Clinical evidence and questions
OpenEvidence
Medical search and clinical decision-support AI that synthesizes peer-reviewed evidence for clinicians.
Last updated: June 5, 2026
Back to directoryGlass Health medical AI product profile
AI-powered clinical decision-support and ambient-scribing platform that drafts differentials, plans, notes, and cited clinical answers for clinician review.
Clinical evidence and questions
Best fit
Clinicians or clinical software teams that want evidence-backed reasoning support embedded into visits, documentation, EHR context, or product workflows with explicit human review.
- Primary use case
- AI-powered clinical decision support, ambient scribing, real-time encounter insights, differential diagnosis drafts, assessment and plan drafts, evidence-backed answers, documentation generation, and developer API workflows
- Audience
- Clinicians, care teams, clinical workflow software teams, telehealth platforms, EHR builders, and governance teams evaluating AI clinical decision support
- Risk level
- High
- Pricing signal
- Individual, clinic, and developer/API terms may change; verify current ambient scribing, CDS, EHR integration, API, and enterprise pricing directly.
- Official sources
- 7 official sources
Compare within workflow: Clinical evidence and questions · comparison shortlist · source index
Regulatory, privacy, evidence, and workflow lens
Product-specific review. These product-specific signals summarize what the cited sources imply before treating Glass Health as safe for a local clinical, operational, or research workflow.
| Regulatory / FDA | Treat differential diagnosis, treatment planning, triage, ambient insights, and encounter-specific recommendations as high-risk CDS; map every deployed function against FDA non-device CDS criteria, intended use, explainability, clinician independence, and any local medical-device obligations. |
|---|---|
| Privacy | Review individual versus clinic or enterprise terms, BAA availability, prompts and outputs, audio and transcripts, EHR context, API payloads, payment data, support access, retention, de-identified or aggregated data, and whether service-improvement or model-training rights fit the deployment. |
| Evidence | Require citation review, guideline currency checks, local chart audits, ambient transcript accuracy testing, drug-information verification, subgroup review, and clinician override tracking before using Glass outputs in patient-specific decisions. |
| Workflow | Best governed as clinician-reviewed reasoning and documentation support with clear rules for when outputs may enter notes, orders, referrals, discharge instructions, patient handouts, or software workflows. |
Where Glass Health fits
Glass describes an AI-powered ambient scribing and clinical decision-support platform that listens during encounters, surfaces evolving differential diagnoses, suggests history questions and next steps, generates notes and plans, answers clinical reference questions with citations, supports EHR integration, and exposes a developer API for clinical queries, triage, record summarization, diagnostic support, treatment planning, documentation, and ambient scribing; its privacy policy covers prompts and outputs, and its clinician terms should be reviewed before entering patient context or relying on generated recommendations.
Not for: Autonomous diagnosis, unsupervised triage or treatment selection, emergency advice, entering PHI without appropriate terms, or replacing local clinical protocols and professional judgment.
What to verify before using Glass Health
- Which workflow is in scope: web CDS, ambient scribing, EHR integration, developer API, triage, record summarization, diagnostic support, treatment planning, documentation, or patient-facing handouts.
- Whether each software function satisfies FDA non-device CDS criteria or needs regulated-device review under the current FDA Clinical Decision Support Software guidance.
- How prompts, outputs, audio, transcripts, patient context, EHR data, API payloads, citations, logs, support access, retention, de-identified or aggregated data, and model-training uses are handled.
- Local safety testing for missed diagnoses, overconfident plans, hallucinated citations, unsupported drug guidance, ambient transcript errors, wrong-patient context, handoff failures, and clinician over-reliance.
Source links
Use these links to confirm current claims, terms, regulatory status, pricing, and deployment requirements.
- glass.health
- glass.health / features
- glass.health / ambient-cds
- glass.health / developer-api
- glass.health / privacy
- glass.health / tos
- www.fda.gov / regulatory-information / search-fda-guidance-documents
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