taking part in this consultation?

You will not be able to change how you comment later.

You must be signed in to answer questions

    The content on this page is not current guidance and is only for the purposes of the consultation process.

    Research plan

    Closed for comments This consultation ended on at   Request commenting lead permission

    2 Evidence gaps

    This section describes the evidence gaps, why they need to be addressed and their relative importance for future committee decision making.

    The committee will not be able to make a positive recommendation without the essential evidence gaps (see section 2.1) being addressed. This will help the committee to make a recommendation by ensuring it has a better understanding of the patient or healthcare system benefits of the technologies.

    2.1 Essential evidence for future committee decision making

    How accurate AI technologies used in teledermatology services are at detecting cancer and non-cancer skin lesions compared with established teledermatology services alone

    Collecting data on the accuracy of AI technologies in detecting cancer and non-cancer skin lesions compared with teledermatology alone is essential for determining whether AI can provide assessments suitable for routine clinical settings. The data will help to determine whether the AI technologies can effectively discharge non-urgent cases from the suspected skin cancer pathway while maintaining diagnostic accuracy for detecting high risk lesions. This data will help evaluate the potential of AI technologies to enhance the diagnosis of cancer skin lesions and optimise clinical resources by reducing the burden on dermatology services. The data will also help to assess whether AI technologies increase staff capacity and benefit people with non-cancer dermatological conditions, compared with teledermatology alone. Additionally, this can help inform whether such technologies can be used autonomously and if this is reliable and safe.

    How accurate AI technologies are at detecting non-cancer and cancer skin lesions in people with black or brown skin

    Collecting information about the accuracy of the technologies in detecting skin lesions on different skin colour is vital for assessing potential biases in performance and for ensuring equitable healthcare. Skin tone should ideally be measured using skin spectrophotometry.

    The effect of using AI technologies in teledermatology services on the number of urgent suspected cancer referrals and for face-to-face dermatology appointments compared with a well-established teledermatology service alone

    Collecting data on the number of face-to-face dermatology referrals generated by AI technology compared with established teledermatology services alone is crucial for understanding their impact on healthcare workflows. This information will help determine if AI can reduce unnecessary referrals, saving dermatologist time, and thereby optimising resources and improving timely access to care for people.