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Vertical AI platform for medicine
Specialty AI, built on the one layer every department shares.
Layer 2: support AI for skin, wounds, and the next specialty. Layer 1: common infrastructure that structures conversation, records, and review.
Purpose-built AI engines for each job in care, and a Clinical OS that keeps them on one patient state under one line of physician authority. It does not replace the EMR.
AetherHeal Global · Information and communication · Ulsan
Product lineup · Current status
Platform structure
Common infrastructure below, specialty AI above
New specialty AI enters the same review flow, where the physician still confirms and decides.
Layer 2 · Vertical specialty AISpecialty modules added only where clinical validation and reviewers exist
Clinical validation
Co-development
DermatoScan AI
Organizes imaging and device inputs for pigmented-lesion assessment. Its validation scope is expanding from an initial set of more than 50 cases.
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WoundScan AI
An R&D project for wound image quantification and progress documentation, with scope and review criteria designed with clinical advisors.
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Layer 1 · Horizontal clinical infrastructureConversation, documentation, review, and follow-up across outpatient care
Beta · field validation
Validation
Dockie-talkie
Interprets conversations between clinicians and international patients in real time, reducing language barriers in the consultation room and helping physicians care for patients across more languages.
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Clinical Copilot
A shared assistance layer that surfaces information and evidence for clinician review. Its reasoning screens and review loops are already in development and improve through clinician feedback and agent-assisted iteration.
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Layer 0 · Clinical OSThe operating base for every encounter: booking, reception, consult, billing, records
The foundation every specialty AI builds on. It threads booking, reception, consult, billing, and records into one flow, and interpretation, reasoning, and specialty AI share that record.
Agent Mode
Ask the AI about today's operations and the selected patient's context
Real-time operations board
Booking to discharge in one view
Want to see firsthand how AI assists physicians in real practice?
Contact usWhy this structure
The sequence builds the advantage
We lay the common infrastructure first, then add specialties we have validated ourselves, and open the same validation method to outside developers as a standard. The depth comes from that order; skip a step and you lose it.
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1. Lay the common infrastructure once
Interpretation (Dockie-talkie) and clinical reasoning (Clinical Copilot) are needed in every outpatient clinic, whatever the specialty. Build them once as shared infrastructure instead of rebuilding them per specialty, and every specialty AI above reuses the same base. A single-specialty product cannot produce that shared asset, and it is the floor the whole platform stands on.
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2. Add specialty AI we have validated ourselves
We validated DermatoScan AI in clinical practice and refined selection criteria spanning 11 lesion types and 11 laser and energy devices. What separates it from a desk demo is an initial body of cases tested in real care. One proven specialty sets the bar for the next—WoundScan AI—and for third-party developers.
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3. Open the validation method to outside developers as a standard
The selection criteria, review process, and physician-approval structure established with DermatoScan AI become the standard for third-party specialty AI. New specialties can join without rebuilding the infrastructure or trust model from scratch. The commercial onboarding model is still being finalized, but the clinical requirements are already defined.
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4. One trust rule runs through every layer: the physician decides
Whether the tool is shared infrastructure, specialty AI we validate ourselves, or a third-party integration, the physician makes the final decision. Shared infrastructure, real clinical validation sites, specialty reviewers, and one responsibility principle reinforce one another across every layer. A feature can be copied; the complete system is much harder to reproduce.
What compounds
Clinical loops outlast features
Features can be built anywhere. The clinical loop, where physicians, data, and review standards move together, keeps thickening over time.
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Clinical access
Products can be tested in real outpatient workflows instead of staying as demos.
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Specialty reviewers
Each specialty has clinicians who can define scope, risks, and review criteria.
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Shared infrastructure
Conversation, documentation, review, and follow-up become reusable assets across products.
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Responsibility boundary
AI assists the review process while final diagnosis and treatment decisions remain with clinicians.
Open ecosystem
Third-party AI, held to the same standard
Once the shared infrastructure is in place, new specialty AI can be built on top of it.
The verification structure established by DermatoScan AI becomes the standard for external developers.
Third-party AI onboarding will open in stages.
FAQ
Platform FAQ
Common questions about AetherHeal platform structure.
No. AetherHeal is a clinical workflow platform. Shared infrastructure handles conversation, documentation, and review, while specialty AI modules are added where clinical validation exists.
No. AI organizes materials, surfaces possible gaps, and provides review-ready information. Final diagnosis and treatment decisions remain with licensed clinicians.
Layer 1 is reusable across outpatient care. Layer 2 can go deep in each specialty without rebuilding the same workflow every time.
Yes. Clinics can begin with the workflow or specialty module that fits their current need, then expand on the same platform foundation.
Recruiting early validation partner clinics
Help us set the standard for clinical AI
We are looking for clinics and partners to validate physician-supporting AI in real clinical practice.