Antigravity Agent
155 lines
5.2 KiB
Markdown
155 lines
5.2 KiB
Markdown
---
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id: wiki-2026-0508-fda-clearance-medical-device-app
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title: FDA Clearance (Medical Device Approval)
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category: 10_Wiki/Topics
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status: verified
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canonical_id: self
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aliases: [510k, fda-510k, premarket-notification]
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duplicate_of: none
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source_trust_level: A
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confidence_score: 0.9
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verification_status: applied
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tags: [regulatory, medical-device, fda, compliance]
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raw_sources: []
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last_reinforced: 2026-05-10
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github_commit: pending
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tech_stack:
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language: english
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framework: regulatory
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---
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# FDA Clearance (Medical Device Approval)
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## 매 한 줄
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> **"매 device 가 predicate 에 substantially equivalent 인가의 증명"**. FDA 의 medical device 시장 진입 경로 — 매 510(k) clearance / De Novo / PMA 의 3 trail. 매 software-as-medical-device (SaMD) 와 AI/ML 의 부상으로 2026 현재 적응형 review pathway 의 도입.
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## 매 핵심
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### 매 Class
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- **Class I** (low risk): 매 general controls. 대부분 exempt.
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- **Class II** (moderate): 매 510(k) submission 필요.
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- **Class III** (high risk, life-supporting): 매 PMA — full clinical trial.
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### 매 경로
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- **510(k)**: predicate device 와 의 substantial equivalence — 매 fastest (3-6 months).
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- **De Novo**: novel low/moderate risk — predicate 의 부재 시.
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- **PMA** (Premarket Approval): Class III — 매 most rigorous, 1-3 year.
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- **Breakthrough Designation**: priority review for unmet need.
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### 매 응용
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1. AI 의료기기 — IDx-DR (diabetic retinopathy), Aidoc (radiology triage).
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2. Surgical robot — da Vinci, Intuitive.
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3. Continuous glucose monitor — Dexcom G7.
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4. SaMD — Apple Watch ECG (De Novo), Cardiologs.
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## 💻 패턴
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### Predicate Search
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```python
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import requests
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def search_510k(device_name: str, limit: int = 50):
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"""openFDA 의 510k database 의 predicate 검색."""
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url = "https://api.fda.gov/device/510k.json"
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params = {"search": f'device_name:"{device_name}"', "limit": limit}
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r = requests.get(url, params=params, timeout=30)
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r.raise_for_status()
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return r.json().get("results", [])
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```
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### Substantial Equivalence Comparison
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```python
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def compare_devices(subject: dict, predicate: dict):
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"""매 indications / technology / performance 의 비교 표 의 생성."""
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rows = []
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for field in ["indications_for_use", "technological_characteristics", "performance"]:
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rows.append({
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"field": field,
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"subject": subject.get(field),
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"predicate": predicate.get(field),
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"different": subject.get(field) != predicate.get(field),
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})
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return rows
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```
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### Adverse Event Lookup (MAUDE)
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```python
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def maude_events(device_name: str, since: str = "2024-01-01"):
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url = "https://api.fda.gov/device/event.json"
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params = {
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"search": f'device.generic_name:"{device_name}" AND date_received:[{since} TO now]',
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"limit": 100,
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}
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return requests.get(url, params=params).json().get("results", [])
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```
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### SaMD Risk Categorization (IMDRF)
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```python
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def samd_category(intended_use: str, healthcare_situation: str) -> str:
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"""IMDRF SaMD: I-IV — 매 information vs treat/diagnose × non-serious/serious/critical."""
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matrix = {
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("inform", "non-serious"): "I",
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("inform", "serious"): "II",
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("inform", "critical"): "II",
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("drive", "non-serious"): "II",
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("drive", "serious"): "III",
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("drive", "critical"): "III",
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("treat-diagnose", "non-serious"): "II",
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("treat-diagnose", "serious"): "III",
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("treat-diagnose", "critical"): "IV",
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}
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return matrix.get((intended_use, healthcare_situation), "unknown")
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```
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### PCCP (Predetermined Change Control Plan) for AI
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```yaml
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pccp:
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modifications:
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- type: retraining
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trigger: quarterly with new data
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validation: hold-out test set AUC > 0.9
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- type: input expansion
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trigger: new sensor model
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validation: equivalence study
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monitoring:
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metrics: [sensitivity, specificity, demographic parity]
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threshold: 5% degradation
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action: rollback + FDA notification
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```
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## 매 결정 기준
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| 상황 | Approach |
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|---|---|
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| Predicate 존재 | 510(k) |
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| Novel low-risk | De Novo |
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| Life-supporting | PMA |
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| AI software | 510(k) + PCCP |
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| Unmet medical need | Breakthrough |
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**기본값**: predicate search 후 510(k) — 매 most devices 의 default.
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## 🔗 Graph
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- 부모: [[Medical-Device-Regulation]] · [[FDA]]
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- 변형: [[510k]] · [[De-Novo]] · [[PMA]]
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- 응용: [[SaMD]] · [[AI-Medical-Device]] · [[Breakthrough-Designation]]
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- Adjacent: [[CE-Mark]] · [[MDR-EU]] · [[ISO-13485]] · [[IEC-62304]]
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## 🤖 LLM 활용
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**언제**: predicate search / SE comparison drafting / adverse event summary.
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**언제 X**: 매 final regulatory submission — 매 RA professional review 의 필수.
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## ❌ 안티패턴
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- **Predicate cherry-picking**: 매 weakest predicate 의 선택 — FDA 의 reject.
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- **Algorithm change without PCCP**: 매 retrain 후 silent deploy — adulteration.
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- **510(k) for novel device**: 매 De Novo 가 필요한 경우 의 wrong path.
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## 🧪 검증 / 중복
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- Verified (FDA CDRH guidance, 21 CFR 807, IMDRF SaMD framework).
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- 신뢰도 A.
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## 🕓 Changelog
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| 날짜 | 변경 |
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| 2026-05-08 | Phase 1 |
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| 2026-05-10 | Manual cleanup — FDA pathways + SaMD/PCCP 패턴 |
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