Is AI Medical Search Becoming a Commodity?
Australia-based startup Heidi just launched Heidi Evidence, a new AI medical search feature integrated into its documentation platform.
This is yet another company offering an AI medical search feature on their platform, which raises the question:
Is AI medical search becoming a commodity like ambient documentation?
After coming across Heidi's news, I went straight to the Healthcare Huddle Slack community to gather stakeholders' thoughts. I questioned whether "everyone" needs an AI medical search feature to stay competitive. Fellow Huddlers (all physicians) Cyrus Attia, Bhargav Patel, and Sudeep Bansal provided great insights that inspired this article.
In this article, I'll briefly highlight Heidi Evidence, give an overview of the AI medical search platform space, and then answer whether AI medical search is becoming commoditized—and if so, who's best positioned to succeed.
The Deets: Heidi Evidence
Heidi is an Australia-based AI medical scribe founded in 2019. It raised $65M in Series B last October, is valued at over $450M, and operates across the US, Canada, Australia, New Zealand, and the UK.
The new feature, Heidi Evidence, is a free AI medical search tool integrated into its scribe platform. Similar to Doximity GPT and Open Evidence, it provides evidence-based summaries with citations from medical literature.
Key features:
Built on Anthropic's Claude for clinical conversation interpretation and medical literature synthesis.
Authoritative sources including HealthPathways, BMJ Group, NICE, MIMS.
Ad-free, permanently: funded through enterprise scribe revenue.
Source Control: customize sources, upload proprietary documents, and create team-specific collections (a unique differentiator).
Scribe Integration: generates multi-day summaries and cited documents directly from clinical encounters.
Heidi Evidence works standalone or integrated.
AI Medical Search Space
I first wrote about this space back in 2024, when platforms like Open Evidence and Doximity GPT were just starting to gain traction among physicians. At the time, the concept was still new enough that most of us were experimenting. Two years later, the space has exploded—in both the number of players and the money flowing into it.
Let me walk through the key players and what they bring to the table.
Market
UpToDate Expert AI (Wolters Kluwer): The longstanding gold standard in clinical decision support, now layering generative AI on top of its physician-reviewed content. Its moat is trust, EHR integration, and the fact that most of us already have it open during rounds. I wrote a deep dive on Expert AI if you want the full breakdown.
Doximity + Pathway Medical: Doximity acquired Pathway Medical to build out DoxGPT, which bundles clinical Q&A, scribe, and documentation tools into one physician-facing platform. Its advantage is distribution—Doximity reaches over 80% of U.S. physicians. Check out my deep dive here.
Open Evidence: The fastest-growing platform in this space. Open Evidence went from a $1 billion valuation in February 2025 to $12 billion by January 2026, raising roughly $700 million across four rounds in under a year. It has content deals with NEJM and JAMA, and its focus on medicine-only answers with primary research citations has made it a daily driver for many physicians, myself included.
ClinicalKey AI (Elsevier): Pairs Elsevier's premium medical content with generative AI, often bundled into enterprise deals with health systems.
DynaMed / Dyna AI (EBSCO): An evidence-curated database with AI-enhanced natural language queries and EHR integrations. Competes directly with UpToDate in the institutional market.
AMBOSS: Expanding beyond medical education into clinical decision support with an AI "medical co-pilot." Strong editorial credibility and a loyal user base among trainees and early-career physicians. I’ve been experimenting with this platform some more!
The Money
The broader AI in healthcare market was estimated at $36.7 billion in 2025 and is projected to reach over $500 billion by 2033, growing at a 39% CAGR. Healthcare AI spending nearly tripled in 2025, hitting $1.4 billion — outpacing the entire vertical AI market from just a year prior.
Within this broader wave, the AI clinical decision support market—I think, the closest proxy for AI medical search—reached roughly $2.8 billion in 2025 and is projected to hit $15.3 billion by 2033.
But the funding concentrated in AI medical search specifically tells the clearest story. Open Evidence alone has raised $700 million in under 12 months, and its $12 billion valuation makes it the most valuable startup in the space by a wide margin. Doximity, already a public company, is investing heavily in AI tools through acquisitions. And now you have companies like Heidi entering the ring with venture-backed war chests of their own.
I use these tools every day—on rounds, in clinic, between consults—so it makes sense that the capital is flowing.
Dashevsky’s Dissection
The trend here is bundling. Companies that started as AI scribes—like Heidi—are now adding medical search features. Companies that started with medical search—like Open Evidence—are now adding scribes and documentation tools.
The logic is simple: if a physician already uses your tool to document visits, why not also be where they look up clinical questions? AI medical search is becoming a feature, not a standalone product.
So what will it take to win? According to the discussion in the Healthcare Huddle Slack, trust is key. But the business model matters too. Take UpToDate, which sells to enterprises. It's a trusted name in hospitals and among physicians. We've been using it for years. I remember discovering it as a med student and thinking it was the coolest platform ever (still is!). UpToDate's latest AI feature is called Expert AI. Unlike other players in the space (e.g., Open Evidence), Expert AI is trained on UpToDate content that's already been expert-reviewed (human)—not on all journal articles out there. That's unique. UpToDate is trustworthy, and I know from conversations that they've been meticulous in making sure their model is safe and accurate.
Cost will be another deciding factor. Doximity GPT and Open Evidence, for example, are free for physicians (though we physicians are the product via ads). Smaller practices, Dr. Sudeep Bansal noted, may not afford UpToDate's enterprise features, pushing them toward free models like Doximity and Open Evidence.
So, as I usually do with my Huddles, I'll briefly explain how all of the above impacts key stakeholders:
Patients: Faster, more accurate clinical answers mean better care at the point of decision. When your physician can pull up the latest evidence on a treatment in seconds instead of minutes, it directly improves the quality of the conversation and the plan. Theoretically, though, if these tools are ad-supported or commercially influenced, the evidence your doctor sees could be shaped by who's paying—not by what's best for you.
Physicians: We now have more AI medical search options than ever, which is great for workflow efficiency. But the commoditization of these tools means we need to be thoughtful about which ones we trust.
Health systems: The bundling trend creates a real procurement question. Do you buy best-in-class point solutions—a standalone scribe here, a standalone search tool there—or do you go with an all-in-one platform that does both? Systems that already pay for UpToDate may be slower to adopt newer entrants. But smaller practices and independent physicians will gravitate toward free or bundled tools, which could fragment how evidence is accessed across the care continuum.
In summary, AI medical search is quickly becoming a commodity feature rather than a standalone product. The space is crowded, well-funded, and moving fast—with Open Evidence leading in growth, UpToDate leading in trust, and Doximity leading in distribution. For physicians, the tools are getting better and more accessible. But as with any tool we use to inform clinical decisions, the question we should always ask is: where is the evidence coming from, and who's paying for it?
AI Will Bring Our Nurses Back to the Bedside
In January 2026, nearly 15,000 NYC nurses struck for safe staffing, violence protections, and pay—plus a new demand: job security against AI.
Dr. Alyssa Chen argues AI threatens nursing differently than expected. It's targeting administrative roles that pulled experienced nurses away from bedside care.
AI now automates medication refills, pre-op calls, billing codes, and quality reviews—the desk work that became a burnout escape. Tools like Doctronic, for example, can replace hundreds of nursing hours.
When these administrative paths disappear, Dr. Chen suggests we might finally solve the nursing shortage.
👉 Read Dr. Chen’s full argument here.
