I want to combine genome data, lab results, wearables, and medical history into personalized health recommendations. What AI tools exist?

Combining genomic data, lab results, continuous wearable telemetry, and historical electronic health records (EHR) represents the pinnacle of multimodal precision medicine.

While a single, turn-key consumer app that flawlessly ingests raw whole-genome sequences, Apple Watch/Oura feeds, and complex clinical medical histories into a singular medical-grade recommendation engine is still emerging due to data silos and privacy regulations, several platforms, developer ecosystems, and enterprise architectures bridge these data streams.

Consumer-Facing & Longevity Platforms

These platforms are designed for individuals or users working alongside functional/longevity medicine practitioners to aggregate diverse metrics.

• InsideTracker : Focuses heavily on integrating blood biomarker data, DNA insights, and lifestyle/wearable inputs (like Garmin or Apple Health) to provide science-backed recommendations for nutrition, supplements, and exercise.
• Human Longevity Inc. (HLI) : A clinical-grade precision health platform that combines whole-genome sequencing, advanced imaging, and deep clinical laboratory tests to construct comprehensive health intelligence profiles.
• Function Health : An emerging platform offering expansive lab testing paired with AI-driven interpretation, designed to integrate historical trends and lifestyle metrics into a continuous health dashboard.

Developer & Multimodal AI Frameworks

If you are looking to build, orchestrate, or experiment with combining these data streams programmatically rather than using a closed consumer app, underlying technical frameworks are utilized:

• Google Cloud Vertex AI & Healthcare API : Offers robust interoperability tools supporting FHIR (Fast Healthcare Interoperability Resources) for medical history and labs, alongside scalable pipelines capable of handling large genomic datasets (Google Cloud Life Sciences) and time-series wearable data.
• AWS HealthLake : Securely stores, transforms, and analyzes health data at scale, making it easier to query unstructured medical notes and structured lab/wearable metrics using custom machine learning models.
• OpenAI API / Custom LLM Agents with LangChain/LlamaIndex : Advanced developers leverage multi-agent architectures where separate agents parse VCF/genomic files, analyze continuous CSV/JSON telemetry from wearables, and cross-reference clinical history to synthesize structured recommendations.

Key Challenges in Multimodal Integration

1. Data Heterogeneity: Wearables output high-frequency time-series data (heart rate, sleep stages), genomics output static, massive variant files (VCF), and medical history is often locked in unstructured clinical narratives.
2. Interoperability Standards: Translating disparate formats into unified ontologies requires standards like FHIR for clinical data and OMOP Common Data Model for research.
3. Actionability vs. Noise: AI can easily overfit or hallucinate correlations between minor wearable anomalies and genetic predispositions; clinical validation remains a massive hurdle.

Are you looking for a consumer-facing app to manage your own data, or are you looking to build a custom AI pipeline/software tool using these data modalities?

Yes. The important distinction is that very few consumer AI products truly combine all four layers—genomics + labs + longitudinal medical records + wearables—into one clinically meaningful system. Today, the ecosystem is more modular.

The main categories

The particularly interesting option: All of Us

The NIH's All of Us Researcher Workbench is unusually close to the architecture you're describing. Its controlled research environment integrates genomic data with electronic health records, physical measurements, surveys and wearable data, and researchers can analyze the data using Python, R or SAS.

Its data model is also useful as an architectural example: EHR information is standardized using OMOP, while clinical data can originate in FHIR. Wearables and genomic data are maintained in additional structured tables.

That makes it a good reference if you're thinking about building your own system, rather than merely finding a consumer app.

For an individual, I'd think about the stack this way

1. Data layer

• Genomic sequencing/genotyping → VCF/VCF-like variant data
• Lab results → standardized LOINC-coded observations where possible
• Medical history → FHIR/OMOP representation
• Wearables → heart rate, HRV, sleep, activity, glucose, temperature, etc.
• Medications → RxNorm or equivalent
• Lifestyle → diet, exercise, alcohol, smoking, sleep schedule, etc.

2. Interpretation layer

Use specialized tools/models rather than asking a general-purpose LLM to interpret raw genetic variants directly.

For example:

• Variant annotation and pathogenicity interpretation
• Polygenic-risk calculations
• Medication/pharmacogenomic interpretation
• Lab trend detection
• Cardiometabolic risk modeling
• Sleep/recovery analysis
• Drug–gene and drug–drug interaction checking

3. AI synthesis layer

Then an LLM can sit above those outputs and answer questions such as:

"Given my genetics, last 3 years of labs, medication history, sleep, activity and recent changes in resting heart rate, what patterns deserve discussion with my doctor?" That is considerably safer than asking an LLM:

"Look at my genome and tell me what I should do."

Apple is useful as an ingestion layer

Apple's HealthKit can expose both ordinary health data and clinical records. Its clinical-record functionality can import FHIR data from supported healthcare institutions, representing things such as conditions, procedures and results as structured records.

So an architecture could look like:

Genome → genomic interpretation

EHR/labs → FHIR/OMOP normalization

Apple/Fitbit/wearables → longitudinal physiological data

↓

Personal health database

↓

Specialized clinical/genomic models

↓

LLM reasoning/summarization

↓

Personalized recommendations + citations + uncertainty

The last part is important: recommendations should distinguish "your data show X", "research suggests Y", and "therefore you should do Z." The latter is where clinical validation and clinician involvement become particularly important.

If you're considering actually building this

I would not start by hunting for one magical AI health app. I'd build around a common data layer—FHIR + OMOP + standardized genomics formats—and then plug AI components into it.

The NIH's All of Us implementation is a useful real-world example of this approach: it links EHR, wearable, physical-measurement and genomic data while standardizing much of the clinical information.

If you tell me whether this is for your own personal health data, a research project, or a product you're building, I can give you a concrete 2026 tool stack—including specific genomics, lab/EHR, wearable, database, and AI components.

Combining genome data, extensive lab panels, continuous wearable data, and electronic medical history into a single AI-driven health dashboard is the cutting edge of the "precision longevity" and preventative health movement.

Several consumer and clinician-guided platforms aggregate these complex data streams to generate personalized protocols:

• Fountain Life uses its proprietary Zori AI health intelligence engine. It is designed to synthesize continuous data from major wearables (Apple Watch, Oura, WHOOP, Garmin), uploaded medical history, advanced diagnostics (like multi-omics, genomics, and full-body MRIs), and blood biomarkers into a unified, evolving action plan.
• Superpower functions as an all-in-one health membership dashboard. It allows you to sync your wearables, upload legacy medical history and past labs, and pair them with extensive annual biomarker panels (~150+ markers) to generate a clinician-grade, centralized health protocol.
• Function Health provides a comprehensive tracking platform that analyzes 160+ lab biomarkers annually. It integrates your personal health history and tracks shifts over time, pairing data science with clinician reviews to offer targeted nutrition, supplement, and lifestyle guidance.
• InsideTracker bridges blood biomarker data, DNA/genomic insights, self-reported demographics, and data streamed from wearable devices. It leverages internal analytical engines (including integrations via Terra AI) to deliver personalized, food- and activity-based optimization recommendations.

To help narrow down which platform fits your specific needs, let me know:

• Are you looking for a fully consumer-driven app or a clinician-supervised medical membership?
• Do you already have raw genomic data and lab results ready to upload, or do you need a service that provides the testing from scratch?

Yes. The ecosystem is moving toward exactly this kind of “personal health AI”, but there’s an important distinction: a few products already combine most of these inputs, while others are infrastructure you can use to build your own system.

The most relevant options

• perplexity.ai — A consumer-facing option that connects medical records, wearables, wellness apps, and Apple Health, then uses AI to answer questions and surface biomarker trends. It currently isn't a genomics-first platform.
• orviva.ai — One of the more interesting matches for your idea. It says it can combine DNA/genomics, lab reports, CGM data, epigenetics, and other health data into a queryable health record intended for both people and AI agents.
• lunas.one — Combines medical records, labs, and genome reports and provides an AI layer over the resulting longitudinal record. It explicitly describes genomics, wearables, records, labs, and research as components of its longer-term ecosystem.
• about.storymd.com — Focuses on medical records, lab results, wearable data, and longitudinal AI insights. It supports FHIR/EHR connections and says it tracks more than 1,200 biomarkers.
• arxova.health — Combines medical records, labs, medications and wearable data with an AI assistant. Its emphasis is on keeping the individual's health information under their control.

If you're thinking about building this

I'd separate the system into four layers rather than looking for one magical AI product:

Human API is particularly relevant on the ingestion side: it normalizes medical records, laboratory results, medications and wearable/wellness data through an API.

For an Apple-centered implementation, HealthKit can act as a central repository for health/fitness data and can also expose clinical records as FHIR data from supported institutions.

The interesting opportunity

The difficult part isn't actually getting an LLM to say “your LDL is high.” It's creating a reliable longitudinal personal health model, e.g.:

Genotype → family history → medications → lab trajectories → sleep/activity → diet → symptoms → interventions → subsequent outcomes Then the AI could answer questions such as:

• “What factors appear to be driving my rising A1C?”
• “Which of my medications could interact with my pharmacogenomic profile?”
• “What changed in the six months before my blood pressure improved?”
• “Given my genetics, labs, sleep and activity, what preventive issues should I discuss with my doctor?”
• “What interventions have actually worked for me?”

That's considerably more powerful than a generic health chatbot.

One caveat: I'd treat the AI's output as decision support rather than autonomous medical advice. Genomic risk in particular is easy to overinterpret, and recommendations need to distinguish association from causation and account for medications, reference ranges, missing data, and clinical context.

If you're considering building this as a product, I can also lay out a concrete architecture—including genomics + FHIR + wearable APIs + database/schema + RAG/LLM layer + privacy/HIPAA + which vendors/APIs I'd choose today.

Yes. The market is moving toward multimodal personal-health AI—systems that combine longitudinal medical records, labs, wearables, and increasingly genomic data rather than interpreting each data source separately. A 2026 Nature Reviews Genetics review specifically describes AI-based integration of genomics with EHR data as an emerging area for personalized clinical decision-making.

Strong options to look at

• chatgpt.com — Connects supported medical records and Apple Health, allowing AI to reason over labs, medications, visits, sleep, activity and other connected information. It's particularly good for asking questions about trends and relationships among your data. OpenAI Help Center ChatGPT Caveat: genomic-data integration isn't currently the main strength.
• orviva.ai — One of the closest matches to your exact idea. It says it can combine raw DNA data, lab reports, methylation data, CGM data and other health information into a structured longitudinal record, then expose that information to AI agents such as ChatGPT or Claude through an API.
• arxova.health — Combines medical records, lab results, medications and wearable data, with an AI assistant that interprets results against your own history rather than only generic reference ranges.
• about.storymd.com — Focuses on connecting EHR data, labs and wearables and turning the longitudinal record into AI-generated explanations and health insights. It advertises FHIR connectivity and tracking of more than 1,200 biomarkers.
• veraxon.ai — Explicitly combines genome, labs, imaging, wearables and medical history into a healthspan-oriented risk/priority map, with clinician involvement.
• 23andme.org — Particularly interesting if genetics is central. Its 2026 beta combines genetics with medical records, laboratory data and lifestyle information to generate personalized health insights.
• formulahealth.ai — More focused on an ongoing "health OS": it says it integrates 300+ wearables/devices and labs and uses an AI coach to synthesize the data into health recommendations.

If you're thinking about building your own system

I'd actually recommend not relying on a single consumer app if your goal is serious personalized health analysis. A better architecture is:

Data layer → normalization → clinical/genomic interpretation → AI reasoning → evidence checking → recommendations → clinician review

For example:

• Genomics: VCF/23andMe/Ancestry raw data → variant annotation → clinically validated databases
• Labs: FHIR/HL7 or PDFs → normalized LOINC-coded biomarkers + units + reference ranges
• Medical history: FHIR/EHR → diagnoses, medications, procedures, family history and notes
• Wearables: Apple Health / Garmin / Oura / Fitbit / CGM → longitudinal time series
• Knowledge layer: guidelines, drug interactions, CPIC pharmacogenomics guidance, clinical literature
• AI layer: multimodal/LLM reasoning over the structured longitudinal record
• Safety layer: confidence levels, source citations, contraindication checks and escalation to a clinician

The key distinction is that an LLM shouldn't simply be asked, "Here's my genome and bloodwork—what should I do?" Genomic interpretation and medical recommendations need a validated evidence layer underneath the model.

For an individual today: I'd start with Health in ChatGPT for medical-record + wearable context, and investigate Orviva if having genomic data in the same structured record is important.

For a serious precision-health project: Orviva's API/structured-data approach is particularly interesting because it is designed to make the resulting health record usable by AI agents rather than just displaying dashboards.

If you tell me whether you're looking to use an existing app for yourself or build a system/product, I can narrow this down to the best 3–5 options and compare their data integrations, genomic capabilities, privacy, APIs, and clinical usefulness.

Combining multi-modal data streams—genomics, lab results, continuous wearable metrics, and electronic medical records (EMR)—into a single unified AI health recommendation engine is the cutting edge of precision medicine.

Because of data privacy regulations (like HIPAA/GDPR) and the structural complexity of merging distinct data types (static DNA vs. real-time streaming heart rate), fully-baked consumer "all-in-one" apps are still emerging. However, several distinct platforms, developer ecosystems, and enterprise tools address this space:

• Consumer & Advanced Health Platforms
• Developer & Interoperability Infrastructure (FHIR-based)
• Clinical & Research Multi-Omic AI Frameworks

Consumer & Advanced Health Platforms

These platforms attempt to bridge consumer biometrics with deeper health data, though full deep-genomic synthesis usually requires uploading raw DNA files (like 23andMe or Ancestry data) alongside manual or connected lab/wearable inputs.

• InsideTracker : Focuses heavily on integrating blood biomarker data, DNA insights, and fitness/wearable tracking data to generate customized nutrition, supplement, and lifestyle recommendations.
• Apple HealthKit / Google Health Connect Ecosystem : While not standalone AI recommendation engines themselves, they act as the primary data aggregation layers. They securely ingest wearable metrics, clinical lab results, and medical history (via FHIR-based hospital record integration) so third-party specialized AI apps can read the combined stream.
• Function Health / Origin-style comprehensive wellness platforms : These platforms coordinate deep baseline lab testing and medical history review, increasingly layering machine-learning models to spot longitudinal shifts when paired with continuous tracking.

Developer & Interoperability Infrastructure (FHIR-First)

If you are looking to build or use an integrated backend that natively speaks the language of medical records and consumer devices, healthcare interoperability frameworks are the gold standard:

• Medplum : An open-source, FHIR (Fast Healthcare Interoperability Resources) native platform. It allows developers to unify electronic health records (EHR), connected Bluetooth/wearable devices, and custom data streams into a compliant database schema where AI bots can run diagnostic summaries and trend analysis.
• Google Cloud Healthcare API / AWS HealthLake : Enterprise-grade cloud toolkits designed to ingest unstructured medical notes, DICOM images, structured lab results, and FHIR data streams, making them ready for custom LLM and machine learning analytics (multi-omic integration).

Clinical & Research Multi-Omic AI Frameworks

At the clinical trial and research tier, true multi-omic AI models process genomic sequences, transcriptomic data, and real-time patient telemetry:

• Nightingale Health : Uses advanced blood metabolomics combined with deep risk-prediction algorithms to forecast chronic disease trajectories long before symptoms present.
• Foundation Medicine / Tempus : Primarily focused on oncology and precision diagnostics, Tempus bridges clinical data, genomic sequencing, and molecular pathology using AI to recommend targeted therapies. While clinical-grade, they represent the blueprint for multi-modal synthesis.

To help narrow down the right approach, are you looking for a consumer-facing app to manage your own personal health, or are you a developer/researcher looking to build a multi-modal data pipeline using APIs?

Yes. The market is moving toward exactly this kind of multimodal personal-health AI—but no single consumer platform yet handles genome + longitudinal labs + wearables + complete medical history equally well.

The strongest options today

1. InsideTracker — closest off-the-shelf match

insidetracker.com is probably the closest match to your description today.

It combines blood biomarkers, DNA, wearable data, and lifestyle information, then produces recommendations around nutrition, exercise, supplements, and lifestyle. It integrates with Apple Watch, Garmin, Fitbit and Oura.

Its newer Terra AI platform goes further: it is designed to combine existing lab results, new labs, activity, sleep, nutrition and genetic information into continuously updated recommendations.

Best for: someone who wants a relatively turnkey "personal health optimization" system.

Caveat: it's more of a healthspan/wellness platform than a comprehensive clinical decision-support system.

2. ChatGPT Health — strongest general-purpose reasoning layer

chatgpt.com now lets eligible U.S. users connect Apple Health and supported medical records, allowing health information to be considered in context rather than asking questions about isolated lab results.

This is particularly interesting if your goal is:

"Look at everything I know about myself and help me figure out what matters." You could potentially combine connected health data with your genomic reports and other documents, then ask questions such as:

• "Which of my genetic risks are actually reflected in my current biomarkers?"
• "How have my ApoB, A1c and resting heart rate changed over the last two years?"
• "What lifestyle interventions have the strongest evidence given my specific profile?"
• "Which findings should I discuss with my doctor?"

I'd treat the resulting recommendations as decision support, not diagnosis or treatment instructions.

3. Perplexity Health

perplexity.ai is another interesting general-purpose option. It can connect medical records, wearables, wellness apps and Apple Health, and generates health summaries and context-aware answers.

It's particularly useful for researching and explaining your health information, rather than being a dedicated genomics/longevity optimizer.

4. 23andMe's newer direction

23andme.com announced an AI Health Summary that aims to combine genetics, medical records, labs and lifestyle data into personalized recommendations. The medical-record integration was still described as being in development/beta in the announcement, so I'd regard this as an emerging rather than fully mature solution.

The really interesting option: build your own

If what you mean is "I want one AI brain that understands my entire longitudinal health record", I'd actually consider building a personal health-data layer rather than relying on a single consumer product.

A good architecture would look like:

Genomic data → raw genotype/WGS + clinically interpreted variants

Clinical data → diagnoses, medications, allergies, procedures, encounters

Labs → normalized measurements + reference ranges + dates

Wearables → sleep, HR/HRV, activity, temperature, glucose if available

Lifestyle → diet, exercise, weight, symptoms, supplements

↓

Longitudinal health database

↓

AI reasoning layer

• trend detection
• risk-factor interactions
• evidence retrieval
• personalized recommendations
• uncertainty/confidence
• "what changed?" analysis

↓

Human-readable health plan

• What is abnormal?
• What is changing?
• What is probably actionable?
• What evidence supports an intervention?
• What should be discussed with a clinician?
• What should be monitored?

This is also where the research field is heading: recent work specifically examines AI integration of genomics + EHR data, because the combination can capture disease heterogeneity and longitudinal context much better than either dataset alone.

One important distinction

I'd separate "personalized health optimization" from "clinical decision support."

Tools such as InsideTracker can be excellent at answering "What lifestyle changes might improve my biomarkers?" But if the system starts answering questions like "Should I change my prescription?", "Do I have this disease?", or "Should I ignore this abnormal result?", you're in a much higher-stakes category. FDA guidance explicitly distinguishes clinical decision-support software and medical-device functions, and the regulatory landscape is evolving.

If you're thinking about building this rather than just using an existing service, that's an especially interesting problem. I can map out a concrete 2026 tech stack for ingesting 23andMe/WGS + Epic/MyChart records + Apple Health/Oura/Garmin + lab PDFs, normalizing everything, and putting an AI reasoning layer on top—including which APIs/data standards to use and where the clinical-safety boundaries should be.

Clinical data → diagnoses, medications, allergies, procedures, encounters

Labs → normalized measurements + reference ranges + dates

Wearables → sleep, HR/HRV, activity, temperature, glucose if available

Lifestyle → diet, exercise, weight, symptoms, supplements

↓

Longitudinal health database

↓

Combining genome data, longitudinal lab results, continuous wearable metrics, and electronic medical records (EMR) into a single, unified AI health advisor is the holy grail of precision medicine.

While a completely seamless, off-the-shelf consumer "omnibus" app that safely digests raw genomic files, clinical EMRs, and all wearable streams simultaneously into medical-grade prescriptions is still emerging due to regulatory (FDA/HIPAA) and data silos, several specialized AI-driven tools and ecosystem categories handle pieces of this puzzle.

1. Consumer & Prosumer Wellness AI Platforms

These platforms attempt to bridge multiple data silos—often connecting wearables and lab data, with varying degrees of genomic integration.

• Ultrahuman Ring Air Engine: Focuses heavily on metabolic and biometric tracking via wearables, utilizing an integrated AI engine that learns individual baselines (HRV, sleep, skin temp) to contextualize data rather than relying on generalized population averages.
• InsideTracker & similar DNA/Blood AI platforms: These platforms analyze blood biomarkers and DNA genetic traits (often via uploaded raw data from 23andMe or Ancestry), combining them with lifestyle inputs to generate personalized nutrition, supplement, and performance recommendations.
• Lab-Specific AI Assistants (e.g., MyLabcorp): Major diagnostics networks are rolling out native AI interpretations (utilizing secure LLM tech like OpenAI integration) to help patients parse complex, changing lab values over time and suggest clinical talking points.

2. Clinical-Grade & Multi-Modal Precision Medicine AI

For deep integration—including raw genomic sequencing (omics ), clinical EMR history, and continuous telemetry—the tools shift toward enterprise and clinical decision support (CDS) frameworks:

• Multi-Modal AI Research Frameworks: Systems trained on clinical data warehouses combine deep learning on electronic health records with genomic risk scores (polygenic risk scores). These are typically deployed within major hospital networks (such as Mayo Clinic or academic medical centers) rather than direct-to-consumer.
• Open-Source Data Normalization Tools: For developers or researchers looking to build custom pipelines, infrastructure tools like Open Wearables allow the normalization of disparate wearable endpoints into structured formats (like FHIR—Fast Healthcare Interoperability Resources), which are required to safely map wearable streams alongside medical history.

Key Challenges in Combining These Data Sources

• Interoperability & Data Silos: Genomic data (large VCF/FASTA files), EMRs (locked behind Epic/Cerner structural formats), and proprietary wearable APIs (Apple Health, Garmin, Oura) speak entirely different data languages.
• Hallucination & Medical Safety: General-purpose AI models (like standard ChatGPT or Claude) can ingest CSVs or text exports of these files, but they lack real-time clinical validation and can misinterpret multi-variable interactions (e.g., how a genetic predisposition interacts transiently with a sudden shift in wearable HRV and a minor lab elevation).

If you're looking to build or use a system like this, tell me:

• Are you looking for a consumer-facing app for personal use or a developer/research stack to build a custom tool?
• Do you already have the raw data files (like 23andMe/Ancestry data or Apple Health exports) ready to analyze?

I can help you narrow down the exact pipeline or software approach you need.

Integrating genome data, blood labs, wearable streams, and electronic medical records (EMRs) represents the bleeding edge of multi-modal precision medicine . While most consumer apps only look at one silo (like fitness data or 23andMe genetics), a growing crop of advanced platforms and AI engines are bridging these data streams together.

The primary AI-driven platforms capable of ingesting and synthesizing these diverse data layers include:

• InsideTracker: Highly optimized for data integration. It processes blood biomarker data, DNA insights, lifestyle inputs, and seamlessly syncs with major wearable devices (like Garmin and Apple Health) to generate automated, science-backed nutrition and performance recommendations.
• Function Health: A comprehensive preventative platform that tracks extensive lab biomarkers (including foundational, hormonal, and heavy metal panels) alongside genetic insights and medical histories. It employs AI-backed clinical summaries to help decode complex, cross-system lab results.
• Bioscope.ai: A clinical-grade platform designed to ingest raw multi-omic data (such as 30X whole-genome sequences and microbiome data) alongside electronic medical records and unstructured clinical notes. It uses an orchestration layer of large language models grounded in peer-reviewed medical literature to map comprehensive health dimensions.
• Perplexity Health: An emerging aggregator that connects directly with electronic health record networks (covering millions of providers) and pulls in continuous data streams from consumer wearables to formulate personalized tracking dashboards and pre-appointment insights.
• Avio Health: An enterprise and clinical AI platform built specifically to smash data silos. It unifies real-time wearable streams, lab panels, and genomic details into a centralized preventive intelligence engine meant for collaborative care.

To help narrow down which tool fits your exact needs, let me know:

• Are you looking for a consumer-facing self-service app or a clinical/developer tool?
• Which specific wearables and genetic raw data files (e.g., 23andMe, Nebula, whole-genome) do you currently have?

Yes. As of 2026, this is becoming a real product category: AI “personal health operating systems” that combine longitudinal clinical data with consumer health data. But very few tools truly combine all four of genomics + labs + wearables + medical history today.

The most relevant options

23andMe is probably the closest match to your exact description. Its 2026 Health Summary combines genetic information with blood labs and lifestyle data, and its HealthEx partnership is designed to bring in medical records as well. It's currently being tested through its Beta Testing Program rather than being a universally available mature product.

Google Health / Fitbit is particularly interesting if wearables are central. Google's 2026 Health app is designed to bring together wearable data, Apple Health, Health Connect and medical records, while its Gemini-based Health Coach can use labs, medications, visit history and wearable data when generating personalized guidance.

Perplexity Health is another broad consumer option. It can connect medical records, wearables, wellness apps and Apple Health and generate context-aware answers, biomarker trends and health summaries. It explicitly positions itself as informational rather than a diagnostic/medical-care replacement.

WHOOP + HealthEx is more wearable-centric. WHOOP's 2026 HealthEx integration lets members connect EHR information so the AI can contextualize recovery, strain and performance against medical history.

Neko Health takes a somewhat different approach: it combines extensive blood, cardiovascular, metabolic, body-composition and imaging measurements with wearable data, then has clinicians interpret the results and provide an action plan.

If you're thinking about building this

The interesting architecture isn't necessarily one monolithic AI. I'd think of it as:

Data layer

• Genomic variants / polygenic risk scores
• Lab results and trends
• EHR/FHIR: diagnoses, medications, procedures, encounters
• Wearables: sleep, HRV, resting HR, activity, glucose, etc.
• Nutrition/environment/lifestyle

→ Normalization + longitudinal patient model

→ Clinical reasoning / evidence layer

• Reference ranges and trajectories
• Drug–gene interactions
• Clinical guidelines
• Genotype–phenotype relationships
• Risk models
• Evidence retrieval

→ AI layer

• Personalized explanations
• Risk prioritization
• “What changed?” detection
• Behavioral recommendations
• Questions to discuss with a clinician

→ Safety layer

• Uncertainty/confidence
• Provenance for every recommendation
• Medication interaction checks
• Red-flag escalation
• Human/clinician review for high-stakes recommendations

Apple's HealthKit is useful as a data aggregation layer, because it can consolidate health/fitness data and, with permission, expose clinical records using FHIR.

The important distinction is that “AI that summarizes your health data” is now common; “AI that safely reasons across your genome, longitudinal labs, EHR and continuous physiological data to make clinically defensible recommendations” is still considerably harder.

If you're evaluating this for a startup/product you want to build, I can also map out the current landscape by API/data-access capability, genomics support, FHIR/EHR integration, wearable integrations, clinical validation, privacy/HIPAA posture, and pricing—and identify the best stack for actually building it.