Setting the stage: Why personalized preventive care is having a moment
Preventive health is breaking out of the annual-checkup mold as cheaper testing, smarter sensors, and AI that can synthesize signals converge to make continuous monitoring feel both possible and pragmatic. In this shift, Function Health positions itself at the inflection point, stitching together large-scale biomarker testing, advanced imaging, and longitudinal analytics to preempt disease rather than react to it. The stakes are clear: if AI can translate complex, multimodal data into timely, personalized actions with reliability, the model could reset expectations for consumer health and primary prevention.
What this timeline covers and why it matters now
This timeline traces Function Health’s path from its 2023 founding through its 2025 funding surge and AI rollout, highlighting how product moves, research ambitions, and strategy shifts compounding over time changed the company’s scope. It focuses on how capital, partnerships, and acquisitions converged to build a unified “medical intelligence” layer capable of turning diverse inputs into individualized guidance. The context matters for investors, clinicians, and consumers weighing whether this approach can scale safely, affordably, and equitably in real-world care.
Today’s relevance: Cost, access, and the promise of an intelligence layer
Consumers are demanding unified, longitudinal views across labs, imaging, wearables, and records—something legacy care rarely delivers at speed or scale. Function’s lower price point and bundled advanced diagnostics reflect pressure to democratize access while maintaining clinical rigor. Moreover, generative AI now feels capable of tailoring recommendations and updating them as evidence evolves, though workflow fit and real-world validation remain decisive hurdles for trust and adoption.
From founding to “medical intelligence”: A chronological timeline of key moments
2023 – Founding, beta launch, and the data-dense preventive thesis
Function Health launched in 2023 as a membership-based preventive platform offering access to more than 160 biomarker labs across cardiometabolic, hormonal, thyroid, nutrient, immune, aging, cancer signals, and autoimmunity markers—far exceeding typical primary care panels. Early capital, part of a total $53 million before the Series B, funded network buildout, scaled lab operations, and consumer onboarding. Membership pricing debuted at $499, signaling a comprehensive, premium service aimed at early adopters eager for data-dense baselines.
Late 2023 to 2024 – Scaling multi-modal inputs and consumer demand
Following a mid-2023 beta, the company accelerated test throughput and ultimately reported over 50 million lab tests completed since launch. Partnerships expanded to integrate wearables and connected devices, laying groundwork for unified longitudinal records and trend-based insights rather than isolated snapshots. The growing telemetry began to set the stage for an intelligence layer that could prioritize what matters most for each member.
2024 – Advanced screening partnerships take shape
Function partnered with Grail to offer the Galleri multi-cancer early detection test, positioning multi-cancer screening alongside dense biomarker profiles to catch risk signals earlier. The platform emphasized clinician-augmented interpretation, preparing for AI that would surface context, suggest next steps, and prioritize follow-up actions for members and care teams without displacing medical judgment.
May 2025 – Acquiring Ezra to add AI-enabled full-body imaging
In May 2025, Function acquired Ezra, bringing AI-enabled full-body imaging under the same membership to complement lab-based insights with noninvasive risk detection. This move unified two advanced diagnostic modalities—comprehensive labs and imaging—reducing fragmentation for consumers and creating a richer substrate for a cross-modal intelligence layer.
Mid-2025 – $298M Series B, valuation to $2.5B, and price cut to $365
In mid-2025, Function raised an oversubscribed $298 million Series B led by Redpoint Ventures, lifting valuation to $2.5 billion. The financing was framed as fuel for logistics scale-up, R&D, and AI infrastructure. At the same time, the annual membership dropped from $499 to $365, aiming to speed adoption and move beyond early adopters into a broader consumer base.
Mid- to Late 2025 – Launching the Medical Intelligence Lab and member-facing AI
Function debuted the Medical Intelligence Lab, led in part by physician-scientist Dan Sodickson, to unify fragmented inputs—labs, imaging, wearables, devices, and medical records—into evolving, personalized insights. Members gained an AI chatbot grounded in their data and step-by-step protocols translating findings into actions, with analysis of prior labs and notes tailoring guidance as evidence advances. Leadership framed the system as clinician augmentation: a triage and translation layer to streamline decisions rather than automate diagnoses.
What changed and why it matters: Turning points, themes, and open questions
Three turning points defined the trajectory. First, unifying advanced diagnostics—bundling Galleri and Ezra with dense lab panels—shifted the narrative from “more testing” to an integrated early-detection stack. Second, the $298 million Series B and price cut redirected the company from a premium niche toward scaled access, funding the logistics and AI backbone. Third, the Medical Intelligence Lab reframed the product from data-rich to insight-rich, promising actionable, longitudinal guidance.
Several themes emerged. Continuous, multi-modal monitoring is supplanting episodic care, with AI as real-time interpreter. Consumers expect a unified health view and will pay if it reduces friction and clarifies next steps. Clinician-augmented AI is becoming the norm in preventive contexts, balancing automation with oversight.
Open questions persist. Validation at scale requires prospective outcomes, cost-effectiveness signals, and clarity on false-positive and false-negative rates across populations. Interoperability needs deeper EHR integration—TEFCA alignment, HL7 FHIR, and bi-directional data flow that fits clinical workflows. Equity hinges on performance across diverse demographics and affordability beyond early adopters. Regulatory clarity must define when components qualify as software as a medical device and what guardrails suit generative recommendations.
Beyond the headline: Nuances, competitive context, and what experts are watching
Rollout will vary by region, given differences in FDA oversight of AI-enabled imaging and risk stratification, as well as variable access to imaging and phlebotomy networks that shape turnaround and experience. Competition spans consumer health platforms like One Medical, Forward, and Carbon Health, and data ecosystems from Apple, Oura, WHOOP, and Levels; few, however, bundle labs, imaging, and AI under a single membership. Imaging entrants such as Prenuvo focus on scanning; Function’s wager is that imaging paired with labs plus an intelligence layer offers superior signal and actionability.
Experts emphasize calibrated thresholds, human-in-the-loop review, and clear escalation paths to curb overdiagnosis and anxiety. Methodologically, multimodal foundation models fusing time-series, imaging, and structured labs are poised to sharpen early risk detection and personalize protocols. Real-world evidence programs and payer pilots remain pivotal, with endpoints such as avoided hospitalizations, earlier-stage detection, and adherence improvements.
Misconceptions to address
More data is not automatically better; relevance, context, and timing determine value. AI is not replacing clinicians; near-term utility is prioritization, explanation, and protocol personalization. Preventive care is not one-size-fits-all; equity demands validation across age, sex, ethnicity, and comorbidity profiles to avoid widening gaps.
Closing thought
If Function’s intelligence layer consistently converts multimodal signals into precise, validated actions at a price that scales, it sets a workable template for truly personal preventive care. The next steps are straightforward: deepen EHR interoperability, expand diverse-cohort validation, publish outcomes with payer-aligned endpoints, and refine clinician-in-the-loop workflows so guidance remains accountable, explainable, and timely.
