What You Can Build with the Glass Health Clinical AI API

If you are building clinical decision support, we give your product a clinical reasoning foundation that searches current guidelines and medical literature, reasons across the patient context you provide, and returns answers with inline citations clinicians can check. Teams choose Glass when they want evidence-based clinical Q&A, differential diagnosis, treatment planning, and documentation from one API, with HIPAA-supported deployment.

This page walks through eight product experiences you can build on the Glass Developer API, who benefits from each, and how to decide where to start. Ready to try it? Explore the Glass Developer API or start in Glass API settings.

Why Glass Health Is a Strong Foundation for Clinical Decision Support

A clinical decision support API has one job that matters above everything else: give clinicians and patients answers grounded in evidence, reasoned over the actual case, and easy to verify. Glass Health is built around that job. Glass reads the input your product supplies and deeply searches current clinical guidelines and medical literature, so responses are grounded in current evidence rather than model memory alone.

The second ingredient is reasoning. Glass approaches a case the way a master clinician would, working across history, exam, lab values, imaging, medications, and any other patient context you pass in. Glass is particularly strong at analyzing large amounts of unstructured patient data, which is exactly what real clinical workflows produce.

The third ingredient is trust. Glass supports cited answers so a clinician can examine the evidence behind a recommendation. That makes clinical evidence part of the experience your product offers, giving users a basis for assessing an answer’s relevance to the case in front of them.

You could assemble these capabilities yourself on a general model API, adding clinical evidence, citations, clinical evaluation, and support for regulated use. Many teams find the more useful decision is to choose a clinical offering that already delivers them, then spend their engineering time on the product experience their users actually see. If you are comparing options, our guide to choosing the best healthcare AI API covers the criteria in more depth.

Eight Clinical Experiences You Can Build with Glass Health

The scenarios below are possibilities, described the way a product team would scope them. Each shows who uses the experience, the clinical work it supports, what Glass contributes, and what the user can do with a cited answer inside your product.

1. An Evidence Assistant Inside the Clinical Workflow

Clinicians ask evidence questions all day: which anticoagulant dose fits a patient with reduced renal function, whether a new guideline changed screening intervals, how to manage a drug interaction. Today those questions often mean leaving the chart, opening a reference site, and reading for several minutes.

A team can build an evidence assistant that lives inside a clinical application, a telehealth console, or a pharmacy workflow. The clinician types a question in natural language, optionally with the relevant patient details already in your product. Glass supports evidence-based clinical Q&A grounded in the latest guidelines and literature and returns an answer with inline citations tied to specific claims.

The clinician can read the synthesized answer, expand the citations to see the source guideline or study, and decide with the evidence in view. For a health system, the opportunity is to bring evidence closer to the moment of a clinical decision. For a digital health startup, it turns a static content library into a responsive clinical reference that reflects current literature.

This is often the fastest first product because the interaction is simple and the value is visible immediately. Clinicians know when an answer is well cited.

2. Case Review and Differential Diagnosis Support

Diagnostic reasoning is where clinicians most want a thoughtful second opinion. A team can build a case review experience where a clinician submits a presentation: chief complaint, history, exam findings, labs, imaging reports, and medication list. Glass reasons across all of that context and returns a differential that reflects the whole case rather than a keyword match on the chief complaint.

Who benefits? Emergency and urgent care teams working under time pressure. Primary care clinicians facing an undifferentiated presentation. Hospitalists reviewing an admission. Medical education platforms teaching diagnostic reasoning with realistic cases.

The experience can show the leading considerations, the features supporting or arguing against each, and what additional information would sharpen the picture. Because Glass is strong with large, unstructured inputs, the clinician can paste a full narrative rather than restructuring it into a form.

The clinician uses the output as a second perspective: is there a dangerous possibility to consider, an alternative explanation for a finding, or a question worth investigating further? Glass contributes clinical reasoning and current evidence while the clinician retains the decision.

3. Treatment Plan and Assessment Support

Once a diagnosis is in focus, the next question is what to do about it. For treatment planning, Glass generally produces an assessment and plan that includes an overall analysis of the patient, problem-based assessments for the major active issues, diagnostic next steps, and treatment or management next steps.

A team can build this into an inpatient rounding tool, a specialty clinic application, or a chronic disease management platform. The clinician provides the current clinical picture inside your product and receives a structured plan organized by problem, with evidence cited for the recommendations.

Consider a patient with heart failure, diabetes, and chronic kidney disease. The plan can address each active problem, note where recommendations interact, and cite the guidelines that inform each choice. A clinician can edit the plan, accept parts of it, and carry the evidence into the note.

This experience is valuable for organizations that care about consistency. Different clinicians approach the same problem differently, and a cited, problem-based draft gives everyone a shared evidence baseline to work from. It also helps newer clinicians see how experienced reasoning is structured. Recommendations that affect care should be reviewed by a licensed clinician before they are acted on, which is how these tools are designed to be used.

4. Longitudinal Patient Summaries

Some of the hardest clinical work is understanding a patient with years of history in a few minutes. Notes accumulate, problem lists drift, and the story of the patient gets buried under documentation volume.

A team can build a longitudinal summary experience where your product supplies the relevant record content and Glass produces a concise clinical synthesis. Because Glass is particularly strong at analyzing large amounts of unstructured data, it can work from progress notes, discharge summaries, and consult reports as written, then surface the trajectory: how the major problems evolved, what has been tried, what changed recently, and what remains unresolved.

Who uses this? A hospitalist admitting a patient with a long outpatient history. A specialist seeing a new referral. A care manager preparing for an outreach call. A clinician covering for a colleague who wants the story in two minutes instead of twenty.

The user gets a synthesis they can verify against the record, with the reasoning visible. For organizations, this is a direct answer to the cognitive load that drives clinician burnout. For product teams, it is a compelling entry point because the value is obvious the first time someone opens a complex chart and sees it summarized coherently.

5. Documentation That Carries Clinical Reasoning

Glass supports clinical documentation including H&P, HPI, clinic notes, progress notes, discharge summaries, prior authorization letters, handoff notes, and patient handouts. That breadth gives teams several ways to address the writing burden surrounding clinical care.

A team can pair this with the Scribing API, which transcribes clinical encounters from audio recordings and optionally generates structured clinical notes such as SOAP notes, H&Ps, and visit summaries directly from the recording. That gives you a path from conversation to draft note inside one product.

Consider prior authorization letters. A clinician who needs to justify a medication can generate a draft that cites the clinical rationale and relevant evidence, then review and send. Consider handoff notes at shift change, where a structured summary with active issues and pending items can reduce the risk of dropped tasks.

Ambulatory clinics, hospital medicine groups, and telehealth providers all benefit. The clinician reviews and signs the note as always; Glass contributes a well-structured, clinically coherent draft that reflects the encounter and the evidence behind the plan.

6. Plain-Language Patient Education

Patients leave visits with instructions they do not fully understand, and that gap drives readmissions, medication errors, and anxious phone calls. Glass generates patient-facing documentation in plain language, including discharge instructions, care plan summaries, medication guides, condition education, and return precaution instructions.

A team can build patient education into a discharge workflow, a patient portal, or a post-visit messaging experience. The clinician confirms the plan inside your product, and Glass produces materials written for the patient rather than for another clinician: what the condition is, what the medications do, what to watch for, and when to seek care.

Who benefits? Hospitals working to improve transitions of care. Digital health companies serving patients with chronic conditions. Pediatric practices whose caregivers need clear guidance. Health plans supporting members after a procedure.

Because the content is grounded in the same evidence base as the clinical reasoning, the patient guidance and the clinician plan stay consistent. The clinician can review the material before it reaches the patient, adjust the reading level or emphasis, and send it with confidence that it reflects current guidance. Patients get something they can act on.

7. Care Navigation and Evidence-Based Triage

The first clinical decision often happens before a clinician is involved: where should this patient go, and how urgently? Glass supports evidence-based triage by evaluating patient-reported symptoms, vital signs, and intake data against clinical criteria, identifying high-risk features, and returning structured triage output.

A team can build this into a patient-facing symptom intake experience, a nurse triage tool, or escalation logic inside a virtual care platform. A patient describes symptoms and supplies basic vitals; Glass identifies high-risk features and returns structured output your product can use to route the patient to the appropriate level of care, flag cases for immediate nurse review, or suggest next steps.

Nurse triage lines, virtual care companies and health systems can build around the same need: making relevant clinical information available when deciding how a patient should enter care.

For a product team, the opportunity is a better experience at the beginning of care: patients can describe their concern, nurses can review clinically relevant information, and the care organization can apply its protocols. Glass brings clinical reasoning to that experience, with clinical oversight of decisions that affect care.

8. Clinical Reasoning as a Component for Other Agents

Healthcare agent products can help with many kinds of work. Glass brings clinical reasoning grounded in evidence to experiences that need to understand a patient’s context or answer a medical question.

A team can build a care coordination assistant that prepares a clinical summary before outreach, a documentation assistant that drafts the clinical rationale for a letter, or an evidence assistant that helps a clinician explore a complex case. Glass provides the clinical expertise those experiences need.

Who benefits? Health tech platforms building agentic products. Enterprise teams adding clinical intelligence to existing automation. Developers who want dependable clinical output without training their own models.

Glass gives agent builders a clinical specialist for work that involves medical questions, patient context and evidence-based synthesis. The team can focus on the user experience and the broader service it wants to offer. See more possibilities in our guide to clinical AI for agent products.

Glass Health Use Cases at a Glance

Deciding Which Problem to Tackle First

With eight viable directions, the practical question is sequencing. A few criteria help.

Start where the clinical context already lives in your product. If your users already enter or view patient information, longitudinal summaries and treatment plan support build on what you have. If your product is closer to the front door, triage and patient education fit naturally. Glass processes the clinical context your product supplies, so the richer the context you can offer, the more the reasoning shines.

Start where verification is easy. Evidence assistants and documentation drafts are reviewed by clinicians as part of normal work, which means adoption does not require a new review process. Citations make that review quick.

Start where the pain is loud. Clinicians will tell you what costs them time: chart review, prior authorizations, unanswered evidence questions, and discharge paperwork are perennial answers. Picking a problem your users already complain about gives you early champions.

Related experiences can grow together. An evidence assistant can expand into case review, and documentation can lead naturally to patient education. Glass supports those related clinical capabilities, giving a team room to develop a broader product over time.

Clinical Performance You Can Evaluate

Buyers rightly ask how a clinical AI system performs before putting it near patient care. Glass is evaluated by clinicians and benchmarked on clinical use cases using a 900-question clinical accuracy benchmark suite covering medical knowledge and reasoning, diagnostic reasoning, clinical note generation, and hallucination detection. On that clinical benchmark suite, Glass 5.5, the current recommended model, outperforms the leading frontier foundation models.

Independent evaluation adds a second view. Glass has been evaluated on the Medical AI Superintelligence Test run by the ARISE AI Research Network, which measures clinical reasoning, safety, and diagnostic performance; the MAST technical results are published for review. The MAST methodology supports comparing clinical systems while noting that any product still needs assessment in its intended use, which is good advice for every buyer.

What these evaluations mean for you is straightforward. Clinical reasoning scores indicate how well a system handles the diagnostic and management questions your users will ask. Safety evaluations indicate whether it avoids recommendations that could cause harm. Hallucination detection indicates how well it stays anchored to evidence. Together they tell you Glass was built and measured for the work you are building on it. Our overview of clinical AI API benchmarks discusses how to read these results when comparing options.

We recommend that every team evaluate Glass on cases representative of their own product, using synthetic or de-identified patient data during development and evaluation. The performance evidence gives you a strong reason to start; your own evaluation confirms fit.

HIPAA-Supported Deployment

Glass is end-to-end HIPAA compliant, and organizations using the Glass Developer API in HIPAA-regulated workflows may obtain a Business Associate Agreement for API use through Glass API settings. This gives a team a defined way to begin the clinical technology relationship as it develops its product.

The BAA covers Glass's role in processing data through the API; your finished product carries its own compliance responsibilities, as with any component you build on. For health systems, this means a clinical AI capability that fits an existing privacy program. For startups, it means the compliance foundation is in place before your first customer asks about it. More detail is available in our guide to choosing a HIPAA-compliant AI API.

Commercial Basics for Planning

Glass Developer API access is offered as a subscription with a minimum of $250 per month, with usage above that floor charged separately. That structure suits teams running an evaluation and then scaling into production without renegotiating access as adoption grows. Pricing details are available in API settings and in the Glass API documentation.

Frequently Asked Questions

What is a clinical decision support API and how does Glass Health fit?

A clinical decision support API lets your product request clinical reasoning, such as an evidence answer, a differential, or a treatment plan, and present the result to users. Glass Health provides that reasoning grounded in current guidelines and literature, with inline citations, so your product can support clinicians and patients with evidence they can verify.

Do Glass responses include citations?

Glass supports clinical answers with citations so clinicians can examine the evidence behind the information. Citations help make a recommendation reviewable and give your product a way to keep supporting evidence close to the answer.

Why choose Glass instead of building on a general model API?

A general model API gives you a capable starting point, and you then assemble medical retrieval, citation handling, clinical evaluation, and a compliance posture around it. Glass delivers those as a clinical offering, evaluated by clinicians on clinical tasks and available under a BAA. Teams choose Glass when they would rather invest in their product experience than in rebuilding clinical infrastructure.

Does Glass connect to our EHR or take clinical actions?

Glass processes the clinical context your product supplies and returns reasoning, documentation, or structured output. Your product decides how that information is presented and what happens next. Glass does not independently retrieve charts or perform clinical actions, which keeps your team in control of the workflow.

Who can build on the Glass Developer API?

Digital health startups adding clinical intelligence to patient or clinician products, health systems building internal tools, telehealth and virtual care companies, medical education platforms, and teams building agentic workflows that need a clinical reasoning component.

How should we evaluate Glass for our use case?

Begin with the published clinical benchmark results and the independent MAST evaluation, then test Glass on synthetic or de-identified cases representative of your product. Involve clinicians in reviewing outputs for the specific experience you plan to build, since performance in your intended use is what ultimately matters.

How do we get started?

Visit the Glass Developer API page to review capabilities, then go to Glass API settings to create access, accept the BAA if your workflow is HIPAA-regulated, and begin evaluating with de-identified data.

Build Your Clinical Experience on Glass Health

Whether your first product is an evidence assistant, a case review tool, a documentation workflow, or a clinical component for a larger agent, we give you evidence-grounded reasoning, citations clinicians can check, clinical performance you can evaluate, and HIPAA-supported deployment from day one. Explore the Glass Developer API to see what you can build, or start in Glass API settings and get your API key today.