Abstract Objective To assess the cost-effectiveness of using artificial intelligence (AI)-derived software to assist reading computed tomography (CT) scans of the chest to identify and analyse lung nodules compared to unaided reading in symptomatic, incidental and screening populations. Methods Decision tree structures were developed in TreeAge Pro 2021. Structures were informed by British Thoracic Society (BTS) clinical guidelines and clinical opinion. Results were presented as incremental cost-effectiveness ratios (ICERs) expressed as cost per quality-adjusted life year (QALY) over a lifetime from the UK National Health Service and Personal Social Services perspective. Results For the symptomatic population, the unaided radiologist reading strategy dominated the AI-assisted reading strategy. In the incidental population, unaided radiologist reading was cost-effective with an ICER of approximately £1,000 per QALY. Conversely, in the screening population AI-assisted radiologist reading dominated unaided reading. The cause of AI-assistance being cost-effective depended on the number of people who had undergone CT surveillance because of non-cancerous findings. Given the limitations in the quality and quantity of evidence to inform inputs, these results should be interpreted with caution. Conclusion Current analyses based on limited evidence suggested that in the symptomatic and incidental populations unaided radiologist reading may be the more cost-effective strategy, while in the screening population AI-assisted radiologist reading appeared to be the dominant strategy. Better quality evidence is required to have a definitive answer about their cost-effectiveness. Advances in knowledge This paper shows whether adding AI-derived software to radiologist reading of CT scans to identify lung nodules offers good value for money.
Auguste et al. (2026) studied this question.