AI is Impacting the Way We Track
How we measure, diagnose, monitor
This is the 4th of 7 separate pieces I am going to be writing over the next few weeks, culminating in a full market map of the consumer wellness space. I will be focusing on how AI is impacting each segment, as well as the companies that are poised to win in the future. The final piece will be a culmination of the entire journey, with thoughts on where the industry is going as we combine each group.
For the full article, including the list of companies, check them out here
Why you should Care
We’re standing at the threshold of the bionic era — where hardware and software seamlessly weave into daily life. We’re not quite at screwdriver-for-a-finger Cyberpunk territory, but wearables, at-home diagnostics, and tracking software are rapidly becoming part of how we manage our health.
Tracking health metrics isn’t new. Elite athletes, soldiers, astronauts, F1 drivers… anyone where a 1% difference in a critical metric, have done it for decades. What’s new is that it’s mainstream. Twenty years ago, VO₂ max and HRV were niche stats; today, my dad tracks them (he’s not an F1 driver, but he’s just as cool, trust me). The shift happened thanks to better tech, more accessible health information, and a culturally seismic pandemic.
COVID-19 pushed consumers back into the driver’s seat. With rising distrust in the medical system, people turned to the internet and influencers for answers. At-home COVID tests normalized medicine from the living room. Micro-influencers turned data into content, fueling curiosity (and sometimes fear). Technology democratized access to metrics once reserved for pros. Then in 2022, ChatGPT opened an entirely new layer of personalized interpretation.
This convergence of catalyst + tech + culture has created the era of the empowered health consumer. Forty-four percent of Americans now own a health wearable, and ~35–40% use health apps. The focus is shifting from reactive sick care to proactive self-care. The medical system has largely conquered acute infectious diseases, but the “four horsemen” of chronic illness — heart disease, cancer, metabolic dysfunction, and neurodegeneration — remain. People see the fragility of aging parents and ask: How do I avoid that?
This section is about answering that question. Using tracking to build a baseline, enabling a more preventative, data-driven approach to health. What gets measured gets managed… but only if we manage what matters.
There are downsides and caveats to this notion. Tests are not always 100% accurate. Even if they are, sometimes there cannot be anything done about the information. A test can say someone is 16% more likely to develop Alzheimers. Yes, they can take action today to get more sleep and improve cognitive function, but will the fear of the potential of a problem do more harm than having the information?
A great example is when Dr. Matt Kaeberlein took 8 different epigentic (biological age) tests (4 tests, 2 times each) at the exact same time. You can see the results below.
As we enter this data-driven era, the expertise of our doctors and medical system is more important now than ever. It is amazing that people are collecting their own health data. However, it’s critically important that they work with an expert to understand the information and create the right plan to maximize their healthspan.
The Major Categories to watch
Wearables
Once limited to counting steps, wearables have evolved into multi-sensor health hubs that continuously track metrics like heart rate, HRV, SpO₂, temperature, sleep quality, and activity strain.
General health wearables: Smartwatches (Apple, Garmin) and fitness bands (WHOOP, Fitbit, Polar) are pushing beyond fitness into recovery, sleep optimization, and even early cardiac risk detection.
Metabolic wearables: Continuous glucose monitors and glucose-adjacent biosensors (Dexcom Stelo, Abbott Lingo) give real-time feedback on metabolic health, enabling personalized nutrition and training.
Clinically prescribed wearables: Medical-grade sensors such as cardiac patches, insertable monitors, and biosensors (Medtronic LINQ, VitalConnect, Philips ePatch) are bridging consumer-friendly tracking with regulated clinical care.
As sensors get smaller, cheaper, and more accurate, wearables will shift from fitness accessories to core health infrastructure—providing the data backbone for both personal wellness and preventative medicine.
Non-Wearable Devices
While wearables dominate the conversation, non-wearable devices quietly fill critical gaps — delivering high-accuracy, periodic measurements without requiring 24/7 use. From connected blood pressure monitors (Hello Heart, iHealth) to portable ECGs (AliveCor, Qardio) and smart scales (Withings) that track body composition over time, these tools are turning kitchens, bedrooms, and bathrooms into health checkpoints. Breath analyzers like FoodMarble bring lab-style testing for digestion into the home, enabling people to pinpoint dietary triggers without invasive procedures. As these devices integrate with AI, they’ll shift from isolated readings to active early-warning systems, helping consumers spot deviations from baseline and prompting action before problems escalate.
Diagnostics
Diagnostics bring lab-quality testing into the hands of consumers, providing point-in-time measurements that were once locked behind clinical visits. These tools help establish baselines, uncover hidden issues, and guide proactive health decisions across multiple dimensions.
Genetic & Epigenetic: DNA and biological-age testing from companies like 23andMe, Nebula Genomics, Veritas, and Nucleus reveal inherited traits, disease predispositions, and how lifestyle factors are influencing gene expression over time
Microbiome & Gut: Tests from Ombre, Evvy, Alba Health, and myBioma analyze gut composition to guide dietary and probiotic interventions
Hormone Health: Platforms like Oova, Inne, and Proov track fertility, stress, and other hormone-driven changes with at-home sampling
Blood Biomarkers: Companies such as Rythm and SiPhox provide single or multi-panel blood work for metabolic health, inflammation, nutrient status, and more
Comprehensive Multi-System Panels: Services like Function Health, Everlywell, LetsGetChecked, and Superpower offer broad-spectrum testing, sometimes integrating with large lab networks like Quest Diagnostics and Labcorp for expanded access.
Collection methods range from full at-home kits to hybrid models where consumers visit a partner facility. As AI integrates with these platforms, diagnostics will shift from isolated snapshots to dynamic health timelines, where repeated testing tracks progress, flags anomalies early, and delivers personalized recommendations.
Software
These platforms lead with software, integrating with wearables, diagnostics, and smartphones to interpret health data and deliver personalized guidance. Elite HRV, Welltory, and Cardiogram specialize in interpreting biometric signals like heart-rate variability and cardiovascular metrics to guide training, recovery, and risk detection. Nutrisense and Signos pair continuous glucose monitoring with AI-driven coaching to optimize metabolic health. Hormona focuses on hormone tracking and education for women’s health, while Humanity uses real-world data to measure and slow biological aging.
Visual and computer vision technologies are also emerging, with 3DLook and Zozofit creating detailed body composition and measurement scans from smartphone cameras, and Vessel enabling at-home wellness testing with instant app-based interpretation. Electrokare applies AI to ECG data for early cardiac event detection.
These companies are positioned to become the interpretation layer in health tracking—translating complex datasets into clear, goal-oriented action without the burden of manufacturing hardware. Their agility in iterating features and integrating across multiple data sources gives them a strong edge as AI reshapes the TRACK landscape.
Facilities
While most tracking happens through personal devices, facilities bring in-person capabilities that can’t yet be replicated at home. These range from advanced diagnostic centers to wellness clinics equipped with full-body scanners, DEXA machines, metabolic carts, and VO₂ max testing. Companies like Prenuvo, Ezra and BodySpec offer comprehensive assessments that combine imaging, lab work, and physical evaluations into a single visit.
Some facilities integrate with at-home and wearable data, creating a closed feedback loop: diagnostics done on-site feed into continuous monitoring, while real-time tracking from the consumer informs what’s tested during visits. As more of these centers adopt AI and interoperability standards like FHIR, expect them to act as hybrid nodes — bridging the gap between consumer-led tracking and clinical care, and making advanced testing accessible to a wider audience.
How AI is Reshaping Track
AI models are only as good as the data that they’re fed. That’s what makes this TRACK movement so exciting. Consumers are generating richer datasets than ever: continuous biometrics, microbiome sequencing, multi-omic panels, full-body imaging, and more. AI is able to act as the connecting neural network on top of all this raw data: finding patterns, interpreting results, and turning raw measurements into precise, personalized action.
Predictive & Preventative Intelligence
AI will play a role in anticipating health issues before one would think to go to a doctor. Multi-omic testing provides a baseline of ones health, and wearables provide pattern recognition for early detection of issues. What does this mean in practice:
Diagnostic baseline: A multi-omic blood panel (like from Function Health or Superpower) flags elevated fasting glucose and slightly elevated HbA1c, indicating early insulin resistance, well before it meets the criteria for Type 2 diabetes.
Wearable monitoring: After the baseline, a continuous glucose monitor (Dexcom, Abbott Lingo, Levels) is worn for several weeks. The CGM detects post-meal glucose spikes — for example, a 50 mg/dL rise after eating certain carbs — that confirm the trend toward poor glycemic control.
Pattern recognition and intervention: AI models correlate CGM data with sleep quality, activity levels, and HRV from a wearable like Whoop or Oura. The pattern shows that poor sleep and late-night eating dramatically worsen glucose responses. The system flags this to the user
Consumers will be able to tap into their genetics, proteomics, and microbiome to get a sense of “where do I focus”. Continuous monitors help track changes from time A to time B. AI helps understand and interpret the information, notifying when it’s time to consult a specialist.
Who’s positioned to win in this sector: the multi-modal wearables that currently have high-frequency data streams. More data + more history ⇒ better models ⇒ better direction and guidance ⇒ higher switching costs.
Personalized Real Time Guidance and Behavior Change
AI will play a role as translator of raw data into individualized, real-time action. This builds upon the above, where AI will be used to help develop plans based on the users information. We could see improvements in adaptive coaching based on CGM, HRV, sleep, and recovery scores. Nutrition, exercise, and recovery recommendations that update dynamically. In a world where AI knows your baseline, your goal, your timeline, and your restrictions, it can help take the role of 24/7 coach.
Notice a poor night’s sleep? Your training plan adjusts to focus on mobility and light recovery work.
See repeated glucose spikes? Your meal plan changes instantly, removing problematic foods.
Detect elevated stress over several days? The system schedules a mindfulness session or prompts you to connect with a friend.
The value isn’t just data + insights. It’s data + insights + actionable plans. The next wave of winners will be companies that
Tie insights directly to user-defined outcomes (e.g., performance gains, weight loss, disease risk reduction)
Minimize the gap between insight and execution (e.g., in-app habit nudges, direct integration with meal delivery or training apps)
While many players offer AI coaches, most still fall short of delivering truly personalized, goal-oriented guidance. The company that nails actionable support that is grounded in data will own the behavior-change category.
Accessible Health Monitoring
AI is making advanced health tracking cheap, non-invasive, and available anywhere. New modalities are emerging with the promise of high accuracy and easy adoption. Examples include smart home sensors (mirrors, scales), computer vision, & LLM applications. Companies like 3DLook promise accurate 3D body maps from smartphone cameras, predicting weight with ~3.5 % error and achieving 96–97 % measurement accuracy. Withings offers in-home body scanners that help people better understand their physiological body composition without having to go to the doctors office. The home is becoming smarter with passive sensing from everyday devices, leading to a world where consumers may be notified of a potential skin-issue from their bathroom weeks before they would have checked it out with a specialist.
Larger companies with extensive hardware, economies of scale, and deep financial pockets are positioned to take advantage here. Yes, there will be disruptive startups, but the successful ones will likely be acquired by larger incumbents. The Google’s, Apple’s and Withing’s of the world are in a strong position moving forward.
Predictive Simulation & “What-If” Modeling
There’s this running idea of a digital health twin, a digital representation of oneself that can be used in simulations to predict the possible outcomes of lifestyle changes or medical treatments. The companies in TRACK provide the data layer necessary to create the digital twin. AI can then be used to simulate potential outcomes from different behaviors in real time. For example, let’s say someone has been feeling exhausted and groggy in mid-morning. They could consult AI and run their tracked data through a predictive model, getting the output: “If you increase daily steps by 2,000 and shift dinner earlier by 1 hour, your glucose variability is projected to drop 12% over the next month.” This turns tracking into a decision engine that is tailored to each individual.
What’s the future:
We’re moving towards a more dynamic, actionable, and personalized approach to health management. In this world, we’ll see wearables move into mainstream care, the rise of a central health OS, and an expansion of the metric universe.
Wearables in Mainstream Care
Several companies have started to cross the blurred lines of clinical vs wellness. As FDA approvals expand and electronic health record (EHR) systems adopt new interoperability standards (FHIR), wearables will become part of standard intake data. Today, if your Apple Watch ECG detects something unusual, FHIR lets that data be sent from Apple Health to your doctor’s EHR in a standardized way so their system immediately knows it’s an ECG result, who it belongs to, when it was recorded, and how to interpret it. FDA has started to approve and accept some wearables as medical grade such as:
Apple Watch's ECG
Dexcom Stelo
Abbott Lingo
Within the next few years, it may be routine for clinicians to incorporate more of consumer’s wearable data into consideration before prescribing treatment. We’ll look into the idea of augmented doctors more in HEAL, but the idea would be that the doctor is partnering with AI and accessing all of the patient’s information to truly build a comprehensive view of the patients health. One barrier to overcome is that physician training and workflow integration is still lagging, as many doctors aren’t trained on how to interpret or act on continuous data. However, this is starting to quickly change (see Alice L. Walton School of Medicine).
The Rise of the Health OS
The holy grail in the health data sector: a single source of truth containing all available health information about the patient. Today, we live in a siloed, fragmented, and territorial world. Companies are resistant to share their data knowing it’s the most valuable competitive moat they have. As we move forward, I would expect that more of these companies create closed ecosystems where they’re managing more aspects of a patients health journey, or, we see the rise of a single health operating system (maybe from a player like Apple). In the second scenario, there may be more than one company that provides a central health OS.
We will move from singular, separate devices towards platforms. Multi-modal wearables, at-home diagnostics, and environmental sensors will consolidate into longitudinal health platforms. They will provide a single interface for all of one’s data. In medicine 2.0, healthcare was annual, non-routine, and passive. Data was kept and stored at the doctors office. Medicine 3.0 is active, predictive, and continuous. The data will be stored in our pockets.
Why is this so important? From a business perspective, whoever wins the “heath OS” race will have a dominant effect in shaping the future of proactive care. From a health perspective, the melding pot of biometric, EHR, diagnostic information + AI will exponentially increase our ability to detect health issues and prescribe precise wellness programs.
Expanding the Metric Universe
Tracking capabilities have already come so far, but expect them to follow Moore’s law and continue to get smaller, cheaper, less invasive, and more desirable. What used to require a full lab visit will happen from a sensor the size of a band-aid.
In the near term, we’ll see breakthroughs in non-invasive glucose monitoring (Apple, Samsung, and Dexcom are already in development), sweat chemistry sensors that detect electrolyte balance, dehydration, or nutrient deficiencies, and hydration trackers embedded into wearables. Continuous hormone panels could become a reality — tracking estrogen, testosterone, cortisol, and thyroid levels from microfluidic patches — unlocking entirely new layers of insight for women’s health, athletic performance, and stress management.
Over time, applying a sensor could be as habitual as putting on your socks in the morning. For endurance athletes, it might mean a patch for hydration and lactate monitoring before training. For someone with metabolic risk, it could be a glucose sensor they barely notice. For women tracking fertility, a tiny wearable could monitor temperature, hormone shifts, and cycle changes continuously.
The companies that win here won’t just measure more things, they’ll measure the right things, interpret them in context, and make acting on them effortless. In the same way the step counter became a cultural mainstay, tomorrow’s winners will turn glucose stability, muscle recovery, or stress resilience into metrics everyone understands and strives to improve.
We Have to be Careful
As I mentioned above, tracking is a double edged sword. We risk information overload and stress-induced obsession of metrics. We risk trusting devices more than our intuition (I feel great but my Whoop says low recovery). We risk missing out on life because we’re living in fear of dying. The ability to track and manage our health is amazing - we’re at a point in our scientific and medical journey where we have the tools to measure and showcase improvements in our healthspan based on our actions. Nonetheless, it’s critical to implement personal and societal guardrails for trust. Now more than ever we must astutely wade between the signal and the noise. Platforms that curate insights and suppress irrelevant alerts will win consumer trust. And if there’s anything worth tracking, it is the pillars and foundation that lead to a healthy, long life filled with joy.