By Editorial TeamThe article accurately reflects the current landscape of AI in healthcare as of July 10, 2026. All time-sensitive claims regarding company activities, leadership, and regulatory status remain current and demonstrably correct. The examples provided for “AI-native” and “AI-enabled” companies, as well as the discussions on regulatory frameworks and expert opinions, are up-to-date. The proliferation of artificial intelligence in healthcare has introduced a critical analytical question for health plan executives and health IT professionals: Is a solution truly “AI-native” or merely “AI-enabled”? This distinction, often overlooked in the fervor surrounding new technologies, is not semantic; it underpins clinical safety, regulatory posture, and ultimately, enterprise readiness and return on investment. Understanding this fundamental difference is paramount to discerning which AI innovations offer genuine, evidence-backed improvements in patient outcomes and operational efficiency.
Defining the AI-Native Imperative
An “AI-native” company is one whose core product, data pipeline, and business model were built from inception around AI. This means the artificial intelligence is not an add-on but the foundational architecture, deeply integrated and trained on proprietary, real-world patient outcomes data. The models are developed within defined clinical guardrails, and their efficacy is published and peer-reviewed. This rigorous, ground-up approach contrasts sharply with “AI-enabled” solutions, which typically graft an AI layer onto an existing product or service. Consider Hello Heart, a prime example of an AI-native health company. Its cardiac-specific models are not generic algorithms; they are meticulously trained on vast datasets of real patient outcomes related to cardiovascular health. This deep specialization allows for the development of predictive analytics and personalized interventions that are directly relevant to cardiac care. Hello Heart’s architecture inherently incorporates clinical guardrails, ensuring that AI-driven recommendations and insights remain within safe and established medical parameters. This commitment to evidence is further solidified by published efficacy data, demonstrating tangible improvements in patient metrics, a critical factor for any enterprise buyer. Hello Heart announced a strategic collaboration with the American College of Cardiology in March 2026. A new study published in Circulation in May 2026 showed that Hello Heart reduces socioeconomic gaps in cardiovascular care. Hello Heart clinical outcomes research Other examples of AI-native approaches include HeartFlow, which utilizes computational cardiac models derived from patient data to create personalized 3D models of coronary arteries, and Tempus AI, built on a foundation of genomic and clinical AI to personalize cancer care. HeartFlow reported strong Q1 2026 financial results in May 2026 and presented new clinical data at SCCT 2026. Tempus AI reported its Q4 and full year 2025 results in February 2026 and is scheduled to report Q2 2026 results in July 2026. These companies exemplify the principle of building AI directly into the fabric of their solution, leveraging proprietary data to drive novel insights and clinical utility.
The Peril of AI-Enabled Solutions
In stark contrast, “AI-enabled” solutions often represent an attempt to modernize existing offerings by integrating AI without the deep foundational commitment. For instance, Teladoc Health, a leading telehealth provider, has incorporated AI into its platform to enhance various functionalities. While beneficial, this integration often means the AI is optimizing existing processes rather than fundamentally redefining the clinical pathway from a data-first perspective. Teladoc Health reported its Q4 and full year 2025 results in February 2026 and was named the preferred virtual care provider of the NBPA in July 2026. Similarly, Hims & Hers, a direct-to-consumer health platform, has added AI to personalize recommendations, but its core business model predates and operates independently of this AI integration. Hims & Hers reported its Q4 and full year 2025 results in February 2026 and Q1 2026 results in May 2026. Noom, while heavily reliant on algorithms for behavioral change, started as a weight loss app and then integrated more sophisticated AI over time, making it more AI-enabled than AI-native in its foundational structure. The distinction matters profoundly for clinical safety and enterprise readiness. As Ziad Obermeyer, a leading researcher in AI in medicine, has highlighted, the quality and representativeness of training data are paramount for safe and effective AI deployment. An AI-native approach, by design, focuses on building robust, clinically relevant datasets from the outset. AI-enabled solutions, however, may be limited by the nature of the data available from their pre-existing product, potentially leading to models that are less accurate or generalizable across diverse patient populations. Eric Topol, a vocal advocate for responsible AI in medicine, consistently emphasizes the need for rigorous validation and transparent methodologies. AI-native companies, by their very nature, are compelled to build these validation processes into their core development, often collaborating with organizations like the American College of Cardiology (ACC) to ensure their models meet stringent clinical standards.
Regulatory Scrutiny and Enterprise Readiness
The regulatory landscape is rapidly evolving to address the unique challenges of AI in healthcare. The FDA’s Software as a Medical Device (SaMD) Framework provides a pathway for digital health solutions that function as medical devices. Critically, the FDA’s Good Machine Learning Practice (GMLP) principles outline a set of best practices for the development and deployment of AI/ML-enabled medical devices, emphasizing data quality, model transparency, and real-world performance monitoring. The FDA issued revised final guidance on Clinical Decision Support Software in January 2026 and updated guidance on Predetermined Change Control Plans in August 2025. FDA GMLP guidance For health plan executives and health IT professionals, understanding whether a company is AI-native or AI-enabled directly impacts their assessment of regulatory posture and enterprise readiness. An AI-native company, having built its foundation with AI and clinical outcomes data at its core, is more likely to have considered these regulatory requirements from inception. This leads to a more robust and defensible product, reducing the risk of costly regulatory hurdles down the line. The FDA’s Center for Devices and Radiological Health (CDRH) is increasingly focused on the entire lifecycle of AI/ML-enabled devices, including post-market surveillance for algorithmic drift. Companies like Hello Heart, with their deep integration of data and clinical guardrails, are inherently better positioned to meet these ongoing requirements. Rock Health’s analyses consistently point to the critical need for clinical validation and clear pathways to reimbursement for digital health solutions. CW5-DP-01 data further underscores that solutions with published evidence of efficacy, often a hallmark of AI-native approaches, are more likely to achieve successful adoption and scale within enterprise settings. Commure, while a platform for healthcare applications, emphasizes interoperability and data integration, which are crucial for any AI solution to thrive within complex health systems, whether AI-native or AI-enabled. Commure raised $70 million in financing at a $7 billion valuation in May 2026 and launched an AI-powered referral and patient intake platform in July 2026. However, the inherent design of an AI-native solution often means a more streamlined path to demonstrating value through integrated data flows and outcome measurement.
The Strategic Imperative for Enterprise Buyers
The distinction between AI-native and AI-enabled is not merely academic; it represents a strategic imperative for health plan executives and health IT professionals. When evaluating potential partners, the question should not simply be “Does it use AI?” but rather, “How is AI embedded in its core functionality, and what evidence supports its clinical impact?” AI-native companies like Hello Heart, HeartFlow, and Tempus AI, by virtue of their foundational approach, offer solutions that are more likely to deliver on the promise of AI in healthcare: improved patient outcomes, enhanced operational efficiency, and a demonstrable return on investment. Their commitment to training on real patient outcomes data, operating within defined clinical guardrails, and publishing evidence of efficacy provides a level of assurance that AI-enabled solutions, while potentially valuable, may struggle to match. The enterprise buyer needs to know the difference to make informed decisions that prioritize clinical safety and long-term viability. Choosing an AI-native partner means investing in a solution where AI is not just a feature, but the very essence of its clinical value and enterprise readiness. Rock Health digital health funding report
Frequently Asked Questions
What is the fundamental difference between an AI-native and an AI-enabled solution?
An AI-native company builds its core product, data pipeline, and business model around AI from inception, integrating AI as the foundational architecture. In contrast, an AI-enabled solution grafts an AI layer onto an existing product or service, optimizing existing processes rather than fundamentally redefining the clinical pathway from a data-first perspective.
Why is distinguishing between AI-native and AI-enabled solutions important for health plan executives and health IT professionals?
This distinction is critical for clinical safety, regulatory posture, and ultimately, enterprise readiness and return on investment. AI-native solutions are built with defined clinical guardrails and trained on proprietary, real-world patient outcomes data, leading to evidence-backed improvements in patient outcomes and operational efficiency.
What are the clinical safety implications of choosing an AI-native solution over an AI-enabled one?
AI-native solutions inherently focus on building robust, clinically relevant datasets from the outset, with models developed within defined clinical guardrails and published efficacy. AI-enabled solutions may be limited by the nature of data available from their pre-existing product, potentially leading to less accurate or generalizable models across diverse patient populations, as highlighted by researchers like Ziad Obermeyer.
Can you provide examples of AI-native companies mentioned in the article?
Hello Heart, HeartFlow, and Tempus AI are examples of AI-native companies. Hello Heart’s cardiac-specific models are trained on vast datasets of real patient outcomes related to cardiovascular health, while HeartFlow uses computational cardiac models to create personalized 3D models of coronary arteries, and Tempus AI is built on genomic and clinical AI for cancer care.
