Uses and Limitations of Artificial Intelligence for Oncology
Open accessAbstract
Modern artificial intelligence (AI) tools built on high-dimensional patient data are reshaping oncology care, helping to improve goal-concordant care, decrease cancer mortality rates, and increase workflow efficiency and scope of care. However, data-related concerns and human biases that seep into algorithms during development and post-deployment phases affect performance in real-world settings, limiting the utility and safety of AI technology in oncology clinics. To this end, the authors review the current potential and limitations of predictive AI for cancer diagnosis and prognostication as well as of generative AI, specifically modern chatbots, which interfaces with patients and clinicians. They conclude the review with a discussion on ongoing challenges and regulatory opportunities in the field.
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In oncology, AI models are being developed to standardize and improve the consistency and quality of care and to support clinicians and patients in navigating complex diagnostic and treatment pathways.
“In oncology, AI models are being developed to standardize and improve the consistency and quality of care, offering new ways to support both clinicians and patients in navigating complex diagnostic and treatment pathways [5].”
From Empathic AI for Patient-Centered Cancer Care: A Scoping Review of Patient Navigation, Support, and Clinical Practice (2026) · cites this paper as [5]
The Introduction says AI can improve cancer screening, diagnosis, and treatment decisions, while the Chatbots section describes potential support for patients and clinicians. The paper does not establish that AI models are being developed to standardize care or improve its consistency; its Discussion instead calls for standardized data collection and reliable algorithms. Page numbers are unavailable in our copy. (LLM, conf=0.83)
Experimental — accuracy not yet measuredModern AI platforms increasingly include built-in traceability features that automatically log training runs, data lineage, and performance metrics.
“Modern AI platforms increasingly include built-in traceability features that automatically log training runs, data lineage, and performance metrics [137,138].”
From Artificial Intelligence in Clinical Oncology: From Productivity Enhancement to Creative Discovery (2025) · cites this paper as [137]
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References 39 · Cited by 126
Research works printed but not linked: 2
- Janiesch C, Zschech P, Heinrich K. Machine learning and deep learning. Electron Mark. 2021;31(3):685-695. doi:10.1007/s12525- 021-00475-2 · DOI · Paper page
- Molnar C, König G, Bischl B, Casalicchio G. Model-agnostic feature importance and effects with dependent features: a conditional subgroup approach. Data Min Knowl Discov. Published online January 10, 2023. doi:10.1007/s10618-022-00901-9 · DOI
- Authors
- Kolla, L., & Parikh, R. B.
- Affiliations
- University of Pennsylvania (US)
- Philadelphia VA Medical Center (US)
- Corresponding author
- Parikh, Ravi B.
- Venue
- Cancer
- Publisher
- Wiley
- Published
- 2024-03-30
- Volume
- 130
- Issue
- 12
- Pages
- 2101-2107
- ISSN
- 0008-543X (ISSN-L)
- 1097-0142
- PMC
- PMC11170282
- OpenAlex
- W4393336895
- Cited by
- 126
- References
- 39
- Citations by year
- 2026: 62 · 2025: 51 · 2024: 12 · 2023: 1
- FWCI
- 2.85
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Cancer (publishedVersion, cc-by-nc)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- Funders
- National Cancer Institute (K08CA263541)
- U.S. National Library of Medicine (F31LM014282)
- 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, predictive AI, cancer diagnosis, generative AI, chatbots, algorithmic bias, data quality, clinical workflow efficiency, regulatory challenges, goal-concordant care
- MeSH terms
- Algorithms, Artificial Intelligence, Humans, Medical Oncology, Neoplasms, Prognosis
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