New data show that AI could enhance imaging-based screen for pancreatic cancer; however, its evaluation must be rigorous and adhere to the same standards used for conventional screening.
In recent years, artificial intelligence (AI) has become a pervasive element of our lives. Consciously or unconsciously, we interact with AI techniques when using search engines on the internet, posting on or reading social media, or using transportation. In clinical medicine, the uptake of AI has happened at a much slower pace, with diagnoses and treatment recommendations still almost exclusively based on human judgement. Only recently have AI techniques been evaluated for their applicability and potential benefit for several clinical scenarios, with video and imaging applications leading the way1. In this issue of Nature Medicine, Cao et al.2 report the results of a study in which they have assessed AI techniques for detecting and classifying pancreatic lesions in non-contrast computerized tomography (CT) imaging. The approach tries to meet the clinical need for the early detection of pancreatic cancer, a challenging disease given its often unspecific symptoms, which result in late detection and poor prognosis3.
This is a preview of subscription content, access via your institution
Access Nature and 54 other Nature Portfolio journals
Get Nature+, our best-value online-access subscription
$29.99 / 30 days
cancel any time
Subscribe to this journal
Receive 12 print issues and online access
$209.00 per year
only $17.42 per issue
Rent or buy this article
Prices vary by article type
Prices may be subject to local taxes which are calculated during checkout
Han, R. et al. Preprint at medRxiv https://doi.org/10.1101/2023.09.12.23295381 (2023).
Cao, K. et al. Nat. Med. https://doi.org/10.1038/s41591-023-02640-w (2023).
Kleeff, J. et al. Nat. Rev. Dis. Primers 2, 16022 (2016).
Lång, K. et al. Lancet Oncol. 24, 936–944 (2023).
Ueda, D. et al. Lancet Digital Health 5, e525–e533 (2023).
Zhou, Y. et al. Nature 622, 156–163 (2023).
Tadesse, G. F., Tegaw, E. M. & Abdisa, E. K. J. Ultrasound 26, 355–367 (2023).
Hon, H. J., Chong, P. P., Choo, H. L. & Khine, P. P. Asian Pac. J. Cancer Prev. 24, 2207–2215 (2023).
Zhou, Y., Shi, Y., Lu, W. & Wan, F. Front Psychol. 13, 866124 (2022).
Bretthauer, M. et al. JAMA Intern. Med. https://doi.org/10.1001/jamainternmed.2023.3798 (2023).
Placido, D. et al. Nat. Med. 29, 1113–1122 (2023).
Kurian, S. J. et al. Mayo Clinic Proc. 95, 2370–2381 (2020).
The authors declare that they have no known competing financial or non-financial interests.
About this article
Cite this article
Kleeff, J., Ronellenfitsch, U. AI and imaging-based cancer screening: getting ready for prime time. Nat Med (2023). https://doi.org/10.1038/s41591-023-02630-y