IDEAS home Printed from https://ideas.repec.org/a/nat/natcom/v15y2024i1d10.1038_s41467-024-51725-8.html
   My bibliography  Save this article

Comparison of AI-integrated pathways with human-AI interaction in population mammographic screening for breast cancer

Author

Listed:
  • Helen M. L. Frazer

    (St Vincent’s Hospital Melbourne
    BreastScreen Victoria
    University of Melbourne)

  • Carlos A. Peña-Solorzano

    (St Vincent’s Institute of Medical Research
    University of Melbourne)

  • Chun Fung Kwok

    (St Vincent’s Institute of Medical Research
    University of Melbourne)

  • Michael S. Elliott

    (St Vincent’s Institute of Medical Research
    University of Melbourne)

  • Yuanhong Chen

    (University of Adelaide)

  • Chong Wang

    (University of Adelaide)

  • Jocelyn F. Lippey

    (St Vincent’s Hospital Melbourne
    St Vincent’s Hospital Melbourne
    University of Melbourne)

  • John L. Hopper

    (University of Melbourne)

  • Peter Brotchie

    (St Vincent’s Hospital Melbourne)

  • Gustavo Carneiro

    (University of Adelaide
    The University of Surrey)

  • Davis J. McCarthy

    (St Vincent’s Institute of Medical Research
    University of Melbourne)

Abstract

Artificial intelligence (AI) readers of mammograms compare favourably to individual radiologists in detecting breast cancer. However, AI readers cannot perform at the level of multi-reader systems used by screening programs in countries such as Australia, Sweden, and the UK. Therefore, implementation demands human-AI collaboration. Here, we use a large, high-quality retrospective mammography dataset from Victoria, Australia to conduct detailed simulations of five potential AI-integrated screening pathways, and examine human-AI interaction effects to explore automation bias. Operating an AI reader as a second reader or as a high confidence filter improves current screening outcomes by 1.9–2.5% in sensitivity and up to 0.6% in specificity, achieving 4.6–10.9% reduction in assessments and 48–80.7% reduction in human reads. Automation bias degrades performance in multi-reader settings but improves it for single-readers. This study provides insight into feasible approaches for AI-integrated screening pathways and prospective studies necessary prior to clinical adoption.

Suggested Citation

  • Helen M. L. Frazer & Carlos A. Peña-Solorzano & Chun Fung Kwok & Michael S. Elliott & Yuanhong Chen & Chong Wang & Jocelyn F. Lippey & John L. Hopper & Peter Brotchie & Gustavo Carneiro & Davis J. McC, 2024. "Comparison of AI-integrated pathways with human-AI interaction in population mammographic screening for breast cancer," Nature Communications, Nature, vol. 15(1), pages 1-12, December.
    • Handle: RePEc:nat:natcom:v:15:y:2024:i:1:d:10.1038_s41467-024-51725-8
    DOI: 10.1038/s41467-024-51725-8
    as

    Download full text from publisher

    File URL: https://www.nature.com/articles/s41467-024-51725-8
    File Function: Abstract
    Download Restriction: no
    File URL: https://libkey.io/10.1038/s41467-024-51725-8?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Scott Mayer McKinney & Marcin Sieniek & Varun Godbole & Jonathan Godwin & Natasha Antropova & Hutan Ashrafian & Trevor Back & Mary Chesus & Greg S. Corrado & Ara Darzi & Mozziyar Etemadi & Florencia G, 2020. "International evaluation of an AI system for breast cancer screening," Nature, Nature, vol. 577(7788), pages 89-94, January.
    2. Quinn McNemar, 1947. "Note on the sampling error of the difference between correlated proportions or percentages," Psychometrika, Springer;The Psychometric Society, vol. 12(2), pages 153-157, June.
    3. Scott Mayer McKinney & Marcin Sieniek & Varun Godbole & Jonathan Godwin & Natasha Antropova & Hutan Ashrafian & Trevor Back & Mary Chesus & Greg S. Corrado & Ara Darzi & Mozziyar Etemadi & Florencia G, 2020. "Addendum: International evaluation of an AI system for breast cancer screening," Nature, Nature, vol. 586(7829), pages 19-19, October.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Alexander P. L. Martindale & Carrie D. Llewellyn & Richard O. Visser & Benjamin Ng & Victoria Ngai & Aditya U. Kale & Lavinia Ferrante Ruffano & Robert M. Golub & Gary S. Collins & David Moher & Melis, 2024. "Concordance of randomised controlled trials for artificial intelligence interventions with the CONSORT-AI reporting guidelines," Nature Communications, Nature, vol. 15(1), pages 1-11, December.
    2. Joachim Meyer, 2024. "Doing AI: Algorithmic decision support as a human activity," Papers 2402.14674, arXiv.org, revised Apr 2024.
    3. Babak Abedin & Christian Meske & Iris Junglas & Fethi Rabhi & Hamid R. Motahari-Nezhad, 2022. "Designing and Managing Human-AI Interactions," Information Systems Frontiers, Springer, vol. 24(3), pages 691-697, June.
    4. Armando Vargas-Palacios & Nisha Sharma & Gurdeep S. Sagoo, 2023. "Cost-effectiveness requirements for implementing artificial intelligence technology in the Women’s UK Breast Cancer Screening service," Nature Communications, Nature, vol. 14(1), pages 1-11, December.
    5. Yuming Jiang & Zhicheng Zhang & Wei Wang & Weicai Huang & Chuanli Chen & Sujuan Xi & M. Usman Ahmad & Yulan Ren & Shengtian Sang & Jingjing Xie & Jen-Yeu Wang & Wenjun Xiong & Tuanjie Li & Zhen Han & , 2023. "Biology-guided deep learning predicts prognosis and cancer immunotherapy response," Nature Communications, Nature, vol. 14(1), pages 1-16, December.
    6. Shu Jiang & Jiguo Cao & Bernard Rosner & Graham A. Colditz, 2023. "Supervised two‐dimensional functional principal component analysis with time‐to‐event outcomes and mammogram imaging data," Biometrics, The International Biometric Society, vol. 79(2), pages 1359-1369, June.
    7. Minkyu Shin & Jin Kim & Bas van Opheusden & Thomas L. Griffiths, 2023. "Superhuman Artificial Intelligence Can Improve Human Decision Making by Increasing Novelty," Papers 2303.07462, arXiv.org, revised Apr 2023.
    8. Juexiao Zhou & Haoyang Li & Xingyu Liao & Bin Zhang & Wenjia He & Zhongxiao Li & Longxi Zhou & Xin Gao, 2023. "A unified method to revoke the private data of patients in intelligent healthcare with audit to forget," Nature Communications, Nature, vol. 14(1), pages 1-13, December.
    9. Sebastian Schleidgen & Orsolya Friedrich & Selin Gerlek & Galia Assadi & Johanna Seifert, 2023. "The concept of “interaction” in debates on human–machine interaction," Palgrave Communications, Palgrave Macmillan, vol. 10(1), pages 1-13, December.
    10. Taynara de Oliveira Castellões & Paloma Maria Silva Rocha Rizol & Luiz Fernando Costa Nascimento, 2024. "Association between Premature Birth and Air Pollutants Using Fuzzy and Adaptive Neuro-Fuzzy Inference System (ANFIS) Techniques," Mathematics, MDPI, vol. 12(18), pages 1-12, September.
    11. Qianwei Zhou & Margarita Zuley & Yuan Guo & Lu Yang & Bronwyn Nair & Adrienne Vargo & Suzanne Ghannam & Dooman Arefan & Shandong Wu, 2021. "A machine and human reader study on AI diagnosis model safety under attacks of adversarial images," Nature Communications, Nature, vol. 12(1), pages 1-11, December.
    12. Mélanie Roschewitz & Galvin Khara & Joe Yearsley & Nisha Sharma & Jonathan J. James & Éva Ambrózay & Adam Heroux & Peter Kecskemethy & Tobias Rijken & Ben Glocker, 2023. "Automatic correction of performance drift under acquisition shift in medical image classification," Nature Communications, Nature, vol. 14(1), pages 1-10, December.
    13. Uttam Bandyopadhyay & Atanu Biswas & Shirsendu Mukherjee, 2009. "Adaptive two-treatment two-period crossover design for binary treatment responses incorporating carry-over effects," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 18(1), pages 13-33, March.
    14. Chacón, José E. & Fernández Serrano, Javier, 2024. "Bayesian taut splines for estimating the number of modes," Computational Statistics & Data Analysis, Elsevier, vol. 196(C).
    15. Bester Tawona Mudereri & Elfatih M. Abdel-Rahman & Shepard Ndlela & Louisa Delfin Mutsa Makumbe & Christabel Chiedza Nyanga & Henri E. Z. Tonnang & Samira A. Mohamed, 2022. "Integrating the Strength of Multi-Date Sentinel-1 and -2 Datasets for Detecting Mango ( Mangifera indica L.) Orchards in a Semi-Arid Environment in Zimbabwe," Sustainability, MDPI, vol. 14(10), pages 1-23, May.
    16. Nosi, Costanza & D’Agostino, Antonella & Pratesi, Carlo Alberto & Barbarossa, Camilla, 2021. "Evaluating a social marketing campaign on healthy nutrition and lifestyle among primary-school children: A mixed-method research design," Evaluation and Program Planning, Elsevier, vol. 89(C).
    17. John E. Core, 2010. "Discussion of Chief Executive Officer Equity Incentives and Accounting Irregularities," Journal of Accounting Research, Wiley Blackwell, vol. 48(2), pages 273-287, May.
    18. Preety Srivastava & Xueyan Zhao, 2010. "What Do the Bingers Drink? Micro‐Unit Evidence on Negative Externalities and Drinker Characteristics of Alcohol Consumption by Beverage Types," Economic Papers, The Economic Society of Australia, vol. 29(2), pages 229-250, June.
    19. Hanousek Jan & Kočenda Evžen & Novotný Jan, 2012. "The identification of price jumps," Monte Carlo Methods and Applications, De Gruyter, vol. 18(1), pages 53-77, January.
    20. Monnery, Benjamin & Wolff, François-Charles & Henneguelle, Anaïs, 2020. "Prison, semi-liberty and recidivism: Bounding causal effects in a survival model," International Review of Law and Economics, Elsevier, vol. 61(C).

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:nat:natcom:v:15:y:2024:i:1:d:10.1038_s41467-024-51725-8. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.nature.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.