Future of Healthcare
August 10, 2026
This is the second article in a two-part series on AI in healthcare. Read Part 1 to learn about how AI is used at Biograph.
The AI tools running in clinical practice today are primarily diagnostic: they identify what is there, right now. What comes next is predictive: AI that models your physiological trajectory years in advance, so a physician can intervene before disease has a chance to establish itself.
That shift may be as significant as the move from paper maps to GPS.
A map tells you where you are. It can show the roads around you and help you make sense of the terrain. But it is static. It cannot tell you that traffic is building ahead, that a faster route just opened, or that if you keep going at your current speed, you will miss the exit.
GPS changed navigation because it became dynamic. It continuously integrates location, speed, traffic, road conditions, and destination, then updates the route in real time.
Medicine is beginning to undergo a similar transformation. For most of modern healthcare, we have relied on snapshots: an annual physical, a lab panel, a scan, a symptom, a threshold. These are useful, but they are closer to maps than GPS. They show us where we are at a point in time.
The future of medicine is a continuously updating view of where your health is heading.
From annual snapshots to continuous insights
Standard executive health programs work in snapshots: periodic assessments that capture your health at a single point in time, then wait for something to cross a clinical threshold before acting.
The next generation of clinical AI will change this. Models will integrate polygenic risk scores, whole-body imaging, routine biomarkers, and continuous wearable telemetry such as glucose trends, sleep architecture, activity, recovery, and heart rate variability, tracking health trajectories closer to real time.
“A once-a-year A1c is useful, but it’s still a snapshot. Wearables and continuous metrics give us a better view of someone’s health trajectory in real time. The opportunity is to turn those patterns into personalized recommendations, so people can see how their daily behaviors are moving them in the right direction. It’s the difference between checking occasional highway markers and having GPS.”
The goal is not more data for its own sake. It is closing the loop between intervention and response: knowing faster whether what you are doing is working, and adjusting accordingly.
This is where AI becomes especially powerful. A physician can interpret a lab panel. A wearable can track sleep. An imaging study can reveal early structural changes. A genetic test can show inherited risk. But the most important signal often emerges between these data sources, across time.
Is sleep disruption preceding glucose instability? Is visceral fat changing before standard metabolic markers cross a threshold? Is cardiovascular risk understated by a normal cholesterol panel? Is an inflammatory pattern transient, or part of a longer trend? Is an intervention actually changing the trajectory, or only improving one isolated marker?
AI can help detect those patterns earlier and more consistently. But the value is not simply in finding more abnormalities. It is in helping physicians and patients understand which signals matter, which are noise, and what to do next.
From medicine as repair to medicine as navigation
Much of modern medicine is built around repair. Something breaks, symptoms appear, a diagnosis is made, and treatment begins.
That model has saved countless lives. It remains essential. But it is not enough for the future of prevention and longevity.
The next era of medicine will focus increasingly on navigation: helping people understand where their health is headed and how to adjust course before disease becomes inevitable.
This is the difference between asking, “Are you sick?” and asking, “What direction is your biology moving?”
A person may be technically healthy by conventional thresholds while their long-term trajectory is already shifting. Their blood sugar may be normal but trending upward. Their muscle mass may still be adequate but declining. Their cardiovascular risk may be underestimated by standard labs. Their sleep may be quietly affecting mood, metabolism, and recovery. Their organ health may be changing years before symptoms emerge.
In a GPS model of medicine, the physician is not waiting for the crash. They are helping the patient see the road ahead.
That does not mean predicting the future with certainty. Medicine will always involve uncertainty, probability, and judgment. But it does mean replacing isolated snapshots with a more dynamic understanding of risk, resilience, and direction.
Measuring how individual organs age
One of the more significant near-term advances will be the clinical use of organ-specific aging clocks and proteomics, the study of protein expression circulating in the blood.
Consumer “biological age” tests today are mostly based on simple epigenetic markers. What is emerging is more precise: machine learning models trained on thousands of circulating blood proteins that can forecast the disease trajectory of individual organs, including the heart, liver, and brain, years before symptoms appear.
“Genomics gives us the blueprint, but proteomics may help us understand what the body is actually doing. We already track glucose, sleep, and activity; the next frontier may be tracking molecular signals that reflect inflammation, metabolism, aging, and disease risk in a more dynamic and personalized way. That’s pretty exciting.”
For clinical purposes, this means moving from “your lipids look fine” to a much more specific picture of what a particular organ is doing and where it is trending.
This matters because aging is not uniform. The brain, heart, liver, immune system, and musculoskeletal system do not necessarily age at the same pace. Two people with the same chronological age may have very different patterns of risk and resilience.
AI can help physicians make sense of this complexity. Instead of treating aging as one broad number, future systems may help identify where reserve is being lost first: cardiovascular reserve, metabolic reserve, cognitive reserve, muscular reserve, or immune resilience.
That shift could make prevention much more targeted. The question becomes less “How old are you biologically?” and more “Which systems need attention now, and what would most likely change their trajectory?”
This systems-based approach is precisely what Biograph and Vero aim to evaluate through their ongoing clinical study, exploring whether organ-specific proteomics can help physicians identify risk earlier and measure how targeted interventions influence organ health over time.
From trial and error to personalized interventions
Optimization and supplementation today often involve a lot of trial and error: protocols built on population averages, adjusted slowly over time based on annual labs. As AI becomes better at recognizing patterns across large biomarker datasets, clinical recommendations will become more precise.
The shift is from broad population averages to individualized targeting. Dr. Fu summarizes it:
"AI’s strength is synthesis -— taking complex, high-dimensional information and identifying patterns that humans may miss. Right now, health optimization can feel like trial and error: try a hundred things and see what sticks. In the future, AI may help narrow that down to the few interventions most likely to make a meaningful difference for your biology and your goals."
This may be one of the most important changes AI brings to preventive medicine: prioritization.
Most people do not need more generic advice. They already know that sleep, exercise, nutrition, stress, alcohol, and medications matter. The harder question is what matters most for them, right now.
For one person, the highest-leverage intervention may be lowering ApoB. For another, it may be improving sleep regularity. For another, building muscle. For another, addressing insulin resistance, alcohol intake, visceral fat, blood pressure, inflammation, or medication adherence.
AI will help identify the likely bottleneck. It can synthesize a patient’s labs, imaging, wearable data, family history, goals, and prior response to interventions into a more complete picture of what may move the needle.
But the future of personalized medicine is not a hundred recommendations. It is the right recommendations, chosen for the right person, at the right time.
That is where the physician’s role changes. In an AI-powered future, the physician spends less time assembling the map and more time helping the patient choose the route. The work shifts from gathering information to interpreting tradeoffs, setting priorities, and translating complex risk into a plan the patient can understand and actually follow.
A recommendation that improves a biomarker but harms quality of life may not be the right recommendation. A risk that matters statistically may not be the risk that matters most to this individual. The best care will combine AI’s ability to synthesize complexity with the clinical judgment and human context needed to turn that synthesis into action.
Biograph’s role in the future of medicine
At Biograph, we think the future of longevity medicine is not simply more data, and not simply AI layered on top of existing care. It is a new clinical model that brings advanced diagnostics, longitudinal tracking, AI synthesis, and expert clinical interpretation together.
A whole-body MRI, deep bloodwork, genetics, wearables, fitness testing, family history, and clinical notes are each useful on their own. But they become far more powerful when interpreted together, over time, by AI systems that can surface patterns and clinical teams who can turn those patterns into action.
Biograph’s role is to help build that infrastructure for proactive medicine: a system that helps patients understand where their health is heading, what matters most, and how to change course. In the best version of the future, AI handles more of the complexity. Physicians and clinical experts provide guidance, accountability, and context. Patients gain agency.
That is the future we’re building towards here at Biograph and the promise of AI in medicine: leveraging data, advanced technology and expert human judgement to make care more informed, more proactive, and more precise.
Commonly asked questions about AI in healthcare
Will AI replace doctors?
AI can help analyze large amounts of health data, identify patterns, and support decision-making, but physicians remain responsible for clinical judgment, patient communication, and personalized care.
How could AI improve preventive healthcare?
AI may help physicians detect subtle patterns across multiple health datasets, allowing risk factors to be identified earlier and interventions to be tailored more precisely to the individual.
What is proteomics?
Proteomics is the study of proteins produced and circulating within the body. Researchers are exploring how protein patterns may provide insight into disease risk, organ-specific aging, and organ health.
Will wearable devices play a larger role in healthcare?
Many experts believe wearable devices will become increasingly important as continuous sources of health data, helping physicians monitor trends in sleep, activity, glucose regulation, heart rate, and recovery over time.
Can AI predict disease before symptoms appear?
Emerging AI models are being developed to identify patterns associated with future disease risk before symptoms develop. While many of these technologies are still evolving, the goal is to enable earlier and more personalized intervention.
Jack Larch is Biograph’s Lead AI Engineer, leading the development of AI systems and internal tools that help both clinicians and internal teams work more efficiently, synthesize complex health data, and deliver more personalized care.
