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By layering field-specific AI on top of common infrastructure, we improve treatment flow and operational efficiency.
Early partner hospitals are with us from the design stage.
WHAT WE ADOPT
It is a two-tier structure that adds proven AI for each field on a common foundation for all outpatient clinics. Because it is a common foundation, even if the medical department expands, it will be expanded on the same foundation.
Structures clinical conversations and materials into records, check items, and review notes for clinician review.
A function in development that surfaces information and evidence for clinician review. Final judgment remains with clinicians.
Organizes imaging and device inputs for pigmented-lesion assessment. Validation scope is expanding from more than 50 early cases.
An R&D project for wound image quantification and progress documentation, with scope and review criteria designed with clinical advisors.
Using proven internal AI as a standard, we will gradually open an ecosystem in which field-specific AI created by external developers will be built on the same common foundation. Medical institutions will be able to add new field-specific AI after introducing the foundation once. The specific store entry method has not yet been confirmed.
AI / CLINICAL BOUNDARY
All of AetherHeal's products are designed based on the premise that the medical staff remains the subject of decision-making. AI only organizes and delivers the materials for judgment, and the authority to diagnose and treat always rests with the medical staff.
AI augments rather than replaces medical staff. AI's suggestions are always reviewed and adopted by medical staff, with the final decision and responsibility remaining with the medical staff. This structure, premised on the authority and judgment of medical staff, is the foundation of AetherHeal’s trust.
ADOPTION EFFECTS
The effect of introduction is a more organized treatment context, proven assistance in each field, and a billing structure that aligns with the interests of patients and medical institutions.
By having a common foundation for interpretation and information organization, medical staff can reduce the cognitive burden of language and administration and focus on clinical judgment itself. The most immediate change in international patient care is not being tied to interpreter schedules and ensuring the flow of care is not interrupted.
When AI structures and organizes the treatment context and results, consultation and procedure records, which are easily fragmented, remain in a consistent form. The quantitative data accumulated in this way becomes the basis for follow-up treatment and accumulates as a trust asset for medical institutions over time.
DermatoScan AI assists in selecting a device appropriate for the lesion based on selection criteria refined from more than 50 first verification cases at Apgujeong Tune Clinic. Newly introduced hospitals can also make more consistent decisions by referring to verified standards.
All profits are based on a flat rate structure that is unrelated to the price and type of treatment. There is no structural financial incentive to recommend expensive procedures, so the platform’s incentives are aligned with the interests of patients and medical institutions. This consistency is a prerequisite for trusting and introducing AI in medical settings.
PARTNER NETWORK
Early collaborating medical institutions and university-industrial-academic channels support AetherHeal’s clinical verification and expansion. Rather than being the case at a single hospital, the product is refined over a network involving multiple specialists.
WoundScan AI is developed through industry-academia joint development with Professor Ha Won (CEO of PL Therapeutics), Department of Plastic Surgery, University of Ulsan. Clinical verification and research collaboration are carried out through university channels, and the practices of field medical staff and the university's research capabilities are reflected in the product. AetherHeal's depth of verification is created at the point where the initial network of collaborating medical institutions and industry-academia channels meet.
Frequently Asked Questions
We've compiled frequently asked questions about introduction cost and period, data security, boundaries of responsibility, and scope of support.
ONBOARDING PROCESS
We proceed step by step from introducing common infrastructure to applying AI in each field. Each step is determined together by checking the medical institution's treatment situation.
Step 1
We organize which configuration is appropriate based on the medical field, proportion of international patients, and current interpretation/recording flow. At this stage, the costs, scope of coverage and data processing terms are clearly agreed.
Step 2
By applying common infrastructure such as Dockie-talkie first, we lay the foundation for interpretation and information organization. Since it works regardless of the department, the entire outpatient department can use it with one introduction.
Step 3
By adding field-specific AI, such as DermatoScan AI suitable for the medical field, we begin to provide decision-making assistance for each field. Verified selection criteria should be used as reference information, but adoption will be determined by the medical staff.
Step 4
Adjust coverage based on operational data and expand step-by-step on the same infrastructure as newly validated domain-specific AI is ready. Introduction is not a one-time completion, but a process of refinement together.
If you are interested in introduction and cooperation, please feel free to contact us. We review together the configuration that suits the medical institution’s situation.