01 / Sources
The sources we draw on
Each review draws from a stack of named public sources. Weights below reflect how much each source family contributes to a tool’s position in our comparisons.
Source 01
Peer-reviewed literature
Weight 25%
- PubMed-indexed studies that evaluate the tool in clinical settings. JAMA, NEJM, BMJ, specialty journals when relevant.
Source 02
Reddit clinician communities
Weight 20%
- Public clinician and trainee discussions where AI tools are evaluated in working clinical context. Sentiment extracted, quotes attributed.
Source 03
Public review aggregators
Weight 15%
- G2, Capterra, Software Advice, TrustRadius profile pages. Star-ratings and recent qualitative reviews.
Source 04
Vendor stability signals
Weight 15%
- Funding rounds, leadership changes, acquisitions, EHR-marketplace certifications (Epic App Orchard, Cerner Code).
Source 05
Physician networks
Weight 10%
- Sermo and Doximity, closed physician-only platforms. Quoted with attribution and anonymization.
Source 06
Vendor documentation
Weight 5%
- Pricing pages, security attestations (HIPAA, SOC 2, HITRUST, FDA-510(k)), EHR-integration disclosures. Used as the factual source for specs and pricing, not as evidence for ranking position.
Source 07
Clinician YouTube reviews
Weight 5%
- Long-form clinician demos and product walkthroughs from board-certified or trainee-credentialed channels.
Source 08
Specialty society guidance
Weight 5%
- AAFP, AAP, ACP, AMA, ACR, ACS recommendations or endorsements when published.
02 / Principles
Six editorial principles that govern every review
Principle 01
Aggregation over self-testing
We do not run AI tools in our own clinical practice for the purpose of these reviews. Sites that claim hands-on testing of every tool in their index typically rely on the same public sources we do, with less transparency about it. We name our sources and link to them.
Principle 02
MD editorial sign-off
Every published review is reviewed by Henrik R., MD, a board-certified physician (Switzerland), before publication. The MD-verified badge appears only on tools whose reviews have completed sign-off within the past six months. Identity verifiable to vendor partners and editorial inquiries on request.
Principle 03
Affiliate transparency
Some outbound links are affiliate links. We disclose this inline at the point-of-occurrence (a sponsored badge next to each link) and at the bottom of every page. Rankings are editorial and never sold. We decline sponsorships from tools that fail our published evaluation criteria.
Principle 04
Source attribution per claim
Quantitative claims (pricing, integration counts, certification status) link to the source. Qualitative quotes from clinician reviews carry the source forum, subreddit, or platform with a date. We do not republish entire reviews; we quote with attribution under fair-use commentary.
Principle 05
Living documents, not frozen reports
Every review carries a last-verified timestamp per data category. Our scrapers re-run vendor documentation and community sentiment on a monthly cadence. Reviews older than 180 days carry a stale-data warning until refreshed.
Principle 06
Transparent uncertainty
Every tool page includes a What we have not verified block: data points the public sources do not let us validate (private SLA terms, enterprise pricing tiers below NDA, real-world latency in specific EHRs). We name what we do not know.
03 / Transparency
What we do not claim
- 01We do not claim "hands-on testing" of every tool in our index. When a tool review includes first-hand observations from an MD on our editorial team, the review is marked with that note.
- 02We do not claim our scoring is objective. Weights are editorial judgments published openly. Reasonable clinicians can disagree.
- 03We do not claim our reviews replace formal evaluation by your IT, compliance, or legal teams. Use this site to narrow a shortlist, not to make a final purchasing decision.
- 04We do not claim AI-tool reviews are medical advice. Reviews are about commercial software, not clinical guidance.
- 05We do not claim independence from commercial relationships. Affiliate links exist and we disclose them. We claim that our rankings are not for sale.
04 / Affiliate disclosure
Where money may change hands
Some outbound links to AI-tool vendors are affiliate links. If you sign up through one of these links, we may receive a referral commission. The price you pay is identical. The following commercial relationships exist:
- Direct affiliate or referral agreements with selected vendors. These links carry a sponsored badge.
- Sponsorship slots on category landing pages (clearly marked, category-exclusive maximum 1 per quarter).
- Newsletter sponsorships in our forthcoming clinician newsletter (subject to the same editorial firewall).
Editorial rankings are not affected by these commercial relationships. We decline sponsorships from tools that fail our evaluation criteria. If you spot a vendor relationship we have not disclosed, please email editorial@medaiverdict.com.
05 / Corrections
Corrections policy
Pricing, integration, and certification data change rapidly. If you are a vendor, clinician, or reader and you spot an inaccuracy, we correct it within seven business days of verification. Corrections are logged at the bottom of each affected review with the original wording struck through.
Material errors that affect a tool’s ranking position trigger a re-publication notice, not a silent edit.
07 / Score rubric
How a tool’s score is computed
Every tool on this site carries a 0–100 score computed deterministically from the public rubric below. No editorial opinion goes into the number. The same inputs produce the same score every time. If a tool ranks high, the math says so; if it ranks low, the math says that too.
The score replaces the older “editorial pick” language we used to publish. Readers should be able to verify rankings themselves, not take our word for them.
The 11 dimensions (100 base points)
Each dimension is scored 0–1 from the underlying data and multiplied by its base point weight. Total possible is 100 before per-silo weighting.
| Dimension | Base pts | Source |
|---|
| FDA clearance (510k, De Novo, PMA) | 12 | Tool certifications field |
| HIPAA + SOC2 + BAA attestation | 10 | Tool certifications field |
| EHR integrations (count) | 14 | Tool EHR list |
| Top-3 EHRs (Epic, Oracle Cerner, Athena) | 8 | Tool EHR list |
| Bidirectional / write-back integration | 4 | Tool features |
| Peer-reviewed papers | 14 | PubMed E-utilities ingest |
| RCT / meta-analysis / systematic review | 6 | PubMed study-type tagging |
| Funding & adoption signal | 12 | market_relevance proxy |
| Clinician sentiment (Reddit) | 9 | Reddit aggregate sentiment |
| Pricing transparency | 5 | Tool pricing tiers |
| Years in market | 6 | Tool founded_year |
Per-silo weighting
Different categories care about different dimensions. An AI medical scribe lives or dies by EHR integration depth; an AI clinical decision support tool lives or dies by FDA clearance and peer-reviewed evidence; an AI USMLE QBank cares about neither. We apply per-silo multipliers (typically 0.1× to 1.5×) and re-normalise so every silo still produces a 0–100 score.
- AI Scribe: EHR count ×1.5, Top-3 EHRs ×1.5, HIPAA ×1.3 / peer-reviewed ×0.5
- AI CDS: FDA ×1.5, peer-reviewed ×1.5, RCT ×1.5 / pricing ×0.5
- AI Radiology: FDA ×1.5, peer-reviewed ×1.3, RCT ×1.3
- AI Mental Health: HIPAA ×1.3, sentiment ×1.5 / EHR ×0.5
- AI Pathology: FDA ×1.5, peer-reviewed ×1.5, RCT ×1.3
- AI Surgical: FDA ×1.5, peer-reviewed ×1.5, RCT ×1.3
- AI Patient Triage: FDA ×1.3, HIPAA ×1.3, sentiment ×1.2
- AI Billing & Coding: EHR ×1.3, pricing ×1.3 / FDA ×0.3, peer-reviewed ×0.5
- AI Drug Info: peer-reviewed ×1.5, funding ×1.3
- AI Medical Education: sentiment ×1.5, pricing ×1.3, years in market ×1.3 / FDA ×0.1, EHR ×0.1
- AI Population Health: funding ×1.5, EHR ×1.3, pricing ×1.2
- AI Research: peer-reviewed ×1.5, pricing ×1.3 / FDA ×0.5, EHR ×0.3
Tier thresholds
Thresholds are calibrated to the actual distribution across our 249-tool catalog. The healthcare-AI market has uneven public data coverage; many enterprise tools score low not because they are bad, but because there is not enough independent evidence in PubMed or Reddit to score them confidently. The tier name reflects what we can defend, not the vendor’s marketing.
| Tier | Score range | Meaning |
|---|
| Top-ranked | 60+ | Strong evidence across regulatory, integration, and clinical-impact dimensions for its silo. Editorial confidence: high. |
| Solid choice | 45–59 | Real evidence in most dimensions; some gaps but defensible recommendation for the fitting persona. |
| Competitive | 30–44 | Present in the category with credible signals; worth comparing against top-ranked alternatives. |
| Niche fit | 20–29 | Partial data; may be the right answer for a specific use-case but the evidence base is thin. |
| Tracked | <20 | Indexed in our catalog but we lack independent sources for a confident review. The vendor description is what we have. |
What the score is not
- Not influenced by affiliate relationships. The score is computed before any affiliate link is added. A tool with a strong score may have no affiliate program; a tool with an affiliate link may score “Tracked”. The two systems do not talk to each other.
- Not a clinical recommendation. A high score means the public evidence base looks solid for a generic clinician decision. Your specific environment (EHR vendor, specialty, deployment scale, patient population) changes the right answer.
- Not a static verdict. We re-compute scores when source data updates (new PubMed papers, new Reddit sentiment, new vendor certifications). Each tool page shows the last computation date.
- Not a substitute for hands-on testing. Some tools carry a separate “MD-verified hands-on” signal (planned roll-out 2026). That layer is editorial and complements the data-driven score, it does not replace it.
06 / Contact
Reach the editorial team
- Editorial
- editorial@medaiverdict.com
- Corrections
- corrections@medaiverdict.com
- Vendor relations
- partnerships@medaiverdict.com
- Press
- press@medaiverdict.com