Artificial Intelligence Speeds Up Cancer Diagnoses— And Could Save Lives

Every minute counts for leukemia patients. Now, a breakthrough in artificial intelligence may help doctors make life-saving decisions faster than ever before.

At Johns Hopkins University, Associate Professor Eugene Shenderov is leading the charge with an AI-driven tool. He is a leukemia survivor himself. The tool is called the “Leukemia Smart Physician Aid.” This cutting-edge algorithm works to quickly identify acute promyelocytic leukemia (APL). APL is a rare but deadly form of cancer affecting white blood cells.

Why Speed Matters in APL

APL is known for its aggressive nature, often leading to sudden brain bleeds that can be fatal. The tragedy? It’s also one of the most curable forms of leukemia—but only if diagnosed quickly. Once identified, APL can be treated effectively with high doses of vitamin A. This treatment pushes its five-year survival rate to over 90%.

However, due to the rarity and complexity of the disease, many patients face multi-day delays in diagnosis. That’s where Shenderov’s AI model steps in—cutting diagnostic times down from days to mere hours.

“In this case, diagnosis directly leads to saving lives, period,” says Shenderov. “If you can catch it three hours into a patient’s stay, you may have saved them from brain bleeds. You potentially prevent the morbidity that brain bleeds cause. You may even prevent death.”

How the Technology Works

The Leukemia Smart Physician Aid uses a common diagnostic test. It is known as a peripheral blood smear. This test involves examining blood cells under a microscope. The AI is trained on thousands of such samples. The algorithm is capable of identifying the telltale signs of APL. It can alert doctors quickly—even in hospitals that lack advanced genetic testing equipment.

This could significantly impact low-resource or rural areas, where diagnostic delays can have catastrophic consequences. Normally, identifying APL requires genomic testing available only at major academic hospitals, sometimes across borders.

“That’s a true paradigm shift,” Shenderov adds. “This algorithm has the potential to add to existing human capacity and not merely substitute for it.”

Beyond Leukemia: Global Implications

Shenderov believes this AI-powered diagnostic model could go much further. With refinements, the algorithm might one day help detect other blood-related diseases. These include malaria, other leukemias, or even degenerative illnesses. It’s already caught the attention of NASA-affiliated researchers, who are exploring its use in space travel diagnostics.

But there’s a challenge: funding.

Due to recent federal budget cuts, many cancer research programs—including those funded by the Department of Defense—are being slashed. Entire initiatives targeting pancreatic, lung, kidney, and bladder cancers have been eliminated.

“You never know what you need in order to save a life until you’ve looked into it,” Shenderov emphasizes.

Fighting for Research Funding

Shenderov isn’t just innovating in the lab—he’s taking the fight to Washington, D.C. On April 7 and 8, he’ll join the American Society of Clinical Oncology’s Government Relations Committee. He will advocate for research funding.

“Cancer is nonpartisan,” he says. “It doesn’t care if you’re a Republican, a Democrat, or an Independent. Research dollars are there to save lives.”

Final Thoughts

Artificial intelligence continues to prove its value in healthcare. Shenderov’s AI tool for APL diagnosis may be among the most impactful applications yet. If implemented widely, it could dramatically reduce delays, improve outcomes, and save lives in ways never thought possible.

Stay tuned to our Artificial Intelligence section for more on how emerging tech is reshaping the fight against disease.