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Use case 1 · Cardiology — predictive deterioration
ML predicts cardiac deterioration 48 hours earlier from RPM streams plus vitals. 22% readmission reduction in cardiac cohorts. Tiered escalation to clinical team.
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AI Engineering for Specialty Clinics
AI features built for specialty workflows — cardiology, oncology, behavioral health, orthopedics, dermatology, ophthalmology, gastroenterology. Specialty EHR integration. Disease-specific AI. Fixed-price productized tiers.
$45K Discovery · $95K Production-Ready · $145K Pilot-Ready
📌 Definition
Specialty Clinics AI is the engineering of AI features purpose-built for specialty practice workflows — cardiology, oncology, behavioral health, orthopedics, dermatology, ophthalmology, gastroenterology, endocrinology, and other clinical specialties. Modern 2026 specialty AI deployments require disease-specific data models, specialty EHR integration (athenaPractice, eClinicalWorks, OncoEMR, NextGen, ModMed, Epic Beaker), specialty-appropriate AI features (cardiac RPM, oncology decision support, behavioral health screening, lesion classification), and BAA-eligible inference paths. Productized fixed-price tiers: $45K (4-week Discovery), $95K (8-week Production-Ready), $145K (12-week Pilot-Ready).
What we build for specialty clinics
Cardiology AI
Cardiac RPM with predictive event detection, AI ECG interpretation assist, post-MI care pathway automation, heart failure exacerbation prediction, AFib detection from wearable data.
Oncology AI
Treatment guideline matching (NCCN, ASCO), trial eligibility identification per patient, tumor-board prep automation, oral oncolytic adherence AI, symptom management AI during chemotherapy.
Behavioral health AI
PHQ-9/GAD-7 augmented with conversational AI screening, risk stratification and care routing, AI session note drafting (clinician-reviewed), medication adherence AI for psychiatric medications.
Orthopedics AI
Pre-surgical risk stratification, post-op rehab adherence tracking, AI-paced rehab protocols, outcomes write-back to surgeon, AI imaging triage for fracture and joint imaging.
Dermatology AI
Lesion triage and dermoscopy-image classification, tele-dermatology workflows, in-clinic AI imaging triage, AI patient intake for tele-derm. Requires FDA SaMD pathway for diagnostic features.
Ophthalmology AI
Diabetic retinopathy and glaucoma screening from retinal images, AI-augmented OCT interpretation, primary-care-deployable screening workflows. Requires FDA SaMD pathway.
Gastroenterology AI
AI colonoscopy quality metrics, polyp detection assist, IBD flare prediction, GI symptom triage chatbot, hepatology decision support.
Endocrinology AI
CGM-stream plus insulin AI assist, diabetic management AI, thyroid management AI, obesity medication management workflows.
Specialty-specific patient engagement
Disease-specific patient education, symptom tracking, AI symptom triage tuned to specialty, post-visit AI check-ins, multilingual chat.
Specialty Clinics AI pricing · 2026
What’s included in every specialty clinic engagement
- HIPAA-compliant architecture from day 1 — encryption at rest and in transit, role-based access, audit logging
- BAA-covered AI providers (OpenAI, Anthropic, AWS Bedrock, Google)
- Specialty EHR integration via FHIR R4 plus HL7 v2 (Epic, Cerner-Oracle, ModMed, eClinicalWorks, athenaPractice, OncoEMR, NextGen)
- SMART on FHIR launch app option for in-EHR deployment
- Specialty-tuned evaluation harness on the clinic’s own patient data
- Override-and-audit UX on every clinical AI feature
- Specialty-appropriate FDA SaMD pathway scoping
- Native iOS and Android plus responsive web where patient-facing
- Multilingual support (30+ languages via BAA-eligible LLM providers)
- Pre-signed BAA templates with major cloud and AI providers
- SOC 2 Type II and HITRUST audit-ready documentation
Common specialty AI use cases
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Use case 2 · Oncology — tumor-board prep automation
AI drafts tumor-board briefing materials from patient chart, recent imaging, recent labs, and current NCCN/ASCO guidelines. Cuts prep time 60–70%.
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Use case 3 · Behavioral health — AI screening at intake
PHQ-9/GAD-7 augmented with conversational AI screening. Risk stratification and care routing. Reduces wait time to appropriate level of care.
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Use case 4 · Dermatology — tele-derm triage
AI lesion classification at patient intake. Triages urgency, routes to appropriate clinician, captures dermoscopy images and patient history. FDA SaMD pathway required for diagnostic features.
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Use case 5 · Ophthalmology — primary-care DR/glaucoma screening
Diabetic retinopathy and glaucoma screening deployable in primary-care settings, expanding the addressable patient population. FDA SaMD pathway required.
Ship specialty AI that fits your specialty workflow
Free 30-min architecture call. We’ll scope your AI use case, specialty EHR integration path, and the right tier for your practice size.
FAQs
FAQ
Epic (including Epic Beaker for lab and Epic Radiant for radiology), Cerner-Oracle (including PowerChart specialty modules), Athena (athenaPractice, athenaClinicals), Allscripts, eClinicalWorks, NextGen, ModMed (ophthalmology, dermatology, gastroenterology, orthopedics, OB-GYN), OncoEMR (oncology), Greenway Intergy, AdvancedMD, Practice Fusion, and Meditech. Integration via FHIR R4, HL7 v2 via Mirth Connect, or SMART on FHIR launch where supported.
Some features do, most don’t. AI features that diagnose, screen, or directly influence treatment decisions for an individual patient typically require FDA SaMD pathway — lesion classification, diabetic retinopathy screening, polyp detection, autonomous diagnosis. Ambient documentation, patient engagement, decision support with reviewer-in-the-loop, and operations AI usually don’t. We scope FDA pathway on every engagement.
Yes. We integrate with PACS systems (GE Centricity, Philips IntelliSpace, Sectra, Carestream, Merge) via DICOM, and with RIS systems via HL7 ORM/ORU messages. Specialty imaging AI typically integrates at the PACS/RIS layer rather than the EHR layer.
The Discovery Sprint is sized for small practices ($45K, 4 weeks). Single-clinician specialty practices can deploy patient engagement AI, AI scheduling, and ambient documentation profitably. Larger AI features (oncology decision support, cardiac RPM AI) require larger patient volume to produce documented ROI — typically 5+ clinicians or 1,000+ active patients.
Yes. Pre-signed BAA templates with OpenAI, Anthropic, AWS Bedrock, and Google. Zero-data-retention configuration verified in writing. Audit logging on every model output. Override-and-audit UX on every clinical AI feature. Zero HIPAA findings on shipped software across 785+ healthcare implementations.
State-specific regulations for behavioral health (42 CFR Part 2 for substance use disorder, state-level telehealth restrictions, prescribing rules for controlled substances) are handled in every behavioral health engagement. We scope state-specific compliance per engagement.
4-week Discovery, 8-week Production-Ready, 12-week Pilot-Ready. Full production with specialty EHR integration adds 16–32 weeks. FDA SaMD pathway (for diagnostic AI features) adds 6–18 months depending on device class and clinical evidence requirements.