Mayo Clinic researchers are launching a sweeping initiative called Precure Research to identify molecular and physiological changes that signal disease years or even decades before patients develop clinical symptoms, using artificial intelligence to analyze genetic information, biological specimens, medical records, environmental exposures, and wearable device data together. The effort represents one of medicine's most transformational ambitions: shifting healthcare from treating established disease to predicting and preventing it.

Why Catching Disease Early Matters More Than You Think

By the time doctors diagnose Alzheimer's disease, heart failure, or cancer, the biological processes behind the illness may have been unfolding silently for years or even decades. Mayo Clinic researchers say recent advances are beginning to make early detection more solvable. In one large genetic study, Mayo scientists identified inherited risks for cancer and cardiovascular disease in nearly 2,000 participants, many of whom had no previous indication that they carried the risks. Other Mayo research has found certain precancerous changes that can be detected years before cancer develops, and in Alzheimer's disease, scientists increasingly can measure biological changes that begin decades before memory problems become apparent.

The challenge is enormous: among the countless biological changes that occur throughout a person's life, which ones reliably signal that disease is beginning, and which can doctors safely intervene on? Precure Research initially focuses on diseases of the brain, heart, kidneys, liver, and lungs, bringing together multiple layers of biological information to reconstruct how a patient's biology arrives at disease.

"We're not just treating disease anymore. We're partnering with people throughout their entire life journey," said Heidi Dieter, Mayo Clinic's chief research administrator.

Heidi Dieter, Chief Research Administrator at Mayo Clinic

How AI Transforms Disease Detection Before Symptoms Appear

• Multimodal Data Integration: AI systems combine genetic variants, proteins, metabolites, immune signals, longitudinal medical records, environmental information, and wearable device readings to identify patterns that conventional analysis might miss.
• Exposome Analysis: Researchers are incorporating the exposome, the accumulation of environmental and lifestyle exposures over a person's lifetime, including diet, exercise, sleep, pollutants, chemicals, and air quality, detecting biological traces of these exposures in blood and tissue specimens.
• Longitudinal Context: Mayo Clinic Platform connects molecular information with deep, longitudinal clinical context and expertise through existing pathways like Digital Pathology and Research Data Atlas to support discovery across diseases and data types.

AI is central to Precure's strategy because the number of possible relationships between biological variables quickly exceeds what researchers can examine manually. A person might carry a genetic variant associated with higher disease risk for a lifetime without developing the condition. Researchers hope to understand what happens between inherited susceptibility and actual illness and identify when that trajectory might change.

Pilot studies are already examining real-world questions: whether long-term air pollution exposure affects solid organ transplant outcomes, whether environmental chemicals influence how the body processes medications, and whether saliva-based biosensors can detect biological changes associated with triggers of head and neck cancers.

What Does This Mean for the Future of Medicine?

The broader healthcare industry is experiencing rapid transformation driven by AI adoption. According to Markets and Markets, the AI in healthcare market is projected to witness a 39.7% compound annual growth rate through 2031, driven by growing demand for automation, increasing clinical complexity, and substantial investment in predictive analytics, imaging AI, and generative AI. In 2026, the focus has shifted from experimentation to scaled deployment, particularly in high-return-on-investment areas such as clinical documentation, imaging analysis, and decision support.

However, finding a biological signal associated with disease is not the same as developing a useful screening test. Researchers must look for biological signals that can develop into screening tests helping physicians intervene early, often when a condition is more easily and effectively treated. Mayo's approach extends beyond what is happening physiologically inside the body, incorporating environmental and lifestyle factors that shape disease risk over time.

"At Mayo Clinic, our research and practice are intertwined. Everything we do must serve our primary value of putting the needs of the patient first," said Vijay Shah, Mayo Clinic's Kinney executive dean of research.

Vijay Shah, Kinney Executive Dean of Research at Mayo Clinic

Precure Research ultimately aims to create Mayo Clinic's largest integrated collection of biological specimens and scientific and health information, connecting what is found in a blood or tissue specimen with molecular measurements, environmental exposures, and years of clinical history. In effect, researchers want to reconstruct how a patient's biology arrived at a particular disease, allowing insights from one disease to inform the understanding, prediction, or treatment of another.

The initiative represents Mayo's next step in extending its 160-year history of seeking fundamentally better ways to care for patients. Where a longitudinal clinical record allowed physicians to see a patient's medical history across years, Precure researchers hope to observe the biological history occurring beneath it, transforming how medicine is practiced in a new era of prevention.