Research Pulse — 2026-06-25-doctobert

---
type: featured-reference
date: 2026-06-25
status: sidecar-post
arxiv: 2606.22079
venue: arXiv cs.CL
source_org: doctolib-lab
authors: [Bofeng Huang, Jacques Sun, Diane Bouchacourt, Nicolas Barascud, Fajwel Fogel]
---

# Featured Reference — DoctoBERT: French Medical Encoder SOTA + Recipe for Korean Porting

> Sidecar post (non-daily research-pulse). Format mirrors `2026-06-17-harness-engineering.md`. Filed 2026-06-25.

**One-line positioning:** Doctolib이 encoder MLM에 적합한 web-data curation recipe를 공개 — **한국어 의료 NLP 시장에 직접 포팅 가능**.

---

## Paper

- **Title:** *Where Does the Signal Live? A Web Data Recipe for Medical Encoder Pretraining*
- **Authors:** Bofeng Huang, Jacques Sun, Diane Bouchacourt, Nicolas Barascud, Fajwel Fogel (Doctolib)
- **arXiv:** 2606.22079 [cs.CL] — Submitted 2026-06-20, 15 pages
- **Code:** github.com/doctolib-lab/doctobert
- **Models:** huggingface.co/doctolib-lab (6 models: DoctoBERT-fr-base, DoctoModernBERT-fr-base, finemed-entity-extractor-fr, finemed-subdomain-classifier-fr, doctobert-fr-ckpts)
- **Datasets:** FineMed (21.1M docs / 19.2B words) + FineMed-filtered + FineMed-rephrased
- **License:** arXiv CC BY 4.0; models + data ODC-By 1.0 / CC BY-SA 4.0

---

## Why this matters for Brown Biotech

**The recipe is language-agnostic by design** — Doctolib explicitly states (Limitations, p.9): *"The pipeline is language- and domain-agnostic by design (multilingual LLM teachers and rephrasers). For a new language, re-distilling the annotators suffices."*

This is rare. Most domain-specific encoder recipes lock to one language via curation choices. Doctolib exposed a **portable methodology**, not just artifacts.

### Korean medical NLP is effectively empty

HuggingFace Korean "medical" datasets (2026-06-25 snapshot) — **5 datasets, 4 mislabeled**:

| Dataset | Actual content | Useful? |
|---|---|---|
| ghfla/korean-medical-dialogue-summary | Carlton Centre (South Africa building) dialogue | ❌ |
| ghfla/korean-medical-classification | 10k classification task | △ |
| hcw0329/medical-korean-alpaca | 15.4k alpaca-format instruction tuning | △ |
| ducut91/korean-medical-dispute-mediation-cases | 611 dispute cases | △ |
| (Korean clinical NER search) | **0 results** | ❌ |

Korean biomedical encoder candidates: **none** — BioBERT / ClinicalModernBERT의 한국어 equivalent 부재.

### DoctoBERT achieved what this recipe unlocks

- **DrBenchmark (public)** — DoctoBERT-fr #1 aggregate (97.14 WP, 98.17 min-max); wins 5/7 per-task
- **Proprietary clinical NER (real-world)** — DoctoModernBERT-fr #1 (F1 79.40)
- **State-of-the-art over 4 prior French medical encoders** (DrBERT, CamemBERT-bio, TransBERT-bio-fr, ModernCamemBERT-bio) + 2 French generalists + 3 English medical

---

## Key Recipe (2 levers)

### Lever 1 — Medical-term density filter (NEW)

**Definition (Eq. 1, §3.2.3):**
```
density = # medical-term characters / # total characters
```

GLiNER2 fine-tuned on 8-class UMLS entity taxonomy. Per-document character ratio of medical spans.

**Why this beats educational quality for encoder MLM** — In dense-terminology domains, masked tokens fall on non-medical text in sparse documents → encoder learns less medical content per pass. Educational-quality filter (FineWeb-Edu style) rewards coherent prose with lay explanations, which **dilutes specialized vocabulary**.

**Ablation (Table 1, §4.1)**:
| Filter | WP |
|---|---|
| NACHOS (curated) | 40.26 |
| TransCorpus-bio-fr (curated) | 32.47 |
| FW2-Med unfiltered | 45.45 |
| FW2-Med: Edu ≥ 4 | 40.26 |
| **FW2-Med: Med-term ≥ 0.1** | **66.23** |
| **FW2-Med: Edu ≥ 4 ∩ Med-term ≥ 0.1** | **68.83** |

**Medical-term density alone beats educational quality by 26 WP. Combined filter beats curated corpora by +29 WP.**

### Lever 2 — Signal-amplifying rephrasing (medical-adapted MGA)

Adapts Massive Genre-Audience rephrasing (Hao et al. 2025) with medical faithfulness constraints.

**Stage 1 (Propose):** Multiple candidate (genre, audience) pairs + register × abbreviation density sampling.
**Stage 2 (Rewrite):** Strictly meaning-preserving — no medical facts/values/entities invented. Non-medical PII replaced with fictional values.

**Critical finding (§4.2, Table 2)** — at 100k source scale:
| Rephraser | WP |
|---|---|
| Raw (no rephrasing) | 54.76 |
| Standard MGA (Qwen3.5-35B-A3B) | **26.19** ← hurts vs raw |
| **Our recipe (Qwen3.5-35B-A3B)** | **95.24** ← +40 WP over raw |

**Medical adaptation > LLM size** — Qwen3.5-35B-A3B beats Qwen3.5-122B-A10B (95.24 vs 73.81 WP). Medically-tuned MedGemma-27B collapses (23.81 WP). The recipe design is the lever, not the model.

### Final mixture (Table 3, §4.2)

| Mix at 1M scale | WP |
|---|---|
| Raw | 53.97 |
| Rephrased only | 57.14 |
| **Rephrased + filtered raw** | **77.78** ← +24 WP |

**Rephrasing complements raw; filtering raw amplifies the gain.** Two levers compound.

---

## Brown Biotech integration paths

### BB-IO Compass (NSCLC CIT, Tier 1 LDT)

- 흉부 CT report / pathology note / 의무기錄 unstructured text → KoDoctoBERT NER layer
- ICD-10 / KDRG / AJCC staging 자동 매핑
- Radiomics feature extraction foundation

### Peptide Service (peptide-service.vercel.app)

- 임상시험 endpoint 자동 coding (사후 보고서 → structured database)
- IRB 자료 준비 비용 ↓, trial timeline ↓

### Paid Briefs (₩2-8M tier)

- "한국 의료기관 ICD-10/KDRG 자동화" packaging
- TAM 보수: 의료기관 70K × ₩20-50M/연 = **₩1.4-3.5 trillion/년**

### biostatx

- entity density metric을 synthetic-vs-real alignment quality 측정으로 재활용
- rare entity의 gradient amplification 원리 활용

---

## Korean Porting Roadmap

### Phase 1 — Feasibility (4 weeks, ~₩5M)
1. KISTI 한국어 메타시소러스 (Korean MeSH / K-UMLS) 가용성 평가
2. HIRA 비식별 데이터 신청 가능성 검토
3. FineWeb-2 한국어 medical content prefilter POC (NVIDIA multilingual classifier)
4. 한국어 ModernBERT backbone 가용성 확인 (없으면 KoBigBird/KR-ELECTRA fallback)

### Phase 2 — KoDoctoBERT-v0 (8 weeks, ~₩30M)
1. Korean GLiNER 학습 (K-UMLS 8-class entity taxonomy)
2. 15-class 한국 subdomain taxonomy (한의약·KDRG 추가)
3. Qwen3.5-35B-A3B 기반 rephraser 한국어 prompt 작성
4. 1B tokens POC 학습

### Phase 3 — Production (12 weeks, ~₩80M)
1. KoDoctoBERT-full 학습 (5-10B tokens, 3-phase pretraining)
2. KoDrBench (한국어 DrBenchmark) 구축 — NER 5-task + classification 2-task
3. BB-IO Compass integration spec + prototype
4. 의료기관 1-2곳 POC

**Total: 24 weeks / ~₩115M / 도메인 파트너 1-2개.**

**추천 파트너:** 서울대병원 AI 랩, 서울아산병원, 분당서울대병원, 한국보건의료연구원 (NIR).

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## Open Questions

1. Korean UMLS (K-UMLS) 완성도 — KISTI의 Korean MeSH / Korean Standard Terminology of Medicine 가용성과 신뢰성?
2. HIRA 비식별 데이터 신청 절차 — 공공데이터포털 vs 건강보험심사평가원 직접 vs 병원 IRB 매개?
3. 한의약 subdomain 별도 모델 vs 통합 — 15-class taxonomy에 "한의약·보완대체의학" 추가 vs 별도 KoDoctoBERT-Han?
4. Rephraser 모델 선택 — paper의 Qwen3.5-35B-A3B 또는 한국어 특화 모델 (예: Solar-Ko, HyperClova)?
5. Medical PII/PHI 처리 — paper는 medical PII에 대해 fictional 치환만. 한국 IRB 기준 별도 준수 필요.

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## Linked Artifacts (Brown Biotech internal)

- Deep analysis: `/Users/ocm/openclaw/workspace/arp-v27/literature/DoctoBERT_Deep_Analysis.md` (12.9 KB, 10 sections)
- bb-wiki concept: `/Users/ocm/openclaw/workspace/bb-wiki/concepts/doctobert-recipe.md` (4-섹션 mandatory)
- Korean brief PDF: `/Users/ocm/.hermes/cache/briefs/koDoctoBERT_brief_2026-06-25.pdf` (1-page)
- PRISM RAG: ingested to `FAISSVectorStore` (TF-IDF, rebuild=True)
- Notion: Active Projects → KoDoctoBERT Feasibility

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**Recommendation:** **Go on Phase 1 즉시 시작.** ~₩5M 비용으로 market validation 가능. Phase 2/3는 Phase 1 결과 보고 결정.

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