Cancer diagnostics faces a fundamental challenge: finding tumors early enough to save lives. Traditional methods such as biopsies and scans often catch cancer too late, when treatment options become limited and survival rates drop significantly. Frederic Scheer, with his extensive background in science and multiple PhDs, is tackling this problem through his work at Alercell, where he’s combining artificial intelligence with genomics to revolutionize how we detect cancer through simple blood tests.
Identifying Limits of Traditional Testing
Scheer doesn’t sugarcoat the problem. “Cancer kills, and it’s mostly because we find it late,” he says. Traditional detection waits until tumors are large enough to see or feel. By that point, the disease has often spread and treatment becomes far more difficult. Patients are left facing chemotherapy and radiation, what he calls “grueling treatments” that might have been avoided with earlier detection. That is why his team at Alercell is taking a different path. Instead of waiting for tumors to grow, they search for the genetic footprints cancer leaves behind in blood samples. “Think of your DNA and RNA as the instruction manuals for your cells,” Scheer explains. “When cancer starts, it rewrites those instructions. We need to spot those changes early, before the disease takes hold.”
Most genetic testing today focuses on mutations, the spelling mistakes in DNA. Scheer looks beyond that. “Genomics is about the signals around DNA, RNA and other Omics, the chemical marks that switch genes on or off,” he says. These subtle changes often appear before traditional genetic tests would notice anything. By studying how DNA fragments are processed and the messages RNA sends through the body, his team can identify the disruptions cancer causes at the very start. The challenge is enormous: detecting tiny, almost invisible changes hidden among millions of normal signals. But Scheer is convinced the payoff is worth it. It means a chance to find cancer when it is still silent and stop it before it demands the harshest treatments.
Using AI to Find Hidden Patterns
This is where artificial intelligence becomes crucial, though not in the way people often fear. “Ignore the buzzwords. Everybody is talking about AI these days,” Scheer says. “AI is simply pattern-finding software. It can identify signals that the human brain cannot see.” The technology is not about replacing doctors. It is about giving them sharper tools. “AI can analyze millions, even billions of data points that no human brain could process,” Scheer explains. “That means it can deliver insights that allow doctors to make decisions based on complete information rather than fragments.” Still, he stresses, human oversight is essential. AI systems are only as good as the data they are trained on, and bias can creep in. “Assume you have a database of people with liver cancer in China,” he says. “Can you apply those findings to people living in New York or Paris? The answer is no. The dataset is biased.” That is why, in Scheer’s view, doctors remain at the center of the process. AI can surface patterns and possibilities, but it takes medical professionals to judge whether those insights truly apply to each patient in front of them.
Bringing Diagnostics to More Patients
One major advantage of blood-based testing is cost and accessibility. Complex scans and biopsies require expensive equipment and specialized facilities. Blood tests are much cheaper and can be processed anywhere with proper laboratory capabilities. Scheer lives in Bozeman, Montana, a town of 50,000 people. “We don’t have the Mayo Clinic here, but if we have access to the same data they do, it changes everything,” he points out. Cloud-based AI analysis could bring sophisticated diagnostic capabilities to smaller communities that lack major medical centers.
The field is evolving rapidly. He notes that genetic testing for cancer patients increased from just 5% in 2003 to 55% today. The FDA projects this will reach 100% by 2035. “We realize that the genome is giving us a lot of information,” he observes. This shift requires new types of medical professionals. “I think that the doctors of the 21st century are going to be in the middle between biochemists and medical doctors and AI engineers,” Scheer predicts. He’s experienced this firsthand, recently completing additional studies at MIT in AI healthcare and Harvard Medical School in genetics, despite already holding two PhDs. His team is even exploring quantum computing for future applications. “When you work with quantum computing, it’s based on linear algebra, and therefore there is a logic behind it that the human brain feels more comfortable about,” he explains. The goal remains straightforward: catch cancer when it’s still treatable, using tools that are accessible and affordable for patients everywhere.
Connect with Frederic Scheer on LinkedIn to follow his groundbreaking work in AI-driven cancer detection.
