Top 7 Ways AI is Helping Doctors Diagnose and Treat Cancer

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What’s the first thing that comes to your mind when you hear about cancer? Perhaps “passing”? If so, you’re not far from the truth. According to the World Health Organization report for 2026, 20 percent of men and 17 percent of women develop cancer during their lifetime, and 12.5 percent of men and 9 percent of women die from the illness. Cancer remains the second leading cause of death after heart disease, claiming over 500,000 lives each year.

Fortunately, technology is steadily changing this outlook. We have already seen artificial intelligence helping combat diseases such as influenza, and many experts believe it can also take on cancer. In this article, you will discover how artificial intelligence and supercomputers are joining forces to fight cancer.

1. Drug Design

Before the advent of supercomputers, developing a new drug for a life-threatening disease required enormous investment and years of work. Often, by the time a treatment reached patients, its benefits were limited. Today, supercomputers allow researchers to identify promising drugs and therapies faster and at lower cost.

For example, Shuxing Zhang of MD Anderson Cancer Center has used supercomputers to scan 1,500 existing drugs to determine which ones could inhibit TNIK, an enzyme involved in cell signaling in colon cancer patients. This approach enables rapid discovery of effective treatments for serious diseases.

2. Immunotherapy

Immunotherapy strengthens the body’s natural defenses to fight cancer. However, it does not work for every patient. Supercomputers help researchers analyze tumor responses and understand how different individuals react to this therapy, turning large and diverse datasets into actionable clinical insights.

These insights allow scientists to design more targeted clinical trials and examine genetic data related to immune proteins. As a result, cancer treatment is becoming more effective, offering greater hope to more patients.

3. Surgery

Surgery remains one of the most common cancer treatments, yet it carries risks of complications. Removing too little of a tumor, for instance, can lead to relapse. Researchers at the University of Texas have used supercomputers to perform laser therapy on a canine tumor without direct human intervention. With today’s vast amounts of imaging data, surgical simulations are helping refine techniques, and scientists are working to apply similar approaches to human cell systems.

The prospect of AI-assisted surgical systems is drawing closer, potentially allowing robotic precision in operating rooms within the coming years.

4. Radiation and Proton Therapy

While traditional X-ray radiation continues to be used, proton therapy is gaining ground. In this method, a precise beam of protons targets cancer cells without damaging surrounding healthy tissue. Because the treatment demands extreme accuracy, supercomputers play a vital role in calibrating equipment and exploring new X-ray techniques that combine real-time imaging with therapy.

5. Cancer Diagnostics

Early diagnosis remains critical. Advances such as sensitive DNA tests, body scans, and nanosensors that detect cancer cells in the bloodstream are improving detection speed. Since testing new methods directly on patients can be risky, researchers rely on supercomputer simulations to evaluate their effectiveness.

One promising development is the nanopore device, which sequences DNA by passing molecules through a tiny membrane pore and can identify cancer-related signals. Scientists are also working on nanocarriers designed to capture and analyze DNA molecules for diagnostic purposes.

6. Genomics

A single human genome contains three billion base pairs. Manually identifying mutations within this vast dataset is impractical. AI combined with supercomputing power can analyze these sequences in minutes rather than weeks, revealing patterns that support targeted therapies and helping identify individual cancer risk factors and treatment responses.

7. Patient-Specific Treatment

Just as one size does not fit all in software development, cancer treatment must be tailored to each patient. Researchers use different approaches, from data mining to mathematical modeling. Thomas Yankeelov, head of the Center of Computational Oncology, favors the latter.

Yankeelov noted: “When you have a model that can recapitulate how tumors grow and respond to treatment, it becomes a classic technology optimization problem. ‘I have this much drug and this much time. What is the optimal way to administer it to reduce the number of tumor cells for the longest possible time?’”

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