The DOI matches its Crossref record; 14 of the 14 references it cites were found in a registry or an index.
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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“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]
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“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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Kaul, V., Enslin, S., & Gross, S. A. (2020) · Gastrointestinal Endoscopy · doi:10.1016/j.gie.2020.06.040 · Cited by 1,184
Artificial Intelligence in Cancer Research and Precision Medicine
Bhinder, B., Gilvary, C., Madhukar, N. S., & Elemento, O. (2021) · Cancer Discovery · doi:10.1158/2159-8290.cd-21-0090 · Cited by 781
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Artificial intelligence in radiation oncology
Huynh, E., Hosny, A., Guthier, C. V., Bitterman, D. S., Petit, S., Haas-Kogan, D. A., Kann, B. H., Aerts, H. J. W. L., & Mak, R. H. (2020) · Nature Reviews Clinical Oncology · doi:10.1038/s41571-020-0417-8 · Cited by 454
Artificial intelligence for clinical oncology
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Molecular Tumor Boards in Clinical Practice
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Artificial Intelligence and Mechanistic Modeling for Clinical Decision Making in Oncology
Benzekry, S. (2020) · Clinical Pharmacology & Therapeutics · doi:10.1002/cpt.1951 · Cited by 96
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The Promise of Digital Health: Then, Now, and the Future
Verily, Abernethy, A., Adams, L., National Academy of Medicine, Barrett, M., ResMed, Bechtel, C., Health, X., Brennan, P., Medicine, N. L. O., et al. (2022) · NAM Perspectives · doi:10.31478/202206e · Cited by 477
Artificial intelligence-based multi-omics analysis fuels cancer precision medicine
He, X., Liu, X., Zuo, F., Shi, H., & Jing, J. (2022) · Seminars in Cancer Biology · doi:10.1016/j.semcancer.2022.12.009 · Cited by 494
Eng, C., Yoshino, T., Ruíz‐García, E., Mostafa, N., Cann, C., O’Brian, B., Benny, A., Perez, R. O., & Cremolini, C. (2024) · The Lancet · doi:10.1016/s0140-6736(24)00360-x · Cited by 508
Machine Learning for Lung Cancer Diagnosis, Treatment, and Prognosis
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Metadata
- 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
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