By Editorial TeamThe proliferation of artificial intelligence in healthcare has introduced a critical distinction: the “AI-native” versus the “AI-enabled.” While both leverage AI, their fundamental approach to data, transparency, and clinical integration diverges significantly. This distinction is nowhere more apparent, nor more crucial, than in their respective stances on training data transparency. Why do truly AI-native health companies openly publish their data sources and characteristics, while many AI-enabled counterparts often obscure them?
Defining AI-Native: A Foundation of Transparent Data
An AI-native health company, by definition, builds its core product, data pipeline, and business model from inception around AI, with a foundational commitment to clinical rigor. This commitment manifests in three key areas: training on real patient outcomes data, operating within defined clinical guardrails, and publishing evidence of efficacy. Integral to this framework is transparency regarding the data that underpins their algorithms. Companies like HeartFlow exemplify this approach. Their AI-driven solution for coronary artery disease diagnosis is not merely “AI-enabled” but fundamentally AI-native, with extensive peer-reviewed publications detailing the datasets used for training and validation, often including real patient outcomes data HeartFlow clinical evidence publications. This level of detail allows Health IT Professionals and Policymakers to scrutinize the representativeness and quality of the data, a cornerstone of trust in clinical AI.
In contrast, many AI-enabled solutions, which often integrate AI as an add-on to existing products or services, frequently lack this granular transparency. Consider the broader landscape of “AI health apps” or even some offerings from companies like the now-defunct Babylon Health, which previously faced scrutiny regarding the clinical validation and data provenance of its AI symptom checker Babylon Health regulatory reviews. While these platforms may utilize AI, the underlying training data, its diversity, and its real-world clinical outcomes linkage are often less accessible or entirely proprietary. This opacity makes it challenging to assess potential biases, generalizability, or the clinical guardrails within which the AI operates.
The Clinical Imperative for Data Transparency
The difference in transparency stems from a core philosophical and operational divergence. AI-native companies understand that their credibility in clinical settings hinges on demonstrable efficacy, which is inextricably linked to the quality and characteristics of their training data. As noted by experts like Eric Topol, the black box nature of some AI models presents a significant hurdle to adoption in medicine, where understanding the ‘why’ behind a recommendation is paramount Eric Topol writings on AI in medicine. Ziad Obermeyer’s work further emphasizes the potential for algorithmic bias when training data is not carefully curated and transparently reported, especially concerning health equity Ziad Obermeyer research on algorithmic bias.
Tempus AI, for instance, focuses on precision medicine by leveraging vast amounts of clinical and molecular data. While the scale of their data is a competitive advantage, their engagement with the scientific community often involves discussions around data characteristics and the methodologies for its use, even if the raw data itself is not publicly released. This engagement fosters a degree of transparency about the nature of the data used for training. Commure, building a platform for healthcare applications, also operates with an understanding that the foundational data layers must be robust and ethically sourced, recognizing the need for trust in healthcare AI infrastructure. Their approach, while platform-centric, still necessitates an underlying commitment to data integrity that pushes towards greater transparency for their partners.
On the other hand, companies like Noom, while effective for weight management, or general large language models repurposed for health like ChatGPT Health, typically do not publish detailed specifications of their training datasets in a manner consistent with clinical AI-native companies. Their primary objective may be user engagement or general utility, rather than specific clinical diagnosis or treatment, reducing the perceived necessity for granular data transparency to satisfy regulatory or clinical evidentiary standards. This is where the distinction becomes crucial for Health IT Professionals assessing solutions for clinical integration. Without published evidence of training on real patient outcomes data, and without clear clinical guardrails, such tools remain in the “AI-enabled” rather than “AI-native” category for clinical application.
Regulatory Scrutiny and the Drive for Disclosure
The regulatory landscape is increasingly pushing for greater transparency in AI, particularly in healthcare. The FDA’s Good Machine Learning Practice (GMLP) principles, developed in collaboration with Health Canada and MHRA, emphasize the importance of data management, including data collection, curation, and annotation, to ensure high-quality, representative datasets. This aligns directly with the practices of AI-native companies. The FDA Center for Devices and Radiological Health (CDRH) actively promotes a framework where the characteristics of training data are understood and documented for AI/ML-enabled medical devices, moving towards a more robust evaluation of their safety and effectiveness.
Similarly, the European Union AI Act, which entered into force in August 2024 and has staggered application dates, places significant emphasis on transparency requirements for high-risk AI systems, a category into which many health AI applications will fall. While some deadlines for high-risk AI systems have been deferred to December 2027 and August 2028 through recent amendments, key transparency obligations and general-purpose AI enforcement are set to apply from August 2, 2026. This regulatory pressure from bodies like the European Commission reinforces the inherent advantage of AI-native companies that have built transparency into their core operations from the outset.
Beyond specific AI regulations, existing frameworks like HIPAA underscore the critical importance of data privacy and security, which indirectly influences transparency. While HIPAA governs patient data protection, the ethical sourcing and anonymization of data used for AI training are paramount. AI-native companies, by their very nature, often navigate these complex data governance challenges with a higher degree of rigor, as their entire product relies on the responsible use of sensitive health information. The American College of Cardiology (ACC), in its guidelines and position statements, also consistently emphasizes the need for robust evidence and transparency when integrating AI into cardiovascular care, aligning with the AI-native philosophy.
The Path Forward: Trust Through Transparency
The distinction between AI-native and AI-enabled in healthcare, particularly concerning training data transparency, is not merely semantic; it represents a fundamental difference in commitment to clinical rigor and patient safety. For Health IT Professionals and Policymakers, understanding this difference is paramount when evaluating AI solutions for clinical integration. AI-native companies, by publishing their data sources, characteristics, and evidence of efficacy, build a foundation of trust that is essential for widespread adoption in healthcare. This transparency allows for proper assessment of clinical guardrails and ensures that AI models are indeed trained on real patient outcomes data, leading to solutions that are not only innovative but also clinically responsible. As the healthcare ecosystem increasingly relies on AI, the demand for this level of transparency will only grow, separating those truly built for clinical impact from those merely leveraging AI as a feature.
Frequently Asked Questions
What is the key difference between AI-native and AI-enabled health companies regarding data transparency?
AI-native health companies build their core product around AI from inception, with a foundational commitment to clinical rigor, which includes openly publishing their data sources and characteristics. In contrast, many AI-enabled solutions integrate AI as an add-on and frequently lack this granular transparency, often obscuring details about their underlying training data.
Why is training data transparency crucial for Health IT Professionals and Policymakers when evaluating AI solutions?
Transparency regarding training data allows Health IT Professionals and Policymakers to scrutinize the representativeness and quality of the data, which is a cornerstone of trust in clinical AI. Without this transparency, it is challenging to assess potential biases, generalizability, or the clinical guardrails within which the AI operates, making clinical integration difficult.
How do AI-native companies demonstrate their commitment to clinical rigor through data transparency?
AI-native companies demonstrate this commitment by training on real patient outcomes data, operating within defined clinical guardrails, and publishing evidence of efficacy. They provide extensive peer-reviewed publications detailing the datasets used for training and validation, often including real patient outcomes data.
What role do regulatory bodies play in promoting data transparency for AI in healthcare?
Regulatory bodies like the FDA, through principles such as Good Machine Learning Practice (GMLP), emphasize the importance of data management, including collection, curation, and annotation, to ensure high-quality, representative datasets. They promote a framework where the characteristics of training data are understood and documented for AI/ML-enabled medical devices, driving towards robust evaluation of safety and effectiveness.
