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Results from a Swedish model-based analysis of the cost-effectiveness of AI-assisted digital mammography.

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Title: Results from a Swedish model-based analysis of the cost-effectiveness of AI-assisted digital mammography.
Authors: Lyth, Johan1 (AUTHOR), Gialias, Pantelis2,3 (AUTHOR), Husberg, Magnus1 (AUTHOR), Bernfort, Lars1 (AUTHOR), Bjerner, Tomas2,3 (AUTHOR), Wiberg, Maria Kristoffersen2,3 (AUTHOR), Levin, Lars-Åke1 (AUTHOR), Gustafsson, Håkan1,3 (AUTHOR) hakan.l.gustafsson@liu.se
Source: European Radiology. Jan2026, Vol. 36 Issue 1, p754-764. 11p.
Subjects: Cost effectiveness, Quality-adjusted life years, Digital mammography, Early detection of cancer, Swedes, Medical care costs, Markov processes
Abstract: Objective: To evaluate the cost-effectiveness of AI-assisted digital mammography (AI-DM) compared to conventional biennial breast cancer digital mammography screening (cDM) with double reading of screening mammograms, and to investigate the change in cost-effectiveness based on four different sub-strategies of AI-DM. Materials and methods: A decision-analytic state-transition Markov model was used to analyse the decision of whether to use cDM or AI-DM in breast cancer screening. In this Markov model, one-year cycles were used, and the analysis was performed from a healthcare perspective with a lifetime horizon. In the model, we analysed 1000 hypothetical individuals attending mammography screenings assessed with AI-DM compared with 1000 hypothetical individuals assessed with cDM. Results: The total costs, including both screening-related costs and breast cancer-related costs, were €3,468,967 and €3,528,288 for AI-DM and cDM, respectively. AI-DM resulted in a cost saving of €59,320 compared to cDM. Per 1000 individuals, AI-DM gained 10.8 quality-adjusted life years (QALYs) compared to cDM. Gained QALYs at a lower cost means that the AI-DM screening strategy was dominant compared to cDM. Break-even occurred at the second screening at age 42 years. Conclusion: This analysis showed that AI-assisted mammography for biennial breast cancer screening in a Swedish population of women aged 40–74 years is a cost-saving strategy compared to a conventional strategy using double human screen reading. Further clinical studies are needed, as scenario analyses showed that other strategies, more dependent on AI, are also cost-saving. Key Points: QuestionTo evaluate the cost-effectiveness of AI-DM in comparison to conventional biennial breast cDM screening. FindingsAI-DM is cost-effective, and the break-even point occurred at the second screening at age 42 years. Clinical relevanceThe implementation of AI is clearly cost-effective as it reduces the total cost for the healthcare system and simultaneously results in a gain in QALYs. [ABSTRACT FROM AUTHOR]
Copyright of European Radiology is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: Results from a Swedish model-based analysis of the cost-effectiveness of AI-assisted digital mammography.
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  Data: <searchLink fieldCode="AR" term="%22Lyth%2C+Johan%22">Lyth, Johan</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Gialias%2C+Pantelis%22">Gialias, Pantelis</searchLink><relatesTo>2,3</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Husberg%2C+Magnus%22">Husberg, Magnus</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Bernfort%2C+Lars%22">Bernfort, Lars</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Bjerner%2C+Tomas%22">Bjerner, Tomas</searchLink><relatesTo>2,3</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Wiberg%2C+Maria+Kristoffersen%22">Wiberg, Maria Kristoffersen</searchLink><relatesTo>2,3</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Levin%2C+Lars-Åke%22">Levin, Lars-Åke</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Gustafsson%2C+Håkan%22">Gustafsson, Håkan</searchLink><relatesTo>1,3</relatesTo> (AUTHOR)<i> hakan.l.gustafsson@liu.se</i>
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  Data: <searchLink fieldCode="JN" term="%22European+Radiology%22">European Radiology</searchLink>. Jan2026, Vol. 36 Issue 1, p754-764. 11p.
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  Data: <searchLink fieldCode="DE" term="%22Cost+effectiveness%22">Cost effectiveness</searchLink><br /><searchLink fieldCode="DE" term="%22Quality-adjusted+life+years%22">Quality-adjusted life years</searchLink><br /><searchLink fieldCode="DE" term="%22Digital+mammography%22">Digital mammography</searchLink><br /><searchLink fieldCode="DE" term="%22Early+detection+of+cancer%22">Early detection of cancer</searchLink><br /><searchLink fieldCode="DE" term="%22Swedes%22">Swedes</searchLink><br /><searchLink fieldCode="DE" term="%22Medical+care+costs%22">Medical care costs</searchLink><br /><searchLink fieldCode="DE" term="%22Markov+processes%22">Markov processes</searchLink>
– Name: Abstract
  Label: Abstract
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  Data: Objective: To evaluate the cost-effectiveness of AI-assisted digital mammography (AI-DM) compared to conventional biennial breast cancer digital mammography screening (cDM) with double reading of screening mammograms, and to investigate the change in cost-effectiveness based on four different sub-strategies of AI-DM. Materials and methods: A decision-analytic state-transition Markov model was used to analyse the decision of whether to use cDM or AI-DM in breast cancer screening. In this Markov model, one-year cycles were used, and the analysis was performed from a healthcare perspective with a lifetime horizon. In the model, we analysed 1000 hypothetical individuals attending mammography screenings assessed with AI-DM compared with 1000 hypothetical individuals assessed with cDM. Results: The total costs, including both screening-related costs and breast cancer-related costs, were €3,468,967 and €3,528,288 for AI-DM and cDM, respectively. AI-DM resulted in a cost saving of €59,320 compared to cDM. Per 1000 individuals, AI-DM gained 10.8 quality-adjusted life years (QALYs) compared to cDM. Gained QALYs at a lower cost means that the AI-DM screening strategy was dominant compared to cDM. Break-even occurred at the second screening at age 42 years. Conclusion: This analysis showed that AI-assisted mammography for biennial breast cancer screening in a Swedish population of women aged 40–74 years is a cost-saving strategy compared to a conventional strategy using double human screen reading. Further clinical studies are needed, as scenario analyses showed that other strategies, more dependent on AI, are also cost-saving. Key Points: QuestionTo evaluate the cost-effectiveness of AI-DM in comparison to conventional biennial breast cDM screening. FindingsAI-DM is cost-effective, and the break-even point occurred at the second screening at age 42 years. Clinical relevanceThe implementation of AI is clearly cost-effective as it reduces the total cost for the healthcare system and simultaneously results in a gain in QALYs. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
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  Data: <i>Copyright of European Radiology is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.1007/s00330-025-11821-9
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        Text: English
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        PageCount: 11
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      – SubjectFull: Cost effectiveness
        Type: general
      – SubjectFull: Quality-adjusted life years
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      – SubjectFull: Digital mammography
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      – SubjectFull: Swedes
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      – SubjectFull: Markov processes
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              Text: Jan2026
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