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AI in cancer research is used to find patterns in biological and clinical data, generate predictions, and support research decisions. Its roles now span cancer biology, drug discovery and repurposing, treatment-response prediction, screening and diagnosis, disease surveillance, and the design and operation of clinical trials.
The important qualification is that a research model or promising prediction is not automatically a safe, effective, regulator-cleared tool for routine care. Evidence depends on the task, the data used, the patient population, prospective testing, reproducibility, and whether the model improves a real clinical workflow.
What AI means in cancer research
The U.S. Food and Drug Administration (FDA) Oncology Center of Excellence defines artificial intelligence as “a machine-based system that can, for a given set of human-defined objectives, make predictions, recommendations, or decisions influencing real or virtual environments.” In oncology, those objectives might include identifying patterns in tumor biology, ranking drug candidates, estimating a patient’s likely response, flagging an image for review, or helping a trial team find eligible volunteers.
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AI is an umbrella term rather than one single technique. Cancer researchers may use machine learning, deep learning, natural-language processing, statistical models, and other computational methods. The common feature is that software learns relationships from data or applies a model to produce a prediction or recommendation. The model’s usefulness is determined by the question it was built to answer and by the quality and representativeness of its training and test data.
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National Cancer Institute (NCI) overviews describe AI activity across the cancer research pipeline, from laboratory biology through screening, diagnosis, treatment research, surveillance, and health-care delivery. A model developed for one cancer, population, device, or workflow cannot be assumed to work equally well in another setting.
How AI supports cancer-drug discovery
Finding biological patterns and possible targets
Cancer generates complex data about genes, proteins, cells, tumors, treatments, and outcomes. AI can examine these high-dimensional datasets to identify patterns that may be difficult to detect manually. Researchers use those patterns to study mechanisms of cancer, characterize tumor behavior, and suggest biological targets for further laboratory work.
A model can prioritize hypotheses; it does not by itself prove that a target causes cancer or that changing it will help patients. Laboratory experiments, animal studies where appropriate, and clinical research are still needed to test whether a computationally identified mechanism is real and therapeutically useful.
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Designing and prioritizing drug candidates
AI methods can help researchers search chemical and biological possibilities, predict properties of candidate compounds, and decide which experiments are most informative. This can reduce the number of candidates that require expensive laboratory testing, but a prediction remains a selection aid until experiments confirm activity, safety, dosing, and manufacturing feasibility.
Researchers also use AI to explore drug repurposing: identifying existing medicines whose known or predicted biological effects might be relevant to a different cancer or indication. Repurposing hypotheses still require evidence for the new use, including appropriate dosing, interactions, toxicity, and clinical benefit.
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Predicting treatment response
Another use is estimating how a tumor or individual cancer cell may respond to a treatment. Models may combine molecular measurements, pathology, imaging, or clinical information to identify response patterns. The intended benefit is more informed research and, eventually, better matching of therapies to tumor biology.
One NCI-described example is PERCEPTION, a proof-of-concept tool developed by NIH researchers. It used gene-expression data from individual tumor cells to predict responses to cancer drugs. The April 18, 2024 NCI report presents PERCEPTION as a potential future way to match drugs to patients, not as a routinely available clinical test or a validated replacement for a clinician’s treatment decision.
How AI is used in screening and diagnosis
Screening and early detection research
Screening models analyze images or other measurements to identify patterns associated with possible cancer. In research, AI may help investigators evaluate large datasets, find subtle features for further study, or prioritize cases for human review. The exact task matters: detecting an abnormality, estimating cancer risk, and confirming a diagnosis are different problems with different evidence requirements.
Diagnostic support
AI can be trained on imaging, pathology, laboratory results, electronic health records, or combinations of these data. A model might classify images, highlight regions for a specialist, or provide a probability estimate. Such outputs are generally intended to support a clinical team rather than replace examination, pathology, multidisciplinary review, or patient-specific judgment.
Performance can change when equipment, image protocols, disease prevalence, clinical practice, or patient demographics differ from the development data. A model that performs well retrospectively at one institution may produce more false positives, false negatives, or unusable results elsewhere. Validation therefore needs to reflect the population and workflow in which the tool will be used.
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What broad claims cannot establish
The available NCI and FDA overviews establish that AI is being investigated across screening and diagnosis, but they do not provide a head-to-head comparison of named diagnostic products or a single accuracy number that applies to all cancers. Claims about sensitivity, specificity, survival, or reduced workload must be tied to a particular model, cancer, dataset, study design, and date.
AI in cancer clinical trials
Finding and matching eligible participants
Trial eligibility criteria can involve diagnosis, stage, prior treatment, laboratory values, biomarkers, organ function, and timing. Informatics and AI systems may help search records, extract relevant information, and identify people who could be evaluated for a study. A potential match is not enrollment: the research team must confirm the criteria, discuss the study, obtain informed consent, and complete required screening.
Monitoring safety and disease progression
AI may help trial teams organize adverse-event information, examine longitudinal records, monitor measurements, or detect patterns that warrant human review. These applications can support oversight, but the system must be tested for missed events, spurious alerts, delayed data, and differences between participating sites. Responsibility for safety reporting remains with the trial investigators and sponsors under the study’s rules.
Improving trial design and representativeness
FDA scientific-interest areas include AI and machine-learning methods for clinical-trial design, real-world data, data quality, scientific literature, and regulatory decision-making. Researchers may use computational tools to model eligibility criteria, estimate recruitment challenges, identify underrepresented groups, or evaluate how design choices affect feasibility.
NCI describes clinical trials as methodical studies that test interventions and move approaches from laboratory research toward clinical settings. An AI system used to plan, monitor, or analyze a trial should not be confused with evidence that an AI-directed intervention improves patient outcomes. The intervention still needs appropriate clinical testing.
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From a research model to a trustworthy clinical tool
Moving from an interesting model to a dependable application requires more than a high score on a retrospective dataset. The following dimensions determine whether results can be trusted in the intended setting.
| Question | Why it matters | Evidence to look for |
|---|---|---|
| What task does the model perform? | Discovery, screening, diagnosis, response prediction, and trial operations have different risks and success criteria. | A precise intended-use statement and defined users. |
| What data trained and tested it? | Labels, missing data, measurement methods, and disease prevalence affect results. | Clear descriptions of data sources, reference standards, and exclusions. |
| Who was represented? | A model can underperform for groups absent or underrepresented in development data. | Performance reported across relevant demographic, geographic, clinical, and technical subgroups. |
| How was it tested? | Retrospective testing can overestimate usefulness in real care. | Independent, multi-site, prospective, or randomized evaluation when appropriate. |
| Does it improve the workflow? | Accuracy alone may not reduce delays, errors, unnecessary procedures, or patient harm. | Evidence from the intended clinical process, including human-AI interaction and outcomes. |
| Can users understand and challenge results? | Clinicians need enough context to recognize uncertainty and inappropriate outputs. | Explainability, confidence information, documentation, and a clear escalation path. |
| How is performance maintained? | Data, devices, clinical practice, and disease patterns change over time. | Monitoring, recalibration, version control, incident review, and update procedures. |
Major limitations and risks
Bias from incomplete or unrepresentative data
NCI warns that training data lacking diversity or representation can perpetuate bias. If a model learns from one population, hospital network, imaging device, or documentation style, its predictions may be less reliable for people or sites that differ. Bias can enter through who was tested, who received a diagnosis, how outcomes were recorded, and which cases were excluded.
Researchers should report subgroup performance and investigate missingness, label quality, and access-related differences. A single overall average can conceal clinically important failures in a smaller group.
Reproducibility and data quality
Results can be difficult to reproduce when datasets, preprocessing steps, code, model versions, or evaluation criteria are not sufficiently documented. FDA’s oncology AI work includes methods related to data quality and real-world evidence because unreliable inputs can produce unreliable outputs even when the algorithm itself is sophisticated.
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Many AI systems provide a prediction without a simple causal explanation. Explainable AI does not make a model automatically correct, but it can help users inspect relevant features, recognize out-of-scope cases, and communicate uncertainty. Integration also matters: an accurate result that arrives too late, cannot be reviewed, or creates excessive alerts may not improve care.
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Privacy and governance
Cancer datasets can contain identifiable health information and sensitive genomic data. Responsible projects need appropriate consent or legal authority, access controls, secure handling, documented data use, and governance for sharing and reuse. The exact requirements depend on jurisdiction, institution, study design, and the type of data.
How to evaluate an AI cancer-research claim
- Define the job. Ask whether the system is discovering a target, ranking a drug, estimating response, screening images, supporting diagnosis, matching trial candidates, or monitoring a study.
- Identify the population and setting. Check the cancer type, disease stage, treatment history, geography, care setting, equipment, and other characteristics represented in development and testing.
- Separate prediction from outcome. A model may predict response without proving that using the prediction improves survival, quality of life, safety, or access.
- Examine the evaluation design. Determine whether testing was retrospective or prospective, internal or independent, single-site or multi-site, and whether a randomized clinical trial was appropriate for the claim.
- Look for subgroup and failure information. Review false positives, false negatives, uncertain cases, missing data, out-of-scope inputs, and performance across relevant groups.
- Check human oversight. Identify who reviews the output, what happens when the model is uncertain, and whether clinicians can override it with documented reasons.
- Verify current status. Establish whether the system is a research prototype, an investigational tool, or an authorized product for a specific use. Do not infer regulatory clearance or clinical availability from a conference presentation or proof-of-concept report.
What researchers and patients should remember
For researchers, AI is most useful when it is connected to a well-defined question, high-quality data, appropriate controls, and a validation plan that matches the intended use. AI can narrow a search space, reveal associations, and help coordinate complex studies, but laboratory and clinical methods remain necessary to establish causation, safety, and benefit.
For patients, an AI mention in a study description does not by itself indicate that treatment is experimental, superior, or unsafe. Ask what the system does, whether it affects your care or only the research process, what human review is provided, how your data are protected, and what evidence supports the specific use. A prediction from a research model should not be used to start, stop, or change cancer treatment without discussion with the qualified clinical team responsible for care.
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Where to find legitimate research resources
NCI maintains resources and tools related to AI in cancer research, including software, datasets, and models intended for researchers. These directories are research resources, not consumer diagnostic services or personalized treatment recommendations. FDA’s Oncology Center of Excellence also publishes information on oncology AI and scientific-interest areas, including methods for drug development, regulatory science, real-world data, and clinical trials.
These public resources can help investigators understand the field, but users should still verify documentation, licensing, data-access requirements, validation results, and the model’s intended population before incorporating any tool into a study.
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