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Medical AI startups spend an average of 12–24 months obtaining MFDS (Korea's FDA equivalent) medical device approval (Class 2/3), and the requirements at each stage—pre-consultation, technical document review, clinical validation, and product licensing—change frequently. Startups with fewer than 20 employees typically lack dedicated RA (Regulatory Affairs) staff, forcing the CEO or CTO to personally monitor MFDS notices and guideline updates, consuming 5–8 hours per week. Missing a requirement change and having to rewrite documentation adds 3–6 months of delay.
Automatically crawls MFDS medical device notices and guideline changes, and provides a project board that manages stage-by-stage checklists and timelines for the approval process. When a change affects the user's current approval stage, an instant alert is sent along with an LLM-generated guide for the required document revisions.
| N Novelty | 1-5 | How uncommon the service is in market context. |
| U Urgency | 1-5 | How urgently users need this problem solved now. |
| M Market | 1-5 | Market size and growth potential from proxy indicators. |
| R Realizability | 1-5 | Buildability for a small team with realistic constraints. |
| V Validation | 1-5 | Validation signal quality from competition and demand data. |
| Tech Complexity | / 40 | Difficulty of core implementation stack. |
| Data Availability | / 25 | Practical availability and cost of required data. |
| MVP Timeline | / 20 | Expected time to ship a usable MVP. |
| API Bonus | / 15 | Bonus for viable public API leverage. |
| Competition | / 20 | Signal quality from competitor landscape. |
| Market Demand | / 20 | Demand proxies from search and mention patterns. |
| Timing | / 20 | Fit with current shifts in tech, behavior, and regulation. |
| Revenue Signals | / 15 | Reference evidence for monetization viability. |
| Pick-Axe Fit | / 15 | How well the concept serves participants in a trend. |
| Solo Buildability | / 10 | Practicality for lean-team implementation. |