How AI Is Used in Preventive Health

Diabetic retinopathy, damage to the retina caused by diabetes, is the most mature example of AI in preventive screening. People with diabetes are advised to have regular eye exams, but many do not, and specialists are in short supply.

The American Academy of Ophthalmology's EyeNet magazine, in a February 2026 feature, noted that three autonomous AI systems had been approved by the FDA for detecting diabetic retinopathy. It also explains a distinction that matters for every AI health tool. Autonomous systems give a diagnostic result without a human making the decision. AI-assisted systems only analyze data and offer suggestions to a clinician who decides.

Accuracy data comes from trials such as a prospective multicenter study that compared one AI system with general ophthalmologists and retina specialists using dilated eye exams. Against the clinical reference standard, the AI system had higher sensitivity for detecting more than mild diabetic retinopathy than either group of doctors, while the doctors had higher specificity. In practice, the AI missed fewer cases but flagged more people who turned out not to need referral.

Performance in routine care also matters. A study at a hospital endocrinology clinic in Belgium found that an AI screening system achieved high sensitivity and specificity for referable diabetic retinopathy compared with a retinal specialist, with consistent accuracy across demographic and clinical subgroups.

The most important evidence for prevention is not accuracy but uptake. Retinal Physician reports that a randomized trial of point-of-care AI screening in young people with diabetes achieved 100% completion of eye exams, compared with 22% for standard referral to an eye care provider. Screening that happens during a regular clinic visit removes a separate appointment, which is where many people drop out.

Researchers also test whole workflows rather than algorithms alone. A trial protocol in JAMA Network Open describes a multicomponent approach combining AI-powered screening, real-time integration of results with electronic health records, and patient education in federally qualified health centers, with the goal of improving adherence to annual retinal screening.

Risk prediction uses data a practice already holds, such as age, history, lab results and prior tests, to identify who would benefit most from screening or early intervention.

The primary care scoping review found that randomized trials showed improvement in risk-appropriate screening and clinical follow-up when AI-supported preventive care was compared with standard care. Observational and validation studies consistently reported that AI models outperformed conventional approaches in identifying high-risk individuals for conditions such as diabetic retinopathy, atrial fibrillation, diabetes and multiple cancers. Almost half of the included studies concerned cancer prevention.

The same review is careful about the gap between promise and practice. It concludes that adoption requires strategies to overcome technical, ethical and organizational barriers, and that future research should address real-world implementation, cost-effectiveness and clinician engagement.

The practical value of risk prediction depends on what happens next. A model that identifies a high-risk patient helps only if the practice has time and a process to contact that patient, offer the right test and track the result.

Continuous monitoring is the most visible consumer face of AI in health. A review of AI in predictive medicine describes how wearable devices and mobile health apps track vital signs and health metrics, with AI algorithms analyzing the data to forecast potential problems and alert providers or patients. It highlights chronic conditions such as diabetes, hypertension and chronic obstructive pulmonary disease, where ongoing management prevents complications.

Some proposals go further. A narrative review in JMA Journal describes a conceptual AI-driven ecosystem that integrates personal health data, sensor inputs and self-reported needs to give tailored guidance and emergency alerts, particularly for older adults living alone. The authors present it as a framework, not as an evaluated system.

That distinction is worth keeping in mind. A consumer wearable that tracks heart rate or sleep is not the same as an FDA-authorized device intended to detect a specific condition. The evidence and regulatory review behind each can differ greatly.

In the United States, the FDA states that it does not regulate AI as such; it regulates medical devices, including AI-enabled medical devices. A device is defined by its intended use, including the diagnosis, mitigation, treatment or prevention of disease, and software can be a device. AI-enabled devices go through the usual premarket pathways, such as 510(k) clearance, De Novo classification or premarket approval. The same FDA page, read on 2026-10-17, states that the agency had authorized over 1,600 AI-enabled medical devices for marketing as of September 2026.

The FDA keeps a public list. It explains that devices on the list have met applicable premarket requirements, including a focused review of safety and effectiveness, and that the list is not comprehensive because it is built by searching for AI-related terms in authorization documents.

What kind of AI is in these devices? A review of 1,016 FDA authorizations in npj Digital Medicine found that quantitative image analysis remains the most common application, though its share has declined, and that over 100 devices use AI to generate data.

Not every health algorithm is a regulated device. The Pew Charitable Trusts explains that the 21st Century Cures Act excluded some software from the device definition, including certain clinical decision support software that informs a clinician's decision but is not intended to replace the clinician's independent judgment. Pew notes that the line between informing and driving a decision has been hard to draw. The same law also excludes software intended for maintaining or encouraging a healthy lifestyle and unrelated to the diagnosis or prevention of disease, such as apps that monitor exercise or encourage healthy eating. Many consumer wellness apps sit in this category.

Pew also describes a technical difference that affects safety over time. A "locked" algorithm gives the same output for the same input until the developer updates it, but it can degrade as populations and practices change. An "adaptive" algorithm can update itself with new data, which may improve accuracy but also means the same input can produce different results over time.

The sources agree on a short list of risks.

• Bias in training data. Pew notes that AI products have sometimes produced inaccurate recommendations because of bias in training data, and gives the example of cardiovascular risk algorithms trained mostly on White patients that over- and underestimated risk in other groups.
• A new setting. An algorithm built at a large academic center may not perform the same way at a small rural clinic unless it is adapted and validated there, according to Pew.
• Privacy. Large datasets raise questions about consent and data ownership. The predictive medicine review stresses compliance with data protection laws such as the GDPR.
• Screening without treatment capacity. Retinal Physician points to long-standing screening principles that facilities to diagnose and treat detected disease should be in place before screening is implemented. Finding sight-threatening disease without a working referral path does not help the patient.
• Images the system cannot read. In the multicenter retinopathy trial, some eyes were ungradable by the AI system, which means a fallback process is always needed.

Whether you are a clinic evaluating a product, an employer considering a wellness benefit or an individual looking at an app, these questions separate tested tools from marketing.

The same discipline applies to the general-purpose AI agents and assistants that many people use to research health questions. They can help you prepare questions for a clinician, but they are not authorized medical devices and should not replace one.