Clinical systems · Clinical decision support AI
Glass Health
Glass Health makes clinical AI software that drafts differential diagnoses and assessment-and-plan notes with cited evidence and writes visit notes from recorded encounters, sold on published monthly plans.
Strong fit
- Primary care and hospital clinicians who want help listing possible diagnoses and drafting a plan
- Small practices that want AI visit notes and clinical decision support at a published monthly price
- Software teams that want to build its clinical reasoning into their own products through an API
Weak fit
- Health systems that want published outcome studies before adopting a clinical AI tool
- Organizations that want a large vendor with a long history of support
- Clinicians who only want a dictation tool
Bottom line
Glass Health is worth considering for clinicians and small practices that want AI help with clinical reasoning and an AI scribe in one tool. It suits users who want cited sources and published monthly prices. Buyers who need strong public evidence of results should know it is thin.
Score breakdown
Weights: Outcomes 35% · Product 30% · Implementation 20% · Pricing clarity 15%.
Glass Health, based in San Francisco, California, makes AI software that drafts a list of possible diagnoses and a treatment plan from a patient summary. It also writes visit notes from recorded conversations.
Dereck Paul, a physician who trained at the UCSF School of Medicine and Brigham and Women's Hospital, founded it in 2021 with Graham Ramsey.
A clinician types a patient summary or records a visit. Glass then drafts a ranked list of possible diagnoses and an assessment and plan, citing the evidence it used so the clinician can see why each item is there.
A team of physicians writes and checks the clinical content behind its answers. Monthly prices for individual clinicians are published.
The company has grown steadily since then. Glass went through Y Combinator in early 2023 and raised a $5 million seed round led by Initialized Capital that September.
It added a developer API in February 2025 and connects to athenaOne, eClinicalWorks, Epic, and Elation. In 2026 it launched Glass for Patients, a version for patients themselves.
It publishes few results from customers.
Other tools serve nearby needs. OpenEvidence suits doctors who mainly want free, cited answers to clinical questions, and Doximity suits a health system that wants physician messaging and telehealth calling with a clinical AI tool attached.
Competitor landscape
| Vendor | Overall | Ease of implementation |
|---|---|---|
| OpenEvidence | 8.4 | 8.8 |
| Doximity | 6.9 | 7.1 |
| Glass Health | 6.8 | 6.9 |
Pricing
| Item | Detail |
|---|---|
| Model | Monthly plans for clinicians with posted prices. The developer API and health system setups are quoted separately. |
| What usually drives cost | The plan (Lite, Starter, Pro, or Max), usage limits for note writing and diagnosis help, and EHR connection, which comes with the Max plan. |
| What to ask before you buy | The yearly price for a group, how many visits per month the usage limits allow, and whether patient data is used to train its AI. |
| Published pricing | Prices are posted on glass.health. |
Published plans are Lite at $0, Starter at $20, Pro at $90, and Max at $200 per month. Lite and Starter carry sponsored content; Pro and Max do not.
Prerequisites for purchase
| Need | Why it matters |
|---|---|
| What you need to get Glass Health to function | |
| Physician lead for AI tools | Someone should review its suggestions. |
| Policy on AI in clinical decisions | Clinicians need clear limits. |
| EHR integration plan | The Max plan connects to the patient record. |
| Patient consent process for recording | Note writing records the visit. |
| A way to report wrong suggestions | Errors should reach the vendor. |
| What will maximize your value | |
| Start with complex visits | Diagnosis help adds the most there. |
| Check citations on unfamiliar cases | The sources show its reasoning. |
| Compare note time before and after | This shows whether AI notes help. |
| Deal-breakers | |
| You need published studies of results | |
| No physician will oversee the AI | |
| You only want dictation | |
Value creation time frame
| # | Stage | Typical range |
|---|---|---|
| 1 | Clinician sign-up | Same day |
| 2 | EHR connection on Max plan | Days to weeks |
| 3 | Rollout to the group, with guidance | 1-2 months |
Leadership
Methodology
| Weight | Factor | What it measures |
|---|---|---|
| 35% | Customer outcomes | Whether buyers get measurable operational or clinical-workflow results after go-live |
| 30% | Product | Capability depth, reliability, and fit for the job the category buys |
| 20% | Implementation | How hard it is to stand up, integrate, train, and stabilize |
| 15% | Pricing clarity | Whether a buyer can model total cost without a mystery quote |
| Label | Meaning |
|---|---|
| Highly recommend | Strong outcomes and product with manageable caveats |
| Recommend | Solid fit for the right buyer; know the tradeoffs |
| Conditional | Only with a specific use case or heavy caveats |
| Not recommended | Avoid for most buyers in this category |
Read our full methodology for how we weight scores and assign recommend labels.