Artificial intelligence in oncology: path to implementation
Open accessAbstract
In recent years, the field of artificial intelligence (AI) in oncology has grown exponentially. AI solutions have been developed to tackle a variety of cancer-related challenges. Medical institutions, hospital systems, and technology companies are developing AI tools aimed at supporting clinical decision making, increasing access to cancer care, and improving clinical efficiency while delivering safe, high-value oncology care. AI in oncology has demonstrated accurate technical performance in image analysis, predictive analytics, and precision oncology delivery. Yet, adoption of AI tools is not widespread, and the impact of AI on patient outcomes remains uncertain. Major barriers for AI implementation in oncology include biased and heterogeneous data, data management and collection burdens, a lack of standardized research reporting, insufficient clinical validation, workflow and user-design challenges, outdated regulatory and legal frameworks, and dynamic knowledge and data. Concrete actions that major stakeholders can take to overcome barriers to AI implementation in oncology include training and educating the oncology workforce in AI; standardizing data, model validation methods, and legal and safety regulations; funding and conducting future research; and developing, studying, and deploying AI tools through multidisciplinary collaboration.
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Clinical stakeholders expressed concerns around the relevance of the AI model's output, especially because the algorithm did not consider social determinants of health to predict risk outcomes for readmissions.
“Baxter et al [35] and Chua et al [49] reported clinical stakeholders’ concerns around the relevance of the AI model’s output, especially because the algorithm did not consider social determinants of health to predict risk outcomes for readmissions.”
From Barriers to and facilitators of artificial intelligence adoption in health care: scoping review (2024) · cites this paper as [49]
The “Workflow and user-design challenges” section says clinicians must find AI recommendations relevant, and “Applications” lists rehospitalization among outcomes AI can predict. Neither passage reports clinical stakeholders expressing concern about a readmission model’s output or says that such a model omitted social determinants of health. (LLM, conf=0.84)
Experimental — accuracy not yet measuredAmbiguity surrounding malpractice liability policy as it relates to physicians' legal responsibilities, for example, in case of diagnostic errors, remains a barrier to AI adoption.
“In addition, ambiguity surrounding malpractice liability policy as it relates to physicians’ legal responsibilities, for example, in case of diagnostic errors, remains a barrier to AI adoption [49].”
From Barriers to and facilitators of artificial intelligence adoption in health care: scoping review (2024) · cites this paper as [49]
Realizing AI's potential in oncology requires effective integration into clinical workflows, which presents logistical and scientific challenges.
“Realizing the potential of AI in oncology requires effective integration into clinical workflows, which presents both logistical and scientific challenges (202).”
From Artificial Intelligence in Oncology: Current Landscape, Challenges, and Future Directions (2024) · cites this paper as [202]
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Artificial intelligence: A powerful paradigm for scientific research
Xu, Y., Liu, X., Cao, X., Huang, C., Liu, E., Qian, S., Liu, X., Wu, Y., Dong, F., Qiu, C.-W., et al. (2021) · The Innovation · doi:10.1016/j.xinn.2021.100179 · Cited by 1,726 · see citing works
Barriers to and facilitators of artificial intelligence adoption in health care: scoping review
Hassan, M., Kushniruk, A., & Borycki, E. (2024) · JMIR Human Factors · doi:10.2196/48633 · Cited by 324 · see citing works
Artificial Intelligence in Oncology: Current Landscape, Challenges, and Future Directions
Lotter, W., Hassett, M. J., Schultz, N., Kehl, K. L., Van Allen, E. M., & Cerami, E. (2024) · Cancer Discovery · doi:10.1158/2159-8290.cd-23-1199 · Cited by 196 · see citing works
Self-citationThe application and use of artificial intelligence in cancer nursing: A systematic review
O'Connor, S., Vercell, A., Wong, D., Yorke, J., Fallatah, F. A., Cave, L., & Anny Chen, L.-Y. (2024) · European Journal of Oncology Nursing · doi:10.1016/j.ejon.2024.102510 · Cited by 51 · see citing works
Froicu, E.-M., Creangă-Murariu, I., Afrăsânie, V.-A., Gafton, B., Alexa-Stratulat, T., Miron, L., Pușcașu, D. M., Poroch, V., Bacoanu, G., Radu, I., et al. (2025) · Current Oncology Reports · doi:10.1007/s11912-025-01698-8 · Cited by 38 · see citing works
Hesso, I., Kayyali, R., Dolton, D.-R., Joo, K., Zacharias, L., Charalambous, A., Lavdaniti, M., Stalika, E., Ajami, T., Acampa, W., et al. (2023) · Radiation Oncology · doi:10.1186/s13014-023-02351-z · Cited by 33 · see citing works
Explainable artificial intelligence for multi-modal cancer analysis: from genomics to immunology
Zhang, M., & Yin, L. (2026) · Critical Reviews in Oncology/Hematology · doi:10.1016/j.critrevonc.2025.105040 · Cited by 5 · see citing works
- Authors
- Chua, I. S., Gaziel‐Yablowitz, M., Korach, Z. T., Kehl, K. L., Levitan, N. A., Arriaga, Y. E., Jackson, G. P., Bates, D. W., & Hassett, M.
- Affiliations
- Brigham and Women's Hospital (US)
- Division of General Internal Medicine and Primary Care Department of Medicine Brigham and Women's Hospital Boston MA USA
- Harvard University (US)
- IBM (United States) (US)
- Corresponding author
- Chua, Isaac S.
- ORCID
- Venue
- Cancer Medicine
- Publisher
- Wiley
- Published
- 2021-05-07
- Volume
- 10
- Issue
- 12
- Pages
- 4138-4149
- ISSN
- 2045-7634 (ISSN-L)
- PMC
- PMC8209596
- OpenAlex
- W3157129994
- Cited by
- 179
- References
- 87
- Citations by year
- 2026: 34 · 2025: 48 · 2024: 43 · 2023: 34 · 2022: 18 · 2021: 2
- FWCI
- 1.97
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Cancer Medicine (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- Brigham and Women's Hospital
- Topics
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Physical Sciences › Computer Science › Artificial Intelligence › AI in cancer detection
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Radiomics and Machine Learning in Medical Imaging
- Keywords
- artificial intelligence in oncology, AI implementation barriers, clinical decision support, access to cancer care, clinical workflow integration, AI model validation, precision oncology, multidisciplinary collaboration
- MeSH terms
- Machine Learning, Artificial Intelligence, Data Collection, Humans, Image Interpretation, Computer-Assisted, Medical Oncology, Bias, Decision Support Systems, Clinical, Precision Medicine, Research Report
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