Artificial intelligence in oncology: current applications and future perspectives
Open accessAbstract
Artificial intelligence (AI) is concretely reshaping the landscape and horizons of oncology, opening new important opportunities for improving the management of cancer patients. Analysing the AI-based devices that have already obtained the official approval by the Federal Drug Administration (FDA), here we show that cancer diagnostics is the oncology-related area in which AI is already entered with the largest impact into clinical practice. Furthermore, breast, lung and prostate cancers represent the specific cancer types that now are experiencing more advantages from AI-based devices. The future perspectives of AI in oncology are discussed: the creation of multidisciplinary platforms, the comprehension of the importance of all neoplasms, including rare tumours and the continuous support for guaranteeing its growth represent in this time the most important challenges for finalising the 'AI-revolution' in oncology.
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Luchini and colleagues summarized 71 FDA-cleared devices in oncology up to 2021 and found radiology accounted for 54.9% and pathology 19.7%.
“For example, Luchini and colleagues (194) summarized 71 FDA-cleared devices in oncology (up to 2021) and found that radiology accounted for the majority of approved devices (54.9%), followed by pathology (19.7%).”
From Artificial Intelligence in Oncology: Current Landscape, Challenges, and Future Directions (2024) · cites this paper as [194]
The same study found breast cancer was the most commonly cited cancer type (31%), most commonly associated with radiology, providing evidence of advancing maturity of AI in mammography.
“The same study also found that breast cancer was the most commonly cited cancer type (31%), most commonly associated with radiology, providing further evidence of the advancing maturity of AI in mammography.”
From Artificial Intelligence in Oncology: Current Landscape, Challenges, and Future Directions (2024) · cites this paper as [194]
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Adaptive Cancer Therapy in the Age of Generative Artificial Intelligence
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Machine learning applications in gynecological cancer: A critical review
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Artificial intelligence in fracture detection on radiographs: a literature review
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Artificial Intelligence in Oncological Hybrid Imaging
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Barriers And Challenges Of Multidisciplinary Teams In Oncology Management: A Scoping Review Protocol
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Advancing pancreatic cancer management: the role of artificial intelligence in diagnosis and therapy
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Mass Spectrometry‐Based Proteomics for Next‐Generation Precision Oncology
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- Authors
- Luchini, C., Pea, A., & Scarpa, A.
- Affiliations
- University of Verona (IT)
- Corresponding author
- Luchini, Claudio
- Scarpa, Aldo
- Venue
- British Journal of Cancer
- Published
- 2021-11-26
- Volume
- 126
- Issue
- 1
- Pages
- 4-9
- ISSN
- 0007-0920 (ISSN-L)
- 1532-1827
- PMC
- PMC8727615
- OpenAlex
- W3215393627
- Cited by
- 281
- References
- 14
- Citations by year
- 2026: 56 · 2025: 83 · 2024: 71 · 2023: 43 · 2022: 28
- FWCI
- 12.50
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Locations
- British Journal of Cancer (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Funders
- Associazione Italiana per la Ricerca sul Cancro (12182)
- Fondazione Cassa di Risparmio di Verona Vicenza Belluno e Ancona (203885/2017)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › AI in cancer detection
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Radiomics and Machine Learning in Medical Imaging
- Keywords
- artificial intelligence in oncology, AI-based medical devices, cancer diagnosis, breast cancer, lung cancer, prostate cancer, rare tumor, clinical implementation of AI
- MeSH terms
- Artificial Intelligence, Forecasting, Humans, Medical Oncology, Neoplasms
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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