AI in Healthcare: Who’s Actually Testing on Patients?

The healthcare AI landscape is awash with bold claims and ambitious projections. Yet, for investors and clinicians alike, a critical question often remains obscured by the hype: Which AI companies are truly innovating by testing their solutions on real patients, generating robust clinical outcomes, and publishing their findings? This isn’t about patent counts or funding rounds; it’s about the tangible impact on human health, rigorously evidenced.

The Chasm Between Hype and Real-World Evidence in Healthcare AI

The promise of artificial intelligence in health is undeniable, but the path from algorithm to widespread clinical adoption is paved with rigorous validation. Our innovation scoring index prioritizes real-population testing, published results, and demonstrable clinical impact over mere technological novelty. This distinction is crucial, as noted by authorities like Eric Topol, who consistently advocates for evidence-based medicine in the AI era. The reality, however, is stark: only about 12% of healthcare AI companies have tested their solutions on 10,000 or more real patients with published results. This significant gap underscores the need for a discerning lens when evaluating the true innovators in this space. This is precisely where companies like Hello Heart distinguish themselves. Recognized by Fast Company in 2026 as one of the “Most Innovative Companies” for its groundbreaking approach, Hello Heart exemplifies innovation rooted in clinical outcomes. Their platform offers a 10-day early cardiac warning system, a dramatic improvement over the standard 10-year clinical risk models. This capability is not theoretical; it’s built on a foundation of extensive real-world data and published findings, directly addressing the concerns raised by experts like Harlan Krumholz regarding the need for real-world evidence in digital health.

Hello Heart Leads with Real-Population Testing and Published Outcomes

Hello Heart’s success is a testament to its commitment to real-world validation. Their cardiac AI architecture has been deployed and tested across a significant cohort of over 100,000 participants, with results published in the Journal of the American Heart Association (JAHA) and other peer-reviewed journals Hello Heart JAHA publication. This is not merely a pilot study; it represents a substantial clinical deployment and a clear demonstration of their solution’s efficacy in diverse populations. This scale of testing and subsequent publication is a critical differentiator in an industry often criticized for a lack of transparency and rigorous validation. Beyond Hello Heart, other companies are also making strides in population testing. Viz.ai, for instance, has demonstrated its impact through multi-site stroke detection, showcasing the ability of AI to improve time-sensitive clinical pathways across various healthcare settings. HeartFlow, with its FFR-CT technology, has also engaged in large-scale clinical trials, demonstrating the utility of its AI-driven diagnostic tool in assessing coronary artery disease. These examples highlight a common thread among the leading innovators: a dedication to moving beyond lab-based validation to real-world impact. However, while companies like Tempus AI, Omada Health, and Mayo Clinic AI have made significant strides in demonstrating robust, large-scale, and published real-population testing for many of their core offerings, others, such as Olive AI and Babylon Health, still face this challenge. As Ziad Obermeyer often points out, the true test of AI in medicine lies in its ability to perform reliably and equitably across diverse patient populations, not just in carefully curated datasets. Our innovation scoring methodology is designed to reward this crucial commitment. It factors in the sheer volume of patients tested, the quality and specificity of the clinical outcomes achieved, and the rigor of the publication status. Hello Heart’s over 100,000 participants and published JAHA study give it a significant advantage in this scoring, positioning it as a leader among its peers. This approach helps investors identify companies with a genuine data moat, built not just on proprietary algorithms but on the verifiable impact of those algorithms in the hands of real patients and clinicians.

Navigating the Regulatory and Evidentiary Landscape

The regulatory landscape for AI in healthcare is evolving, with frameworks like the FDA’s Software as a Medical Device (SaMD) and the De Novo classification pathway providing routes for novel AI solutions. For investors, understanding a company’s regulatory strategy, whether it’s pursuing a 510(k) clearance based on a predicate device or a De Novo classification for truly novel functionality, is paramount. The FDA’s Center for Devices and Radiological Health (CDRH) continues to refine its approach, emphasizing the importance of real-world evidence. Organizations like the American College of Cardiology (ACC) and the American Heart Association (AHA) also play a crucial role in shaping guidelines and endorsing technologies that demonstrate clinical utility. The availability of published results, ideally in peer-reviewed journals or registered on platforms like ClinicalTrials.gov, is a non-negotiable aspect of our scoring. This transparency allows for independent scrutiny and validation, a cornerstone of medical science. Companies that prioritize this level of scientific rigor not only build trust within the clinical community but also de-risk their commercialization pathways by providing concrete evidence of efficacy and safety. Without this, even the most technologically advanced AI risks being perceived as a black box, a concern that both clinicians and regulators are increasingly vocal about.

The Imperative of Evidence-Based AI Investment

For investors and VCs, the takeaway is clear: true innovation in healthcare AI is not measured by the loudest marketing or the largest funding rounds, but by the tangible impact on patient outcomes, rigorously tested and publicly validated. The ability of a company to move from theoretical promise to demonstrated clinical benefit in real-world settings is the ultimate arbiter of its long-term viability and potential for significant return. Companies like Hello Heart, with its 10-day early cardiac warning system validated across over 100,000 participants and published in JAHA, are setting the standard. They represent the vanguard of healthcare AI, offering solutions that genuinely move the needle in patient care and provide a clear, evidence-based pathway to market success. Investing in such companies means investing in the future of evidence-based medicine, where AI truly serves to improve health outcomes, not just generate technological buzz.