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Cited by 63Lung Cancer Diagnosis and TreatmentHealth Systems, Economic Evaluations, Quality of LifeAI in cancer detection

Cost-Effectiveness of Artificial Intelligence Support in Computed Tomography-Based Lung Cancer Screening

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Ziegelmayer, Sebastian, Graf, Markus, Makowski, Marcus, Gawlitza, Joshua, Gassert, Felix·2022·Cancers
DOI 10.3390/cancers14071729PMC PMC8997030
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Abstract

Background: Lung cancer screening is already implemented in the USA and strongly recommended by European Radiological and Thoracic societies as well. Upon implementation, the total number of thoracic computed tomographies (CT) is likely to rise significantly. As shown in previous studies, modern artificial intelligence-based algorithms are on-par or even exceed radiologist’s performance in lung nodule detection and classification. Therefore, the aim of this study was to evaluate the cost-effectiveness of an AI-based system in the context of baseline lung cancer screening. Methods: In this retrospective study, a decision model based on Markov simulation was developed to estimate the quality-adjusted life-years (QALYs) and lifetime costs of the diagnostic modalities. Literature research was performed to determine model input parameters. Model uncertainty and possible costs of the AI-system were assessed using deterministic and probabilistic sensitivity analysis. Results: In the base case scenario CT + AI resulted in a negative incremental cost-effectiveness ratio (ICER) as compared to CT only, showing lower costs and higher effectiveness. Threshold analysis showed that the ICER remained negative up to a threshold of USD 68 for the AI support. The willingness-to-pay of USD 100,000 was crossed at a value of USD 1240. Deterministic and probabilistic sensitivity analysis showed model robustness for varying input parameters. Conclusion: Based on our results, the use of an AI-based system in the initial low-dose CT scan of lung cancer screening is a feasible diagnostic strategy from a cost-effectiveness perspective.

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  • Ziegelmayer et al. reported that AI-assisted low-dose computed tomography remained cost-saving (saving $68 per patient) if the cost per scan stayed below $1240, a threshold confirmed through probabilistic sensitivity analyses.

    “Ziegelmayer et al.25 reported that AI-assisted low-dose computed tomography (LDCT) remained cost-saving (saving $68 per patient) if the cost per scan stayed below $1240—a threshold confirmed through extensive probabilistic sensitivity analyses25.”

    From Systematic review of cost effectiveness and budget impact of artificial intelligence in healthcare (2025) · cites this paper as Ziegelmayer et al.25

    Section 3.3 (page unavailable) says AI support remained cost-saving only while its additional cost was at or below $68 per scan; $1,240 was the limit for cost-effectiveness at a willingness-to-pay of $100,000 per QALY, not for cost savings. Section 3.1 reports approximately $68 lower cumulative cost per patient only in the base case, when the additional AI cost was $0. Sections 3.2–3.3 describe probabilistic sensitivity analysis and threshold analysis separately, rather than saying the former confirmed a cost-saving threshold of $1,240. (LLM, conf=0.90)

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  • Ziegelmayer et al. used up to 30,000 Monte Carlo simulations to demonstrate that AI-assisted LDCT remained cost-saving under various scenarios.

    “Ziegelmayer et al.25 used up to 30,000 Monte Carlo simulations to demonstrate that AI-assisted LDCT remained cost-saving under various scenarios.”

    From Systematic review of cost effectiveness and budget impact of artificial intelligence in healthcare (2025) · cites this paper as Ziegelmayer et al.25

    Methods §2.7 reports a Monte Carlo probabilistic sensitivity analysis with 30,000 iterations, rather than 30,000 separate simulations. Results §3.1 finds lower costs for CT + AI in the base case, but Results §3.2 says the ICER turned positive when specificity varied. Those cases remained cost-effective at the stated willingness-to-pay threshold, not cost-saving. (LLM, conf=0.90)

    Experimental — accuracy not yet measured

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References 43 · Cited by 63

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Authors
Ziegelmayer, S., Graf, M., Makowski, M., Gawlitza, J., & Gassert, F.
Affiliations
TUM Klinikum (DE)
Corresponding author
Ziegelmayer, Sebastian
ORCID
Venue
Cancers
Publisher
MDPI AG
Published
2022-03-29
Volume
14
Issue
7
Pages
1729
ISSN
2072-6694 (ISSN-L)
DOI
10.3390/cancers14071729
PMC
PMC8997030
OpenAlex
W4221017734
Cited by
63
References
43
Citations by year
2026: 14 · 2025: 24 · 2024: 11 · 2023: 10 · 2022: 4
FWCI
7.91
Type
journal article
Language
en
Open access
Licence
Locations
  • Cancers (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
  • mediaTUM – the media and publications repository of the Technical University Munich (Technical University Munich) (submittedVersion)
  • mediaTUM – the media and publications repository of the Technical University Munich (Technical University Munich) (submittedVersion, other-oa)
Topics
  • Health Sciences › Medicine › Pulmonary and Respiratory Medicine › Lung Cancer Diagnosis and Treatment
  • Social Sciences › Economics, Econometrics and Finance › Economics and Econometrics › Health Systems, Economic Evaluations, Quality of Life
  • Physical Sciences › Computer Science › Artificial Intelligence › AI in cancer detection
Keywords
lung cancer screening, artificial intelligence, computer-aided detection, low-dose CT, cost-effectiveness, quality-adjusted life years, lung nodule detection, Markov decision process, incremental cost-effectiveness ratio, willingness-to-pay threshold

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