Objectives To characterize the capabilities of CE-marked AI products for lung nodule analysis in lung cancer screening (LCS), quantify their coverage of tasks defined in nodule management recommendations, and assess their peer-reviewed evidence. Materials and methods Six core tasks in LCS (nodule detection, classification, measurement, growth assessment, malignancy risk estimation, and structured management) were derived from 4 nodule management recommendations: Lung-RADS 2022, British Thoracic Society (BTS) guidelines, European Union Position Statement (EUPS), and European Society of Thoracic Imaging (ESTI). Products were identified through <a href="http://www.healthairegister.com/">www.healthairegister.com</a>. Vendors confirmed capabilities using a standardized questionnaire; public documentation supplemented non-responders. Task coverage was calculated as the percentage of functional overlap (0–100%) per recommendation. Peer-reviewed evidence was evaluated using a six-level efficacy framework and assessed for study characteristics. Results In total, 16 products from 16 vendors were included; 10 vendors completed questionnaires. Analysis showed that 14 products detect and measure solid and subsolid nodules, 12 support growth assessment, and 9 provide malignancy risk estimation (PanCan in 5, AI-based scores in 4). No product provides support for endobronchial or cystic lesions. High task coverage (> 75%) was observed in 10 products for EUPS and 4 for BTS, whereas no product achieved high coverage for Lung-RADS or ESTI. Overall, 60 peer-reviewed studies were identified; 7% were prospective and evidence clustered at lower efficacy levels: 70% assessed diagnostic accuracy, while none reported patient outcomes or societal impact. Conclusion Numerous CE-certified AI products could support CT-based lung cancer screening, but gaps in task coverage and predominantly lower-level evidence necessitate cautious, monitored implementation. Key Points <Emphasis Type="BoldItalic">Question</Emphasis> Do commercially available AI products for lung nodule analysis functionally cover international nodule management recommendation-defined tasks, and what peer-reviewed clinical evidence supports them? <Emphasis Type="BoldItalic">Findings</Emphasis> AI products support standard nodule detection and measurement in line with management recommendations but lack support for endobronchial or cystic lesions and high-level clinical evidence. <Emphasis Type="BoldItalic">Clinical relevance</Emphasis> CE-marked AI products can assist radiologists with core lung cancer screening tasks, but capability gaps exist. Limited high-level clinical evidence complicates integrating AI into guidelines, securing reimbursement, and formulating recommendations for its use in lung cancer screening programs. Graphical Abstract

Commercial AI for CT lung cancer screening: product capabilities, coverage of nodule management tasks and supporting evidence

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2026年5月7日

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Noa Antonissen,
Steven Schalekamp,
Horst K. Hahn,
Kicky G. van Leeuwen,
Colin Jacobs

摘要

Objectives

To characterize the capabilities of CE-marked AI products for lung nodule analysis in lung cancer screening (LCS), quantify their coverage of tasks defined in nodule management recommendations, and assess their peer-reviewed evidence.

Materials and methods

Six core tasks in LCS (nodule detection, classification, measurement, growth assessment, malignancy risk estimation, and structured management) were derived from 4 nodule management recommendations: Lung-RADS 2022, British Thoracic Society (BTS) guidelines, European Union Position Statement (EUPS), and European Society of Thoracic Imaging (ESTI). Products were identified through www.healthairegister.com. Vendors confirmed capabilities using a standardized questionnaire; public documentation supplemented non-responders. Task coverage was calculated as the percentage of functional overlap (0–100%) per recommendation. Peer-reviewed evidence was evaluated using a six-level efficacy framework and assessed for study characteristics.

Results

In total, 16 products from 16 vendors were included; 10 vendors completed questionnaires. Analysis showed that 14 products detect and measure solid and subsolid nodules, 12 support growth assessment, and 9 provide malignancy risk estimation (PanCan in 5, AI-based scores in 4). No product provides support for endobronchial or cystic lesions. High task coverage (> 75%) was observed in 10 products for EUPS and 4 for BTS, whereas no product achieved high coverage for Lung-RADS or ESTI. Overall, 60 peer-reviewed studies were identified; 7% were prospective and evidence clustered at lower efficacy levels: 70% assessed diagnostic accuracy, while none reported patient outcomes or societal impact.

Conclusion

Numerous CE-certified AI products could support CT-based lung cancer screening, but gaps in task coverage and predominantly lower-level evidence necessitate cautious, monitored implementation.

Key Points

Question Do commercially available AI products for lung nodule analysis functionally cover international nodule management recommendation-defined tasks, and what peer-reviewed clinical evidence supports them?

Findings AI products support standard nodule detection and measurement in line with management recommendations but lack support for endobronchial or cystic lesions and high-level clinical evidence.

Clinical relevance CE-marked AI products can assist radiologists with core lung cancer screening tasks, but capability gaps exist. Limited high-level clinical evidence complicates integrating AI into guidelines, securing reimbursement, and formulating recommendations for its use in lung cancer screening programs.

Graphical Abstract