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University hospitals and research institutes conducting AI-based cancer research need to anonymize DICOM metadata, verify IRB compliance, and quality-check labeling before using medical imaging data for AI training. A single researcher takes 3–4 hours to process 100 images. With tens of thousands of images to process annually, manual errors risk patient information leaks — leading to research suspension and legal liability.
Upload DICOM files and the system auto-detects and removes patient identifiers (name, ID, date of birth, address, etc.), auto-checks IRB compliance, validates labeling consistency (overlap detection, missing-region detection), and generates anonymization audit logs.
| 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. |