Cancer care is no longer one-size-fits-all. Advances in genomics, imaging, and molecular diagnostics have made it possible to tailor treatments to the biology of each individual tumor. But as the volume and complexity of this data grow, so does the challenge of making sense of it in real time. That’s where artificial intelligence is making a difference—not by replacing clinicians, but by extending their reach.
From identifying hidden patterns in pathology slides to predicting which therapies are most likely to work, AI is helping oncologists make faster, more informed decisions. Here, we outline how AI is reshaping precision oncology today and where it’s likely to take us next.
Image Credit: Gorodenkoff/Shutterstock.com
Download your PDF copy now!
The Foundation of AI in Personalized Oncology
Cancer care today generates a staggering amount of data—think genetic sequencing, medical scans, lab reports, and detailed patient histories. The goal of precision oncology is to use all of that information to figure out what’s actually driving a person’s cancer and how best to treat it. But no human team is able to sort through that kind of complexity fast enough to make real-time decisions. That’s where AI comes in.
Machine learning models can sift through patterns in clinical data that most people wouldn’t spot, while deep learning—especially in the form of neural networks—is particularly good at handling messier, unstructured inputs like pathology slides or radiology images.1,2
In practical terms, that means AI can help doctors better understand tumor subtypes, surface potential drug targets, and even flag biomarkers that might predict how well a treatment will work. And because these models pull from such a wide range of data sources (genomics, imaging, proteomics, and more) they offer a much more complete picture of the disease. It’s like switching from a blurry snapshot to a detailed, high-res scan.2,3
Smarter, Faster Diagnosis
Early diagnosis is one of the clearest ways to improve cancer outcomes. The earlier it’s caught, the more options a patient usually has. But even with today’s advanced tools, diagnosis still often depends on human interpretation of scans, slides, and symptoms, which means there’s always some variability. AI is helping smooth that out.
Deep learning models trained on millions of medical images can now spot signs of cancer in mammograms, CT scans, and MRIs with impressive accuracy. These tools aren’t just faster; they’re sometimes better at catching things early, before symptoms even appear. Recent studies from the National Cancer Institute (NCI) show that AI-supported imaging improves detection rates and helps sort patients by risk level, which means more tailored screening plans and faster follow-up.4,5,6
And it’s not just radiology. In pathology, AI systems can analyze biopsy slides to tell the difference between benign and malignant cells, and even detect subtle features linked to treatment outcomes.1,6 Meanwhile, liquid biopsy lets AI search for circulating tumor DNA, making it possible to monitor disease progression or recurrence without needing invasive procedures.5,7
Tailoring Treatment, Down to the Details
Choosing the right treatment is one of the most complex decisions in oncology, and AI is starting to become a trusted guide in that process. Algorithms can analyze the specific mutations in a tumor, map out gene expression patterns, and examine protein activity to suggest which therapies might work best.
Tools like IBM Watson for Oncology have shown that AI recommendations can line up with expert tumor boards more than 90 % of the time in breast cancer cases. These systems are not just reliant on static guidelines, they pull from clinical trials, drug approvals, and the latest research, updating constantly.2,4
In solid tumors, AI can also be used to predicts the efficacy of chemotherapy, immunotherapy, and targeted agents by integrating clinical context with molecular information. For non-small cell lung cancer, AI-guided approaches identify patients likely to benefit from immunotherapies based on the presence of specific biomarkers. This predictive capacity allows clinicians to evade ineffective therapies and minimize treatment-related toxicity, preserving both patient well-being and healthcare resources.4,8
Even more exciting, AI isn’t just making one-time suggestions. Some platforms now track how a tumor responds to treatment over time. If things change, the system can recommend dose adjustments or new therapies. A tool called CURATE.AI, for example, fine-tunes chemotherapy dosing based on how each individual is responding, with studies showing better results and fewer side effects.9
Can AI Predict What Happens Next?
Predicting the course of the disease is a big part of making the right treatment decisions in cancer care. Oncologists constantly weigh questions like: Will this tumor come back? Is it likely to spread? How long might this treatment work? Traditionally, these predictions have been based on population-level data or scoring systems, but they can miss the nuances of an individual case.
AI is starting to change that. By combining data from pathology slides, imaging scans, genetic sequencing, and even lifestyle factors, AI models can create much more personalized forecasts. And the results have been promising. In breast and lung cancers, some AI models are already outperforming traditional tools when it comes to predicting five-year recurrence and survival rates.4,6,10
That kind of insight isn’t just useful for doctors—it’s helping patients make more informed decisions too. Whether it’s choosing a more aggressive treatment, deciding to join a clinical trial, or planning for recovery and long-term care, having a more accurate picture of what lies ahead can be a game-changer.4
Rethinking How Cancer Drugs Are Discovered
Drug development is notoriously slow and expensive, but AI is speeding things up. Instead of relying on trial-and-error in the lab, researchers are using machine learning to analyze biological pathways, pinpoint new drug targets, and predict how different compounds might interact with tumor cells.
One of the most exciting developments is how AI is helping to repurpose existing drugs. By mapping out how these medications behave across different cancer types, models can identify unexpected matches that might otherwise be overlooked.1,9
AI also plays a key role in designing smarter clinical trials. It can help match patients to studies more efficiently by analyzing who’s most likely to benefit from a new therapy. This not only accelerates research but helps patients access promising treatments sooner.1,9
On the cutting edge, researchers are pairing AI with CRISPR gene-editing tools to study how tumors develop resistance or where they might be most vulnerable. And when it comes to tailoring drugs to patients, AI-driven pharmacogenomics is showing real potential in aligning treatments with the molecular profile of each tumor.9
Making AI Work in the Real World
The potential of AI in oncology is huge, but actually bringing it into everyday clinical practice is a challenge of its own. It’s not enough for an algorithm to be accurate in a research setting; it has to be reliable, explainable, and easy for medical teams to use when it matters most.
That’s why integration is key. Increasingly, AI is being built directly into electronic health record systems, diagnostic platforms, and even mobile apps that support clinicians at the bedside.6,10 Regulatory agencies are starting to give formal approval to more of these tools, which is a sign they’re moving from experimental to essential.
Still, people—not machines—make the final call. That’s why training is a big piece of the puzzle. Hospitals and cancer centers are investing in programs to help doctors and nurses understand how AI works, what its limitations are, and how to use its recommendations safely and effectively. The goal isn’t to replace clinical judgment, but to sharpen it with better data.2,8
The Big Picture: Challenges and Ethics
AI might be doing some incredible things in oncology, but it’s not without its complications. One of the biggest hurdles is the data itself.
Data heterogeneity complicates model training, and the quality of input data influences the predictive accuracy of AI tools. Cancer data comes from all over the place—different hospitals, labs, imaging systems, patient records—and it doesn’t always play nicely together. That makes it hard to train models that work consistently across diverse patient populations. If the data is messy or biased, the AI can be too, and that’s a risk no one wants in high-stakes medical decisions.
Then there’s the question of privacy. AI systems need access to huge amounts of patient information to learn effectively, which raises tough questions about consent, security, and who really owns the data. On top of that, clinicians and patients need to understand how AI is reaching its conclusions. If a system makes a recommendation, people want to know why—not just that the math says so.2,10,11
So while the tech is advancing fast, the ethics, regulations, and real-world logistics still have some catching up to do.
Where it’s Headed
The future of AI in oncology isn’t just about bigger, faster algorithms; it’s about making them smarter, more inclusive, and easier to use. Researchers are working on models that are both accurate and interpretable so that clinicians can trust and explain the outputs. There’s also a strong push to build larger, more diverse datasets that reflect the realities of everyday clinical practice.
We’re seeing more efforts to combine data streams in real time—from genomic sequencing to imaging to lab results—all feeding into AI systems that can guide care moment by moment. This kind of integration could allow for hyper-personalized treatment strategies, where adjustments happen continuously based on how a tumor is responding.2,6,12
And while we’re not there yet, the trajectory is clear: AI is moving toward supporting every part of the cancer care journey—from risk prediction and screening to therapy and long-term follow-up.
Conclusion
AI is reshaping how we think about cancer care. It’s helping us move from a one-size-fits-all model to something more responsive, personalized, and, in many cases, more effective. Whether it’s improving early detection, guiding treatment choices, or accelerating drug discovery, AI is giving oncologists new tools to work smarter and giving patients more tailored options than ever before.
But none of this is automatic. It takes thoughtful design, responsible data use, and close collaboration between clinicians, researchers, and tech teams to make sure these systems actually serve the people they’re built for. The ongoing application and refinement of AI will further improve outcomes and drive innovations that directly address the challenges faced by cancer patients and healthcare professionals around the world.2,6,12
Want to Learn More?
Take a look at how people-centered care is reshaping cancer treatment, what innovations are driving oncology forward, and the ethical questions that arise when AI gets it wrong.
Download your PDF copy now!
References and Further Reading
- Liao, J. et al. (2023). Artificial intelligence assists precision medicine in cancer treatment. Frontiers in Oncology, 12, 998222. DOI:10.3389/fonc.2022.998222. https://www.frontiersin.org/journals/oncology/articles/10.3389/fonc.2022.998222/full
- Calvino, G. et al. (2025). From Genomics to AI: Revolutionizing Precision Medicine in Oncology. Applied Sciences, 15(12), 6578. DOI:10.3390/app15126578. https://www.mdpi.com/2076-3417/15/12/6578
- Fountzilas, E. et al. (2025). Convergence of evolving artificial intelligence and machine learning techniques in precision oncology. Npj Digital Medicine, 8(1), 1-19. DOI:10.1038/s41746-025-01471-y. https://www.nature.com/articles/s41746-025-01471-y
- How Artificial Intelligence Is Transforming Cancer Care in 2025: Diagnosis, Treatment, Clinical Trials, and Screening. (2025). OncoDaily. https://oncodaily.com/oncolibrary/artificial-intelligence-ai
- Anderson, D. Pioneering Centers for AI-Generated Precision Oncology. Medical Tourism Magazine. https://www.magazine.medicaltourism.com/article/pioneering-centers-for-ai-generated-precision-oncology
- AI and Cancer. (2024). Comprehensive Cancer Information - NCI. https://www.cancer.gov/research/infrastructure/artificial-intelligence
- Jiménez, C. Global Leaders in AI-Guided Personalized Cancer Treatment. Medical Tourism Magazine. https://www.magazine.medicaltourism.com/article/global-leaders-in-ai-guided-personalized-cancer-treatment
- Hashem, H., & Sultan, I. (2025). Revolutionizing precision oncology: The role of artificial intelligence in personalized pediatric cancer care. Frontiers in Medicine, 12, 1555893. DOI:10.3389/fmed.2025.1555893. https://www.frontiersin.org/journals/medicine/articles/10.3389/fmed.2025.1555893/full
- Mohammed, A. H. et al. (2025). Exploring the role of artificial intelligence in chemotherapy development, cancer diagnosis, and treatment: Present achievements and future outlook. Frontiers in Oncology, 15, 1475893. DOI:10.3389/fonc.2025.1475893. https://www.frontiersin.org/journals/oncology/articles/10.3389/fonc.2025.1475893/full
- Shahraki-Mohammadi A, Aliabadi A, Karimi A. (2025). Clinical Application of Artificial Intelligence in Cancer Treatment: A Systematic Literature Review. Health Scope. DOI:10.5812/healthscope-158492. https://brieflands.com/articles/healthscope-158492
- AI and Cancer: The Emerging Revolution. (2025). Cancer Research Institute. https://www.cancerresearch.org/blog/ai-cancer
- Huhulea, E. N. et al. (2025). Artificial Intelligence Advancements in Oncology: A Review of Current Trends and Future Directions. Biomedicines, 13(4), 951. DOI:10.3390/biomedicines13040951. https://www.mdpi.com/2227-9059/13/4/951
Disclaimer: The views expressed here are those of the author expressed in their private capacity and do not necessarily represent the views of AZoM.com Limited T/A AZoNetwork the owner and operator of this website. This disclaimer forms part of the Terms and conditions of use of this website.