AI can carry bias into cancer care when its data or evaluation do not represent the people who will use the system. But the evidence summarized here does not establish that a currently deployed cancer AI system has denied a named racial group life-saving treatment. That distinction matters: unequal representation is a real concern, while a claim that AI caused a specific treatment denial requires evidence about the system, its clinical use and the patient outcome.
How could AI bias affect cancer care?
A cancer-care model learns patterns from the data used to train it. If some groups are underrepresented, if measurements are less complete or reliable for some patients, or if the people treated differ from the model’s development population, its predictions may be less reliable for some groups.
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Depending on its purpose and where it is used, a prediction could inform risk assessment, diagnosis, referral to a clinical trial or a treatment decision. That creates a plausible route by which a poorly validated model could contribute to unequal care. It does not mean the model independently makes every clinical decision, or that a biased output necessarily results in a denied treatment. Establishing harm requires examining how a particular tool was used in practice and what happened to patients.
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What does the evidence say about representation?
Representation varies by dataset; one dataset’s composition cannot stand in for every cancer AI system. In a presentation hosted by the U.S. Food and Drug Administration (FDA) on November 20, 2024, Faisal Mahmood reported that 82.0% of all cases in The Cancer Genome Atlas (TCGA) were from patients with European ancestry. In the presentation’s TCGA lung adenocarcinoma cohort of 566 people, the listed proportions were 68.6% White, 9.2% Black and 1.4% Asian.
Those figures describe TCGA and the specified lung adenocarcinoma cohort—not all cancer datasets, all AI training data or the makeup of patients receiving cancer treatment. They demonstrate why dataset composition needs to be checked for each model, rather than serving as proof that any particular model is biased or has caused harm.
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Does cancer AI work as well for Black patients?
There is no single answer for cancer AI as a whole. A 2025 study in JCO Clinical Cancer Informatics evaluated a multimodal model using data from 5,708 patients in five randomized phase III prostate cancer trials. It reported performance across African American and non-African American subgroups and found no evidence of algorithmic bias in the evaluated setting.
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That is useful but bounded evidence. It concerns one model, prostate cancer trial data, the study’s chosen endpoints and those two reported subgroups. It does not establish performance for every model, cancer type, population or clinical workflow. Nor does a finding about model performance by subgroup establish whether patients have equal access to diagnosis, treatment or clinical trials.
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Why isn’t removing race always the answer?
Fairness is not automatically achieved by deleting race from a model’s inputs. A 2024 Proceedings of the National Academy of Sciences study on colorectal cancer risk prediction found that race adjustment could improve performance in a setting where family-history data quality varied by race. The example shows why a feature’s role and the quality of the measurements matter: a model may need to account for unequal data quality rather than simply ignore the variable associated with it.
The Agency for Healthcare Research and Quality’s evidence review, “Impact of Healthcare Algorithms on Racial and Ethnic Disparities in Health and Healthcare,” is another source for assessing how algorithmic design and data quality relate to disparities. In practice, the relevant question is whether a model’s inputs, measurements, intended use and validation are appropriate for the task—not whether one rule, such as removing race, is applied to every system.
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What should a meaningful fairness evaluation check?
A headline accuracy figure cannot answer every question about fairness or access. A careful evaluation should identify the model’s intended clinical task and examine its performance in the populations and settings where it may be used.
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- What does “performance” mean? Assess predictive accuracy and calibration by group for the intended task. These are distinct from fairness metrics, access to care and patient outcomes.
- Does the evaluation match clinical use? A retrospective analysis or trial-dataset study does not by itself show how a tool performs in a different population or a live workflow.
- What happens after a prediction? Examine how clinicians or health systems use the output, whether patients can reach the recommended follow-up, and whether actual treatment access or patient benefit is measured.
- Is performance monitored over time? Models need evaluation in relevant clinical workflows; a mismatch between development data and the population where a tool is used can undermine reliability.
Similar measured performance across two groups would not, by itself, prove equal access to care. Conversely, an imbalanced dataset alone does not prove that a model caused a harmful decision. Those are separate questions requiring different evidence.
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What cancer AI research is—and is not—ready for clinical use?
NCI’s PERCEPTION project illustrates the difference between a promising research tool and an established treatment-selection system. The proof-of-concept tool uses single-cell RNA sequencing to predict drug response from tumor cell populations. Researchers built models for 44 FDA-approved cancer drugs using cell-line data, then evaluated published patient datasets that included multiple myeloma, breast cancer and non-small cell lung cancer. NCI notes that single-cell sequencing costs more than bulk sequencing and is not yet widely available in clinical care. The project is research with potential, not evidence that this tool is a routine bedside method for choosing cancer treatment.
Regulators are also paying attention to the field. The FDA’s Oncology Center of Excellence says its Oncology AI Program began in 2023 in response to growing interest in oncology AI. That indicates active regulatory and research attention; the program’s existence alone does not establish a particular approval, rule or finding about a specific system.
How does AI relate to unequal trial access?
AI bias and unequal access to cancer research or treatment can intersect, but one should not be mistaken for proof of the other. NCI describes Black and Latino underrepresentation in NCI-supported clinical trials and lists the ECOG-ACRIN SUPPORT trial, which tests navigation and research-literacy approaches to diversity participation and enrollment. This evidence concerns barriers to trial participation; it does not show that AI caused underrepresentation or denied treatment.
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