2nd/10_Wiki/Topics/FDA Clearance (Medical Device Approval).md

---
id: wiki-2026-0508-fda-clearance-medical-device-app
title: FDA Clearance (Medical Device Approval)
category: 10_Wiki/Topics
status: verified
canonical_id: self
aliases: [510k, fda-510k, premarket-notification]
duplicate_of: none
source_trust_level: A
confidence_score: 0.9
verification_status: applied
tags: [regulatory, medical-device, fda, compliance]
raw_sources: []
last_reinforced: 2026-05-10
github_commit: pending
tech_stack:
  language: english
  framework: regulatory
---

# FDA Clearance (Medical Device Approval)

## 매 한 줄
> **"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.

## 매 핵심

### 매 Class
- **Class I** (low risk): 매 general controls. 대부분 exempt.
- **Class II** (moderate): 매 510(k) submission 필요.
- **Class III** (high risk, life-supporting): 매 PMA — full clinical trial.

### 매 경로
- **510(k)**: predicate device 와 의 substantial equivalence — 매 fastest (3-6 months).
- **De Novo**: novel low/moderate risk — predicate 의 부재 시.
- **PMA** (Premarket Approval): Class III — 매 most rigorous, 1-3 year.
- **Breakthrough Designation**: priority review for unmet need.

### 매 응용
1. AI 의료기기 — IDx-DR (diabetic retinopathy), Aidoc (radiology triage).
2. Surgical robot — da Vinci, Intuitive.
3. Continuous glucose monitor — Dexcom G7.
4. SaMD — Apple Watch ECG (De Novo), Cardiologs.

## 💻 패턴

### Predicate Search
```python
import requests

def search_510k(device_name: str, limit: int = 50):
    """openFDA 의 510k database 의 predicate 검색."""
    url = "https://api.fda.gov/device/510k.json"
    params = {"search": f'device_name:"{device_name}"', "limit": limit}
    r = requests.get(url, params=params, timeout=30)
    r.raise_for_status()
    return r.json().get("results", [])
```

### Substantial Equivalence Comparison
```python
def compare_devices(subject: dict, predicate: dict):
    """매 indications / technology / performance 의 비교 표 의 생성."""
    rows = []
    for field in ["indications_for_use", "technological_characteristics", "performance"]:
        rows.append({
            "field": field,
            "subject": subject.get(field),
            "predicate": predicate.get(field),
            "different": subject.get(field) != predicate.get(field),
        })
    return rows
```

### Adverse Event Lookup (MAUDE)
```python
def maude_events(device_name: str, since: str = "2024-01-01"):
    url = "https://api.fda.gov/device/event.json"
    params = {
        "search": f'device.generic_name:"{device_name}" AND date_received:[{since} TO now]',
        "limit": 100,
    }
    return requests.get(url, params=params).json().get("results", [])
```

### SaMD Risk Categorization (IMDRF)
```python
def samd_category(intended_use: str, healthcare_situation: str) -> str:
    """IMDRF SaMD: I-IV — 매 information vs treat/diagnose × non-serious/serious/critical."""
    matrix = {
        ("inform", "non-serious"): "I",
        ("inform", "serious"): "II",
        ("inform", "critical"): "II",
        ("drive", "non-serious"): "II",
        ("drive", "serious"): "III",
        ("drive", "critical"): "III",
        ("treat-diagnose", "non-serious"): "II",
        ("treat-diagnose", "serious"): "III",
        ("treat-diagnose", "critical"): "IV",
    }
    return matrix.get((intended_use, healthcare_situation), "unknown")
```

### PCCP (Predetermined Change Control Plan) for AI
```yaml
pccp:
  modifications:
    - type: retraining
      trigger: quarterly with new data
      validation: hold-out test set AUC > 0.9
    - type: input expansion
      trigger: new sensor model
      validation: equivalence study
  monitoring:
    metrics: [sensitivity, specificity, demographic parity]
    threshold: 5% degradation
    action: rollback + FDA notification
```

## 매 결정 기준
| 상황 | Approach |
|---|---|
| Predicate 존재 | 510(k) |
| Novel low-risk | De Novo |
| Life-supporting | PMA |
| AI software | 510(k) + PCCP |
| Unmet medical need | Breakthrough |

**기본값**: predicate search 후 510(k) — 매 most devices 의 default.

## 🔗 Graph
- 변형: [[510k]]

## 🤖 LLM 활용
**언제**: predicate search / SE comparison drafting / adverse event summary.
**언제 X**: 매 final regulatory submission — 매 RA professional review 의 필수.

## ❌ 안티패턴
- **Predicate cherry-picking**: 매 weakest predicate 의 선택 — FDA 의 reject.
- **Algorithm change without PCCP**: 매 retrain 후 silent deploy — adulteration.
- **510(k) for novel device**: 매 De Novo 가 필요한 경우 의 wrong path.

## 🧪 검증 / 중복
- Verified (FDA CDRH guidance, 21 CFR 807, IMDRF SaMD framework).
- 신뢰도 A.

## 🕓 Changelog
| 날짜 | 변경 |
|---|---|
| 2026-05-08 | Phase 1 |
| 2026-05-10 | Manual cleanup — FDA pathways + SaMD/PCCP 패턴 |