Despite the promise of medical artificial intelligence applications, their acceptance in real-world clinical settings is low, with lack of transparency and trust being barriers that need to be overcome. We discuss the importance of the collaborative process in medical artificial intelligence, whereby experts from various fields work together and tackle transparency issues and build trust over time.
This is a preview of subscription content, access via your institution
Relevant articles
Open Access articles citing this article.
-
Sync fast and solve things—best practices for responsible digital health
npj Digital Medicine Open Access 04 May 2024
Access options
Access Nature and 54 other Nature Portfolio journals
Get Nature+, our best-value online-access subscription
$29.99 /Â 30Â days
cancel any time
Subscribe to this journal
Receive 12 digital issues and online access to articles
$119.00 per year
only $9.92 per issue
Buy this article
- Purchase on Springer Link
- Instant access to full article PDF
Prices may be subject to local taxes which are calculated during checkout
References
Tonekaboni, S., Joshi, S., McCradden, M. D. & Goldenberg, A. Proc. 4th Machine Learning for Healthcare Conference 106, 359–380 (2019).
Grote, T. & Berens, P. J. Med. Eth. 46, 205–211 (2019).
Montani, S. & Striani, M. Yearb. Med. Inform. 28, 120–127 (2019).
Markus, A. F., Kors, J. A. & Rijnbeek, P. R. J. Biomed. Inform. 113, 103655 (2021).
Shortliffe, E. H. & Sepùlveda, M. J. J. Am. Med. Assoc. 320, 2199–2200 (2018).
London, A. J. Hastings Cent. Rep. 49, 15–21 (2019).
van Baalen, S. & Carusi, A. Synthese 196, 4469–4492 (2019).
Winter, P. & Carusi, A. Sci. Technol. Stud. 35, 58–77 (2022).
Winter, P. & Carusi, A. Med. Humanit. https://doi.org/10.1136/medhum-2021-012318 (2022).
Winter, P. & Carusi, A. J. Responsib. Technol. 12, 100052 (2022).
Oakden-Rayner, L. https://lukeoakdenrayner.wordpress.com/2018/01/24/chexnet-an-in-depth-review/ (24 January 2018).
Scheek, D., Rezazade Mehrizi, M. H. & Ranschaert, E. Eur. Radiol. 31, 7960–7968 (2021).
Elish, M. C. & Watkins, E. A. Data & Society https://datasociety.net/pubs/repairing-innovation.pdf (2020).
Oakden-Rayner, L. & Palmer, L. J. in Artificial Intelligence in Medical Imaging (eds Ranschaert, E. R., Morozov, S. & Algra, P. R.) 83–104 (Springer, 2019).
Nagendran, M. et al. Br. Med. J. 368, m689 (2020).
Carusi, A. Stud. Hist. Philos. Biol. Biomed. Sci. 48, 28–37 (2014).
Carusi, A. Humana-Mente J. Philos. Stud. 30, 67–86 (2016).
Acknowledgements
We thank J. Anderson for helpful feedback. The research informing this Comment was supported by a Wellcome Grant for the project ‘AI in the Clinic’ (grant number WT/213606).
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Competing interests
The authors declare no competing interests.
Peer review
Peer review information
Nature Machine Intelligence thanks James Anderson for their contribution to the peer review of this work.
Rights and permissions
About this article
Cite this article
Carusi, A., Winter, P.D., Armstrong, I. et al. Medical artificial intelligence is as much social as it is technological. Nat Mach Intell 5, 98–100 (2023). https://doi.org/10.1038/s42256-022-00603-3
Published:
Issue Date:
DOI: https://doi.org/10.1038/s42256-022-00603-3
This article is cited by
-
Sync fast and solve things—best practices for responsible digital health
npj Digital Medicine (2024)
-
Artificial intelligence and illusions of understanding in scientific research
Nature (2024)