Адаптация LLM к медицинской терминологии
Адаптация LLM к медицинской терминологии
Сначала проговорите ответ вслух или тезисами.
Формулы, план решения, риски и примеры.
Откройте разбор только после своей попытки.
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Короткий ответ
Start with evaluation and retrieval/terminology grounding, then consider SFT or LoRA on domain examples if data quality and privacy allow. Keep human review and hallucination checks.
Подробный разбор
First define the failure modes: term confusion, missing facts, unsafe recommendations, formatting errors or hallucinated citations. Build a domain evaluation set with clinician-reviewed transcripts, expected notes and terminology checks. In medicine, this is as important as the model choice.
Low-risk improvements include better prompting, structured extraction schemas, glossary/ontology grounding and RAG over approved medical sources or internal clinical guidelines. Retrieval should cite or expose evidence snippets and should be evaluated for freshness and relevance.
If you have enough high-quality, privacy-safe examples, supervised fine-tuning or LoRA can teach the model domain style and terminology. It does not replace RAG for up-to-date factual grounding. The production system should include PHI controls, audit logs, clinician-in-the-loop review, confidence or guardrail checks, and explicit refusal/escalation behavior for uncertain medical content.
Типичные ошибки
- Jump directly to fine-tuning without an eval set.
- Use public medical text without checking privacy and licensing.
- Assume RAG alone fixes summarization style and terminology.
Как сказать на собеседовании
- Start from failure taxonomy and evaluation.
- Separate RAG for facts from SFT/LoRA for behavior and terminology.