Reimagining cancer treatments in the era of generative AI
Open accessAbstract
Significant advances in the treatment of cancer have been achieved as reflected by the ever-expanding space of cancer therapeutics being available to cancer patients. Often, however, it is not clear which patient would respond to which drug and what combination of drugs will improve patient outcomes. Furthermore, while many of these drugs are initially effective, therapeutic resistance is often inevitable due to the evolving nature of cancer. Generative artificial intelligence (GenAI) powered by the increasingly large amount of accumulating clinical, molecular, and radiomics data about cancer patients and their treatments may serve as the kernel of rapid learning decision-support systems that could enable personalized cancer treatments to counter therapeutic resistance and overcome the shortcomings of the current standard of care. This perspective is explored in the context of current advances of AI applications in oncology and the potential of GenAI learning and inferencing capabilities to support patient-tailored dynamic cancer treatments. A discussion of this vision is elaborated with respect to issues pertinent to GenAI use in real-world clinical settings, including clinical validation, data curation, and sharing, large language model hallucinations as well as ethical concerns and considerations such as privacy, bias, transparency, and accountability.
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Chain-of-thought reasoning produces narrative descriptions of apparent reasoning without exposing the actual generative process.
“Chain-of-thought reasoning, often presented as evidence that LLMs are interpretable, produces narrative descriptions of apparent reasoning without exposing the actual generative process”
From A generative AI multi-agent framework with integrated XAI governance for cancer diagnostics: from multi-omics interpretation to lifestyle risk stratification (2026) · cites this paper as Derbal, 2025
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Self-citationLakatos, E., Hockings, H., Mossner, M., Huang, W., Lockley, M., & Graham, T. A. (2021) · iScience · doi:10.1016/j.isci.2021.102889 · Cited by 12 · see citing works
Mukherjee, U., Hockings, H., Counsell, N., et al. (2024) · BMJ Open · doi:10.1136/bmjopen-2024-091262 · Cited by 12 · see citing works
Adaptive Treatment of Metastatic Prostate Cancer Using Generative Artificial Intelligence
Derbal, Y. (2025) · Clinical Medicine Insights Oncology · doi:10.1177/11795549241311408 · Cited by 10 · see citing works
Self-citationAdaptive Control of Tumor Growth
Derbal, Y. (2024) · Cancer Control · doi:10.1177/10732748241230869 · Cited by 10 · see citing works
Self-citationON MODELING OF LIVING ORGANISMS USING HIERARCHICAL COARSE-GRAINING ABSTRACTIONS OF KNOWLEDGE
Derbal, Y. (2012) · Journal of Biological Systems · doi:10.1142/s0218339013500083 · Cited by 9 · see citing works
Self-citationCell Adaptive Fitness and Cancer Evolutionary Dynamics
Derbal, Y. (2023) · Cancer Informatics · doi:10.1177/11769351231154679 · Cited by 7 · see citing works
Self-citationGenerative Artificial Intelligence as a Catalyst for Effective Cancer Treatments
Derbal, Y. (2025) · AI in Clinical Medicine · doi:10.14740/aicm5 · Cited by 3 · see citing works
Self-citation
Flagged first, then by citations
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Cited by
Sorted by citations
Karni, J. E., Simon, C., & Hack, S. (2026) · Digital Health · doi:10.1177/20552076261450749 · Cited by 2 · see citing works
Barki, C., Chouchen, M., Sediri, A., Ceylan, H. İ., Rahmouni, H. B., Muntean, R. I., El-Seedi, H. R., Bragazzi, N. L., & Dergaa, I. (2026) · Frontiers in Systems Biology · doi:10.3389/fsysb.2026.1892418 · Cited by 1 · see citing works
“Chain-of-thought reasoning, often presented as evidence that LLMs are interpretable, produces narrative descriptions of apparent reasoning without exposing the actual generative process (Derbal, 2025).”
InfluentialWang, X., Li, L., Cai, X., Luo, W., Shen, M., Zuo, Y., Zhang, J., Bai, Z., Yao, Y., Zhang, M., et al. (2026) · Frontiers in Public Health · doi:10.3389/fpubh.2026.1779200
“For cancer survivors, who may already face stigma and discrimination concerns, privacy anxiety may meaningfully constrain willingness to adopt patient-facing GenAI, even when perceived usefulness is high (34).”
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Self-citation
- Authors
- Derbal, Y.
- Affiliations
- Toronto Metropolitan University (CA)
- Corresponding author
- Derbal, Youcef
- ORCID
- Derbal, Youcef
- Venue
- DIGITAL HEALTH
- Publisher
- SAGE Publications
- Published
- 2025-05
- Volume
- 11
- Pages
- 20552076251394631-20552076251394631
- ISSN
- 2055-2076 (ISSN-L)
- PMC
- PMC12583873
- OpenAlex
- W4415818745
- Cited by
- 4
- References
- 96
- Citations by year
- 2026: 4
- FWCI
- 0.24
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Digital Health (publishedVersion, cc-by-nc-nd)
- Europe PMC (PubMed Central) (publishedVersion, cc-by-nc-nd)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- 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
- generative AI, cancer therapy, personalized medicine, treatment resistance, decision support system, large language models, hallucinations, clinical validation, AI ethics, privacy, algorithmic bias, transparency and accountability, radiomics
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