Science Digest: AI and ML in Early Disease Detection

Biomarkers, wearables, AI and ML are being used for early detection to make preventing disease the standard of care.

The days of waiting for symptoms to appear and align before diagnosing serious conditions are in the rearview. Team KC Laboratory Informatics Consultants Valerie Olberding and Tahrima Jubery cover the latest in how medicine is approaching disease detection.

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As consultants in the software space, we get to see various applications for AI across the biotech industry. Whether it’s AI double-checking your ELN or huge ML models predicting the next best targets in gene therapy, the AI buzz is here to stay. With AI and ML integrating into nearly every aspect of life, it’s no surprise that artificial intelligence is being used to spot the earliest signs of disease in routine tests, and early detection is becoming the norm. AI isn't just improving existing tests; it's finding entirely new ways to extract diagnostic information from data that scientists have been collecting for years. Standard medical procedures are now discovering things that traditional screening methods used to miss, transforming everyday healthcare into powerful diagnostic opportunities.

Early intervention is on track not just to become possible, but routine. For patients, this means catching diseases when they're most treatable. For healthcare systems, it represents a shift toward prevention that could transform both outcomes and economics. 

With this trend accelerating, we decided to deep-dive into the science that’s driving it. Let’s start with biomarkers, molecular clues that can reveal a disease long before symptoms appear.

Biomarkers →

Biomarkers are “measurable indicators of a biological state or condition”. In disease detection, they’re like early warning signals written in our blood, tissues, or even gut bacteria and can reveal disease in the body before any symptoms show up. Take cardiac troponin I, a protein that's released when heart muscle is damaged: a study focused on developing AI-powered paper-based sensors that can quickly and cheaply measure this critical heart attack indicator has shown great success when compared to typical hospital-based testing methods. With a small device, paper test strips, and access to a phone or laptop, this method could be used in rural areas without easy access to hospitals. Cancer detection is seeing similar breakthroughs with multi-protein approaches. Based on an ovarian cancer study, scientists using AI algorithms rapidly identified an 8-protein biomarker panel from thousands of potential candidates, achieving 97% sensitivity and 68% specificity in distinguishing between benign and malignant tumors regardless of cancer type. This process typically takes months to years of manual testing without machine learning assistance.

Another promising advancement is that the trillions of bacteria living in our digestive system, our gut microbiome, can signal cancer risk. A colorectal cancer study performed analyses on stool samples from patients. This revealed bacterial signatures that are associated with colorectal cancer, offering more precise biomarkers than previous screening methods while providing biological insights into how these microbes increase protein and mucin breakdown in ways that healthy gut bacteria don’t. These advances are moving us toward a future where a simple blood test, stool sample, or cardiac assessment could catch serious diseases at their most treatable stages.

Early Diagnostics & Detection → 

Early diagnostic testing refers to the process of identifying disease at its earliest stage, often before patients become symptomatic. By identifying diseases at earlier stages, patient outcomes are improved, healthcare costs are reduced, and more personalized treatment strategies are enabled. 

A recent study explored a creative approach: repurposing existing MRI breast imaging data to screen for thoracic aortic aneurysms, a silent but deadly condition. Using artificial neural networks (ANN), researchers analyzed over 5,000 previously collected MRI exams and found that their ANN-based pipeline improved aneurysm detection rates by 3.5 times compared to standard clinical readings. This work highlights how AI can unlock hidden insights from routine medical imaging and boost early detection without adding any extra burden for patients.

What are Artificial Neural Networks (ANN)?

According to the NIH, "Machine learning is where a machine (i.e., computer) determines for itself how input data is processed and predicts outcomes when provided with new data. An artificial neural network is a machine learning algorithm based on the concept of a human neuron."

Liquid Biopsy/Cancer →

Liquid biopsies are an early, minimally invasive breakthrough that is transforming cancer detection by finding tumor DNA floating freely in our blood. Unlike regular tissue biopsies that are invasive, liquid biopsies use a simple blood draw to detect circulating tumor DNA (ctDNA), cell-free DNA (cfDNA), and even whole circulating tumor cells, offering a repeatable approach to monitor cancer progression. By utilizing the liquid biopsy method, considerable progress has been made toward catching one of the most challenging cancers to detect early with blood tests, brain cancer. Researchers have developed machine learning algorithms that analyze genome-wide cell-free DNA fragmentomes (patterns of how tumor DNA breaks apart) to detect brain cancers from blood samples with 69% higher sensitivity compared to traditional plasma-based approaches. 

Liquid biopsies are now being used to identify mutations that guide personalized treatment strategies, assess residual disease after treatment, monitor treatment response, and also detect resistance to therapies before it becomes apparent. What makes this technology particularly powerful is its ability to provide a real-time update of a patient's cancer throughout their treatment journey, turning cancer management from periodic check-ups into continuous monitoring that could catch changes much earlier than previous methods.

Wearables →

Early medical wearables like fitness trackers and patch sensors laid the groundwork for continuous patient monitoring. When paired with AI, these devices can now detect early warning signs, such as hypoglycemic episodes, arrhythmias, or post-surgical complications before symptoms escalate. This has the ability to empower patients to take more control over their health, and clinicians will be able to manage conditions more effectively, increasing the outcomes of patients' health in a meaningful way.

A study by Poterucha et al., 2025 trained a deep learning model on over 1 million heart rhythm and imaging records to detect structural heart disease from ECGs. This model showed high accuracy across diverse populations and clinical settings; even outperforming cardiologists in controlled evaluations. While the study itself focused on ECG-based detection, it opens the door for future integration with wearable technologies. For example, researchers are exploring opportunities that incorporate smartwatches capable of taking ECGs at present plus AI and color coding to indicate if a patient is having a life-threatening condition. Colors include blue-green, signifying that nothing is wrong, yellow indicates the beginning of a problem, and orange-red represents that immediate intervention is needed. Wearable technologies like this will transform routine heart monitoring into a powerful screening tool, enabling patient autonomy through early diagnosis of heart conditions outside the clinic. 

Impact → 

When AI is combined with creative thinking and existing processes, it can redefine what’s possible in early disease detection. From biomarkers and liquid biopsies to medical imaging and wearables, AI is unlocking insights from routine data. Data that was previously overlooked, inaccessible, or intractable. These technologies are shifting the focus of medicine from reactive treatment to proactive prevention, detecting diseases at their most treatable stages and transforming the patient experience. 

In combination, these all point to a future that’s not limited by labs or specialists. Instead, diagnostics are becoming more integrated into everyday healthcare at a faster, cheaper, more accurate, and personalized rate. Though early detection methods across a range of diseases are still in development, it’s no surprise that this emerging trend will become the foundation of a more intelligent, more sustainable healthcare system.

Karchem Consulting helps biotech and life sciences teams adopt cutting-edge tools to accelerate advancements like these. To learn more about how we bridge the gap between innovative technologies and practical implementation get in touch with us!