TL;DR: the shortlist for clinicians in a hurry
AI clinical decision support (CDS) in 2026 is a fragmented market with one clear behavioral winner. OpenEvidence reports more than 1 million physician consults per day and claims roughly 65% of US physicians as registered users, all free, ad-funded by pharma. UpToDate Expert AI remains the institutional anchor because hospital librarians and CMIOs already pay for the seat licenses. The wrong question is "which CDS is best". The right question is "which fits the access patterns I already have at the point of care, and what does my institution license for me anyway".
This post is for practicing physicians, residents, PAs, NPs, and clinical informaticists choosing between AI CDS tools as either a free overlay or an institutional purchase. Our editorial verdict, condensed:
Best overall (free, US physicians): OpenEvidence. Free, NPI-gated, literature-grounded answers with citations. The behavioral default among US physicians in 2026.
Best institutional gold standard: UpToDate Expert AI. Editor-curated topics with a generative layer; awards CME credit when you ask a clinical question, which most competitors still do not.
Best for differential diagnosis and clinical reasoning: Glass Health. DDx, problem representation, and assessment & plan in one workflow. The reasoning challenger.
Best for visual / dermatologic DDx: VisualDx. 32,000+ peer-reviewed images with image-based DDx; the long-standing default for derm, pediatrics, and ID.
Methodology framing: we aggregated public reviews from physicians on r/medicine, r/Residency, Doximity, and G2; cross-checked vendor documentation, FDA listings, and KLAS signals; and signed off through a board-certified physician on our editorial team. See our [full methodology](https://healthcareai.brainbyt.es/methodology).
How we evaluated 10 AI clinical decision support tools
We did not run a head-to-head clinical trial in our own practice. Our evaluation aggregates six sources with the standard Healthcare AI Hub weights: vendor documentation 30%, public review aggregators 20%, clinician community sentiment 20%, peer-reviewed literature 15%, vendor stability signals 10%, and specialty society guidance 5%. The mix matters more in CDS than in scribes because half the buying decision is institutional (your hospital library licensed it, you use it) and half is behavioral (you Google-replaced your search box with a free tool, your institution does not know).
We also gave extra weight to three CDS-specific signals: whether answers ship with traceable citations to the underlying evidence; whether the tool publishes its retrieval corpus and update cadence; and whether the vendor offers physician-only access (an integrity signal against AI hallucination tourism).
Every tool below carries a last-verified date of May 2026. Pricing in CDS changes more slowly than in scribes, but institutional licensing terms shift quarterly. If you spot stale data, email corrections@healthcareai.brainbyt.es and we publish a correction within seven business days.
Best overall, free for US physicians: OpenEvidence
OpenEvidence has, in roughly thirty months, eaten the behavioral default for US physician clinical Q&A. The company reported crossing 1 million physician-verified consults per day in late 2025 and publicly claims roughly 65% of US physicians as NPI-verified registered users. Founded in 2022, headquartered in the US, funded through a model the founders openly describe as "Bloomberg for medicine": free at the point of use, monetized through targeted, disclosed pharma advertising visible only to verified physicians.
The product is a search box. You type a clinical question; you get back a structured answer with inline citations to peer-reviewed literature, guidelines (NCCN, IDSA, AHA, USPSTF, ACOG), and FDA labels. Each citation is clickable and resolves to the actual source. The retrieval corpus refreshes continuously, which matters because in our editorial review the failure mode of generic LLMs in medicine is not bad reasoning, it is stale or fabricated references.
In our aggregated reviews from r/medicine and r/Residency over the past twelve months, the dominant sentiment shifted from "skeptical curiosity" in 2023 to "I open it before UpToDate now" in 2025. Residents in particular report opening OpenEvidence on the workroom monitor between patients. The free + physician-only model removes the friction that kept UpToDate behind a paywall for trainees rotating at unaffiliated sites.
Pros
Free for NPI-verified US physicians. No card, no trial, no institutional license required.
Inline citations to primary literature and society guidelines, with click-through resolution.
Continuous corpus refresh; new guideline releases typically searchable within days.
Roughly 65% US physician registration share according to vendor disclosures; the network effect on prompt quality is real.
Pharma advertising is disclosed, gated to verified physicians, and visually separated from clinical content.
Cons
Ad-funded. Some clinicians dislike pharma adjacency on principle, even when disclosed.
US-physician-gated. International clinicians, PAs, NPs, and nurses face inconsistent access depending on registration policy.
No CME credit attached to queries; UpToDate Expert AI and ClinicalKey AI both grant CME.
No formal FDA clearance as a clinical decision support device; positioned as a reference tool, which matters for liability framing.
Best for: US physicians and residents who want a free, fast, citation-backed second opinion at the point of care, especially at sites without UpToDate institutional access.
Read the full OpenEvidence review →
Best institutional gold standard: UpToDate Expert AI
UpToDate Expert AI, from Wolters Kluwer, is the tool your hospital almost certainly already licenses. Individual subscriptions list around $559 per year; institutional pricing is opaque and negotiated per site, with most US academic medical centers covered as a default benefit. In KLAS Clinical Reference 2024-2025 segments, UpToDate has held the top position for more than a decade, and the 2025 generative AI layer (now called Expert AI) has not changed that ranking; reviewers cite editorial curation as the differentiator that the LLM-only entrants cannot replicate.
The Expert AI layer overlays generative answers on the same editor-curated topic corpus that has defined the brand since the 1990s. Critically, you can still drop to the underlying topic when the AI summary feels thin, which in our editorial review is the right architecture: AI for speed, curated topic for depth. Wolters Kluwer also awards AMA PRA Category 1 CME credit for searches answered through Expert AI in eligible institutions, which is a small but real recurring incentive.
In our aggregated reviews, the consistent praise is editorial trust: clinicians know who edited the topic, when it was last updated, and what the conflict-of-interest disclosure is. The consistent criticism is price and clunkiness; the search interface still feels like it was designed in 2010, and the generative layer arrived late enough that some users had already migrated their muscle memory to OpenEvidence.
Pros
Editorial curation by named subspecialists; conflict-of-interest disclosed per topic.
Generative AI layer (Expert AI) overlays an established curated corpus, not a raw LLM.
CME credit for clinical questions answered through the tool in eligible institutions.
Deepest integration with Epic, Cerner / Oracle Health, and major library proxies (OpenAthens, EZproxy, Shibboleth).
HIPAA-attested; institutional infosec sign-offs already exist at most US health systems.
Cons
Expensive at the individual list price; effectively only worth it if your institution does not cover you.
Interface feels dated next to OpenEvidence and Glass Health.
The generative layer is newer and has lagged OpenEvidence on update freshness in head-to-head queries we ran in editorial review.
Best for: Attending physicians, hospitalists, and subspecialists at institutions that already license UpToDate; anyone who wants CME credit attached to point-of-care queries.
Read the full UpToDate Expert AI review →
Best for DDx and clinical reasoning: Glass Health
Glass Health is the clinical-reasoning challenger and a different shape of product from OpenEvidence or UpToDate. Founded in 2021 in San Francisco, Glass started as a "second-brain" for clinicians and pivoted into AI-assisted differential diagnosis and assessment & plan generation. Pricing is freemium: a free Lite tier with limited generations, then paid tiers from roughly $20 to $200 per month depending on volume and team size.
The interface accepts a one-line problem representation ("72M with progressive dyspnea on exertion, lower extremity edema, NT-proBNP 4,200") and returns a ranked differential with reasoning, recommended workup, and a draft assessment & plan grounded in literature with citations. The reasoning trace is the differentiator: instead of a single answer, you see the candidate diagnoses ranked with epidemiologic priors, distinguishing features, and the evidence that would shift each one. This matches the way residents are taught to think and, in our editorial review, is the closest any current product comes to an attending-on-your-shoulder.
In aggregated reviews from r/medicine and Doximity, Glass is praised by residents and internists who use it for tough zebras and for teaching; the criticism is that for routine bread-and-butter questions ("first-line agent for uncomplicated UTI"), OpenEvidence is faster and free. The two tools are increasingly used together rather than as substitutes.
Pros
Genuine clinical-reasoning workflow: problem representation, ranked DDx, A&P drafting in one tool.
Free Lite tier means residents and trainees can start without an institutional purchase.
Literature grounding with citations; not a raw LLM.
Privacy posture appropriate for de-identified clinical inputs; the team has been explicit about not training on user input.
Cons
Smaller corpus and slower update cadence than OpenEvidence or UpToDate.
Paid tiers add up quickly for residents on a budget if you exceed the Lite generation cap.
Newer vendor; institutional procurement teams will ask more questions than they would for Wolters Kluwer or Elsevier.
Best for: Residents, internists, hospitalists, and educators who care about the reasoning trace, not just the answer.
Read the full Glass Health review →
Best for image-based DDx: VisualDx
VisualDx, founded in 1999 and based in Rochester NY, is the long-standing default for image-based differential diagnosis. The corpus exceeds 32,000 peer-reviewed clinical images across dermatology, infectious disease, ophthalmology, oral medicine, and pediatrics; the AI layer (DermExpert and the broader VisualDx AI) accepts an uploaded image plus a short structured history and returns a ranked image-DDx. Individual subscriptions run around $399 per year; institutional licensing is the more common access pattern, and many US medical schools have campus-wide licenses.
The reason VisualDx persists in a market full of generalist LLMs is the image corpus itself. In our editorial review and aggregated reviews from r/dermatology and r/FamilyMedicine, the consistent point is that generalist multimodal models still misclassify dermatologic findings at clinically meaningful rates, especially across Fitzpatrick skin types IV through VI. VisualDx was an early signatory of equity-oriented image curation and the corpus is intentionally diverse across skin tones; that data advantage is the moat.
Pros
32,000+ peer-reviewed clinical images, curated for skin-tone diversity.
Image-first DDx workflow; no other CDS in the market matches the visual corpus.
Long vendor history (since 1999); institutional licensing well established.
Strong fit for primary care providers seeing skin / rash / lesion presentations who do not have dermatology nearby.
Cons
Individual pricing is steep if your institution does not cover you.
Outside of image-driven specialties (derm, ID, peds, ophtho) the value drops sharply.
Generative AI layer is narrower than OpenEvidence or UpToDate Expert AI.
Best for: Dermatologists, primary care providers serving rural or under-resourced populations, pediatricians, ID specialists, and any PCP who sees a rash and is honest enough to admit they want a second look.
Read the full VisualDx review →
What to look for: 5-criteria buyer's guide
Criterion 1: Evidence grounding and citation traceability
The single largest failure mode of generic LLMs in clinical use is fabricated citations. Every CDS tool worth using ships answers with inline citations that resolve to primary literature or named society guidelines. OpenEvidence does this well; UpToDate does this by virtue of its editorial model; Glass Health does this for its reasoning steps. Generic chatbots, including general-purpose AI assistants, still hallucinate citations at rates incompatible with bedside use. Demand traceable evidence or do not deploy the tool.
Criterion 2: Access pattern (institutional license vs. free + NPI-gated)
The honest answer to "which CDS should I use" depends on what your institution already pays for. If you have UpToDate institutional access, you have already paid for the gold standard and should use it. If you do not (you are at a community hospital, on a rotation away from your home institution, or in solo practice), OpenEvidence is free, fast, and physician-gated. Most clinicians end up using both, with OpenEvidence as the first stop and UpToDate as the dive-deeper second.
Criterion 3: Specialty fit
CDS is not a single product category. Image-driven specialties (derm, ID, ophtho) need VisualDx. Diagnostic-reasoning-heavy specialties (internal medicine, family medicine, hospitalist work) get the most from Glass Health. Reference-heavy specialties (oncology, infectious disease, cardiology) lean on UpToDate and OpenEvidence. Pediatrics has special needs around weight-based dosing where DynaMed and UpToDate dominate. Match the tool to the question type you actually ask.
Criterion 4: Privacy posture and PHI handling
If you paste anything resembling identifiable patient information into a chatbot, you have a HIPAA problem. The tools that are HIPAA-attested (UpToDate, ClinicalKey AI) handle PHI under a BAA when the institution licenses them. OpenEvidence positions itself as a reference tool, which means de-identified questions only; the same applies to Glass Health and VisualDx individual subscriptions. Read the BAA terms or do not paste the chart.
Criterion 5: Vendor stability and update cadence
Wolters Kluwer (UpToDate, Sentri7), Elsevier (ClinicalKey AI), and EBSCO (DynaMed) are the establishment incumbents and are not going anywhere. OpenEvidence is the strongest-funded of the AI-native entrants and crossed the 1M-consults-per-day threshold in late 2025. Glass Health is venture-funded and shipping fast. The smaller tools (DXGPT, iatroX, Medwise, Docus AI, Vera Health) are interesting but carry vendor-stability risk that matters more for institutional purchase than for free individual use.
How the field has shifted in 2026
The single biggest shift in 2026 is that the behavioral default for US physician clinical Q&A moved away from institutionally-licensed reference tools and toward free, NPI-gated, ad-funded literature search. OpenEvidence is the proximate cause; the underlying driver is that residents and early-career attendings adopted a free tool the way prior generations adopted UpToDate, and the cohort effect compounds. Wolters Kluwer responded by shipping Expert AI on top of UpToDate and attaching CME to queries, which protects the institutional license but does not win back the free-tier behavior.
A second shift: clinical-reasoning challengers (Glass Health, Vera Health, iatroX in the UK, Docus AI) are no longer experimental. Residency programs report informal use in morning report. Atropos Health has carved out a separate "real-world evidence on demand" niche, returning study-grade evidence from 160M de-identified patient records, used more by researchers than at the bedside but increasingly cited in CDS conversations. Bayesian Health crossed an FDA milestone in May 2026 with a 510(k) clearance for continuous AI sepsis monitoring, which is operationally a CDS device but functionally a different product category from the Q&A tools covered here.
Finally, the integration story is shifting from "log in to a separate website" to "embedded in the EHR". Epic and Oracle Health are both building native CDS sidebars; Wolters Kluwer is positioned through the existing UpToDate Epic integration; the AI-native entrants are negotiating placement. Expect EHR-embedded CDS to be a much larger story by the time we update this post in 2027.
Comparison table
Full side-by-side comparison: see the complete tool table.
Frequently asked questions
Is OpenEvidence actually free, or is there a catch?
It is genuinely free at the point of use for NPI-verified US physicians. The monetization is targeted pharma advertising shown only to verified physicians, similar to the model Doximity has run for over a decade. The privacy posture is that queries are de-identified and the corpus is public literature, so HIPAA is not the question; the relevant question is if you accept pharma advertising adjacent to clinical content. If the answer is no, use UpToDate.
Does my institutional UpToDate license cover Expert AI, or is it a separate purchase?
Increasingly bundled, but check with your library. As of mid-2026, most academic medical centers and large IDNs have Expert AI bundled into existing UpToDate Anywhere licenses with no extra cost to clinicians. Smaller hospitals and ambulatory networks sometimes received Expert AI as a separate line item. CME credit through Expert AI requires institutional eligibility, so confirm with your CME office before assuming it counts.
Should I trust a generic AI assistant (general-purpose chatbots) for clinical questions?
No. The failure mode is fabricated citations, not bad reasoning. Generic LLMs hallucinate references at rates incompatible with bedside use, and the citations they do produce frequently misattribute claims to real papers. Use a purpose-built tool with traceable evidence (OpenEvidence, UpToDate Expert AI, Glass Health, ClinicalKey AI). If you must use a general assistant, treat its output as a hypothesis to verify in a real CDS, not as an answer.
What about HIPAA when I paste patient information into a chatbot?
Do not. Even into HIPAA-attested tools, paste de-identified clinical scenarios only unless your institution has a signed BAA with the vendor that covers the specific use case. OpenEvidence, Glass Health, and Doximity GPT are reference tools that should receive de-identified questions. UpToDate and ClinicalKey AI handle PHI under institutional BAAs. The safe default is to write your question without names, MRNs, dates, or any of the 18 HIPAA identifiers.
Which CDS is best for residents who do not have institutional UpToDate access?
OpenEvidence as the daily driver; Glass Health when you want a reasoning trace; VisualDx if your institution licenses it for image-based questions. This stack is free or close to free for residents and matches what aggregated reviews from r/Residency and r/medicine describe as the modal 2026 setup at programs without strong library budgets.
Is there an FDA-cleared CDS tool I should know about?
Bayesian Health received FDA 510(k) clearance in May 2026 for continuous AI sepsis monitoring, which is an operationally important CDS device for inpatient settings but functionally different from the Q&A tools in this post. Most reference and DDx tools (OpenEvidence, UpToDate, Glass, VisualDx) are positioned as decision support and not as cleared diagnostic devices; the FDA distinction matters for liability framing and institutional procurement, not for clinician workflow.
Related reading on Healthcare AI Hub
Methodology + disclosure
This article aggregates public reviews from clinicians on r/medicine, r/Residency, Doximity, and G2; cross-checks vendor documentation, KLAS rankings, and FDA listings; and is signed off by our board-certified physician advisor. We may earn a commission when a clinician signs up through outbound links, at no extra cost. Full policy at /affiliate-disclosure. Tools were last verified in May 2026. To report stale data or factual errors, email corrections@healthcareai.brainbyt.es and we publish corrections within seven business days.