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    Artificial intelligence-derived software to analyse chest X-rays for suspected lung cancer in primary care referrals: early value assessment

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    1 Recommendations

    1.1 Artificial intelligence (AI)-derived software cannot be used for early routine use in the NHS to analyse chest X‑rays alongside clinician review for suspected lung cancer in adults referred from primary care while evidence is generated. The following AI-derived software should only be used in research to help identify abnormal lung features that suggest lung cancer, prioritise review of chest X‑rays and speed up referral for a CT scan:

    • AI-Rad Companion Chest X-ray (Siemens Healthineers)

    • Annalise CXR (Annalise ai)

    • Auto Lung Nodule Detection (Samsung)

    • Chest link (Oxipit)

    • ChestView (Gleamer)

    • Chest X-ray (Rayscape)

    • ClearRead Xray (Riveraintech)

    • InferRead DR Chest (Infervision)

    • Lunit INSIGHT CXR (Lunit)

    • Milvue Suite (Milvue)

    • qXR (Qure.ai)

    • Red dot (Behold.ai)

    • SenseCare-Chest DR PRO (CADe, SenseTime)

    • VUNO Med-Chest X-Ray (VUNO).

    1.2 Centres already using AI‑derived software for review of chest X‑rays in adults referred from primary care may continue to do so but only under appropriate research governance and only alongside clinician review.

    1.3 Further research is recommended to assess:

    • the impact of the software on clinical decision making and the number of people referred to have a chest CT scan

    • how using the software affects healthcare costs and resource use

    • the impact on review and reporting time, and time to CT referral and diagnosis

    • the diagnostic accuracy of AI-derived software alongside clinician review in detecting nodules or other abnormal lung features that suggest lung cancer, including in people with multiple conditions

    • the technical failure rates of the software

    • whether the software works in groups that could particularly benefit (see section 3.9).

    Why the committee made these recommendations

    There is an unmet need for quicker reporting of chest X‑rays in people referred from primary care, particularly when this may support earlier detection of lung cancer. AI‑derived software may help to reduce time to diagnosis and treatment by supporting clinician review of images and by prioritising images for review so a CT scan can be done the same day. But, there is insufficient evidence. With the available evidence, it is not possible to assess the clinical and cost benefits or risks of using the technology in the NHS. So, AI-derived software should not be used for clinical decision making in the NHS until more evidence is available.

    There is no evidence to show how accurate software-assisted clinician review will be at identifying lung abnormalities compared with clinician review alone in people referred for a chest X‑ray by their GP. Using this software could lead to lung cancer being missed or people having unnecessary CT scans, which can cause anxiety. This could also increase costs. More research on the performance of the technologies could show what the benefits and risks are.

    Limited evidence suggests that the technologies did not significantly reduce the time to report chest X‑rays, but that they could allow same day CT scans when used to prioritise X‑rays for clinical review. More research on the impact of the technologies on how lung cancer is diagnosed would allow better understanding of the benefits for patients and the healthcare system.