The Promise and Peril of AI in Cancer Care

Artificial intelligence is swiftly becoming a defining force in the field of oncology. By integrating complex clinical, genomic, and imaging data, AI is reshaping how cancer is detected, diagnosed, and treated. Technologies like IBM Watson for Oncology and Tempus exemplify the promise of AI in this space—using machine learning algorithms to interpret vast, multidimensional datasets in ways that enhance diagnostic precision and guide personalized therapies. These capabilities are already improving clinical outcomes and minimizing adverse effects for many patients.1,2

Yet this rapid technological ascent brings with it a host of ethical, clinical, and regulatory challenges. The same tools that enable individualized care may also introduce significant risks. Algorithmic bias, a lack of model transparency, and the possibility of misuse or overreliance on automated systems threaten to undermine both patient safety and equity in care delivery. Many AI applications in oncology still operate in environments with minimal oversight, and issues related to data quality, interpretability, and ethical use remain unresolved. As a result, the medical community is faced with a crucial imperative: to match technological innovation with robust governance frameworks that can ensure AI systems are safe, fair, and accountable.3,4

Why Oncology Demands Tailored AI Governance

Oncology, in particular, demands a specialized focus in AI governance. The stakes are exceptionally high—diagnostic inaccuracies or inappropriate treatment recommendations can lead to delayed interventions, ineffective care, or even life-threatening outcomes. The biological complexity of cancer, coupled with variation across patient populations, calls for models that are not only clinically validated but ethically designed. AI must account for diverse tumor types, genetic profiles, and demographic variables to avoid compounding existing disparities in care.5

This complexity is further heightened by the sensitivity of the data involved. Genomic sequences, advanced imaging, and detailed patient histories—central to AI-powered oncology—are also deeply personal. Inadequate safeguards around data use can jeopardize privacy and exacerbate distrust, especially in marginalized communities that already face systemic barriers in healthcare. There is growing evidence that AI tools trained on non-representative datasets may underperform or produce skewed results in such populations, reinforcing disparities rather than eliminating them.6

Despite these risks, the momentum behind AI in oncology continues to build. From decision support systems to AI-assisted pathology and radiotherapy planning, these tools are becoming increasingly embedded in clinical workflows. However, without comprehensive oversight, even well-intentioned technologies may introduce new harms. To mitigate these risks, a strong governance framework must be established—one tailored specifically to the needs and ethical complexities of oncology.7,8

Core Principles of Responsible AI in Cancer Care

A responsible AI approach in this field rests on several key principles. Foremost among them is safety. AI models must be rigorously validated, regularly updated, and continuously monitored to ensure clinical accuracy in evolving real-world contexts. Equity is equally vital. Training data must reflect the full spectrum of patient populations to avoid reinforcing structural biases. Transparency and explainability also play a central role; clinicians and patients need to understand how and why an AI system is making a recommendation, especially when that recommendation may shape the course of care.9,10

Accountability must be clearly defined. It should be explicit who bears responsibility when errors occur—whether it be developers, clinicians, or institutions—and what safeguards are in place to ensure that human oversight remains integral to every decision. Finally, privacy cannot be an afterthought. Given the sensitive nature of oncology-related data, robust protections and clear, informed consent frameworks are essential to maintain trust and uphold patient rights.

Recent efforts to develop governance models have begun to address these priorities. Proposals include establishing interdisciplinary oversight committees, adhering to evolving regulatory standards, and instituting regular equity audits to evaluate model performance across diverse populations. Documentation of AI performance metrics, risk management strategies, and sustained human-in-the-loop practices are also being emphasized as essential components of ethical deployment.11,12

Reference:

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  2. Tiwari A, Mishra S, Kuo TR. Current AI technologies in cancer diagnostics and treatment. Mol Cancer. 2025 Jun 2;24(1):159. doi: 10.1186/s12943-025-02369-9. PMID: 40457408; PMCID: PMC12128506.

  3. Muralidharan, V., Ng, M.Y., AlSalamah, S. et al. Global Initiative on AI for Health (GI-AI4H): strategic priorities advancing governance across the United Nations. npj Digit. Med. 8, 219 (2025). https://doi.org/10.1038/s41746-025-01618-x

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  5. Hassan M, Borycki EM, Kushniruk AW. Artificial intelligence governance framework for healthcare. Healthcare Management Forum. 2024;38(2):125-130. doi:10.1177/08404704241291226

  6. Goel I, Bhaskar Y, Kumar N, Singh S, Amanullah M, Dhar R and Karmakar S (2025) Role of AI in empowering and redefining the oncology care landscape: perspective from a developing nation. Front. Digit. Health 7:1550407. doi: 10.3389/fdgth.2025.1550407

  7. Stetson, P.D., Choy, J., Summerville, N. et al. Responsible Artificial Intelligence governance in oncology. npj Digit. Med. 8, 407 (2025). https://doi.org/10.1038/s41746-025-01794-w

  8. 2025 Watch List: Artificial Intelligence in Health Care: Health Technologies [Internet]. Ottawa (ON): Canadian Agency for Drugs and Technologies in Health; 2025 Mar. Available from: https://www.ncbi.nlm.nih.gov/books/NBK613808/

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  10. Lotter W, Hassett MJ, Schultz N, Kehl KL, Van Allen EM, Cerami E. Artificial Intelligence in Oncology: Current Landscape, Challenges, and Future Directions. Cancer Discov. 2024 May 1;14(5):711-726. doi: 10.1158/2159-8290.CD-23-1199. PMID: 38597966; PMCID: PMC11131133.

  11. Goel I, Bhaskar Y, Kumar N, Singh S, Amanullah M, Dhar R, Karmakar S. Role of AI in empowering and redefining the oncology care landscape: perspective from a developing nation. Front Digit Health. 2025 Mar 4;7:1550407. doi: 10.3389/fdgth.2025.1550407. PMID: 40103737; PMCID: PMC11913822.

  12. Bouderhem, R. Shaping the future of AI in healthcare through ethics and governance.Humanit Soc Sci Commun 11, 416 (2024). https://doi.org/10.1057/s41599-024-02894-w