For years, preventive medicine has centered on screenings, routine checkups, and occasional lifestyle interventions. Now, artificial intelligence is transforming that approach. It offers the potential to shift from reactive care to truly proactive, preventive health care. With machine learning models capable of analyzing vast datasets, from electronic health records and genomics to neighborhood-level social determinants of health, AI is enabling us to predict disease before symptoms appear. This paves the way for personalized prevention. While our stethoscopes and standard diagnostic tools are still valuable, the algorithm is quickly becoming more efficient, precise, and insightful.

Moving beyond risk scores

Traditional risk calculators like ASCVD or CHA₂DS₂-VASc rely on broad population data and only a limited set of variables. In contrast, AI models can integrate hundreds or even thousands of features, including lab trends, medication patterns, lifestyle metrics, and even subtle indicators like voice tone or facial expressions captured during telehealth visits.

Imagine identifying a patient’s likelihood of developing depression not just by their PHQ-9 score but by subtle changes in their text messages or sleep data. Picture being able to predict the onset of Type 2 diabetes by analyzing insulin resistance markers, family history, and socioeconomic stressors long before the first elevated A1c.

This is no longer theoretical. AI tools are being deployed in research and clinical settings to predict everything from heart failure and early-stage cancer to kidney disease and opioid misuse risk.

From one-size-fits-all to precision medicine

Preventive care has often been generic. AI enables precise prevention that is tailored to an individual’s biology, behavior, and environment. Consider a 55-year-old man with normal BMI and cholesterol. Traditional guidelines might not flag him. However, an AI model trained on millions of records might detect early cardiac risk due to a hidden pattern of elevated nighttime heart rate, disrupted sleep, and family history. This is in a patient with a perfect report on his basic annual physical. That is the power of prediction: being able to detect the signs before the condition hits.

What this means for clinicians

Some fear AI will replace us. In preventive and precision medicine, it will do something far more valuable. It will empower us.

AI does not replace clinical judgment; it enhances it. It detects risk earlier. It helps us prioritize already limited time and resources. It nudges us when a patient is due for screening or when patterns in data suggest something we might miss. It could also caution us about a patient who is at high risk for thirty-day readmission after discharge. More importantly, it helps us fulfill the true focus of public health. That is, to keep the population healthy.

Avoiding bias and ensuring optimum performance

AI is only as good as the data it is trained on. If we do not include diverse populations in our datasets, we risk creating tools that either miss or misjudge patients from marginalized groups.

Preventive medicine must also prevent injustice. That means clinicians should participate in developing, auditing, and deploying these tools to ensure transparency, fairness, and real-world relevance. As physicians, we can guide how these tools are used, ensure that they are equitable, and demand that they serve, not exploit, our patients.

What is next

Prediction is only the beginning. The future of preventive medicine should be predictive, actionable, and personalized.

That means coupling AI risk scores with behavioral changes, culturally tailored care, and community-based interventions. It means integrating AI with the human touch, not replacing it. This human-centered approach is often referred to as augmented intelligence. I believe we are well on our way from “sick” care to “health” care.

Jalene Jacob is a physician-entrepreneur.