@unisound-llm

unisound-med-teach generates clinical teaching cases from question text or structured json input, routing them through an internal medical llm and returning formatted json output with answer, metadata, and compliance notes.

unisound-med-synonym matches medical terminology to canonical forms via internal llm. accepts question text or structured json input with configurable model parameters and multiple file input modes.

unisound-med-treat generates structured diagnostic and treatment recommendations from medical case summaries via internal llm, accepting question text or jsonl/json files with configurable model parameters and dry-run capability.

unisound-sm-doc extracts and structures medical entities from clinical notes via internal medical llm, accepting question text or jsonl input and outputting json with extracted fields. deployment is standalone via scripts/run.py.

med-followup-record-struct extracts and normalizes chinese outpatient followup records into granular json fields covering history, examination, diagnosis, and treatment recommendations. designed for medical text structuring with privacy controls.

门诊慢病审核（糖尿病/高血压）。输入 OCR 结果数组 JSON，由内部医疗大模型输出审核结论与原因（原始 JSON + 自然语言结论）。

医学名词规范解释。由调用方提供题目文本或含 `question` 字段的结构化输入，经内部医疗大模型按题干约束生成作答；仅含 `scripts/run.py`，可独立拷贝部署。

从中文医患对话文本生成门诊初诊病历，输出结构化分段的病历正文（文本）。