Regard vs Glass AI
Detailed side-by-side comparison to help you choose the right tool
Regard
🟢No CodeDocument Management
AI clinical insights platform that reviews 100% of patient charts to support hospital documentation, diagnosis capture, and revenue integrity workflows.
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Starting Price
Custom enterprise pricing; exact starting price not publicGlass AI
🟡Low CodeAI Development Assistants
Clinical decision support AI that assists healthcare professionals with differential diagnosis, medical knowledge search, and evidence-based clinical reasoning.
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Starting Price
FreemiumFeature Comparison
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💡 Our Take
Choose Glass AI if you want a clinician-driven workflow where you author the problem representation and Glass returns differentials and notes you control. Choose Regard AI if you are a hospital IT team looking for an EHR-integrated platform that proactively scans patient charts to surface missed diagnoses across an inpatient census — Regard is institution-deployed; Glass is clinician-deployed.
Regard - Pros & Cons
Pros
- ✓Reviews the entire medical record for every patient rather than relying only on encounter notes or clinician prompts.
- ✓Generates draft clinical documentation before the physician sees the patient.
- ✓Connects clinical quality and revenue integrity by recommending diagnoses and supporting more complete documentation.
- ✓Public impact materials cite concrete customer outcomes, including Sentara Health results.
- ✓Designed for existing hospital workflows rather than as a separate consumer health application.
- ✓Addresses both clinician burden and administrative documentation needs, which can strengthen the enterprise ROI case.
Cons
- ✗Pricing is not public, so hospitals must go through a sales and demo process to estimate cost.
- ✗The website does not provide detailed implementation requirements, supported EHR vendors, or deployment timelines in the visible content.
- ✗Regard is specialized for hospital clinical documentation and chart review, not general-purpose medical AI or consumer triage.
- ✗Clinical teams still need to review and accept recommendations; the product does not replace clinician judgment.
- ✗Public materials emphasize successful case studies and impact metrics, but institution-specific ROI depends on implementation, adoption, payer mix, and governance.
Glass AI - Pros & Cons
Pros
- ✓Bundles ambient scribing and clinical decision support in one subscription, replacing what would otherwise require two separate tools (typically $150-$400/month combined)
- ✓Pro tier at $90/month is competitive against standalone AI scribe products and includes unlimited usage of both scribing and decision support
- ✓Available on iOS, Android, and web, letting clinicians capture encounters on a phone in the exam room and review notes from a desktop later
- ✓Evidence-based diagnostic suggestions are grounded in current medical literature, helping clinicians consider comprehensive differentials for atypical or complex presentations
- ✓Educational value for medical residents and students — the structured problem-representation-to-differential workflow mirrors how clinical reasoning is formally taught
- ✓Max tier ($200/month) adds direct EHR integration so AI-generated notes flow into the chart without copy-paste
Cons
- ✗Restricted to licensed healthcare professionals — patients and non-clinical users cannot access the platform for self-diagnosis or general health questions
- ✗Specialty depth varies: well-suited to general internal medicine, family medicine, and emergency medicine, but may underperform on rare diseases or narrow subspecialties (e.g., advanced oncology subtypes, rare genetic disorders)
- ✗EHR integration is gated behind the $200/month Max tier — Pro users must still copy notes manually into their charting system
- ✗Output quality is bounded by the quality of clinician input: incomplete histories or vague problem representations produce weaker differentials
- ✗HIPAA and institutional compliance review is typically required before clinics can deploy ambient scribing on real patient encounters, adding rollout friction
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