Intelligent documentation in medical education: Can AI replace manual case logging?
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This study investigates the feasibility of using large language models (LLMs) to automate procedural case log documentation in radiology training. We evaluate whether AI can replace manual logging, identify procedure types most challenging for extraction, and assess integration into clinical workflows. We retrospectively analyzed 36 ,659 radiology reports authored by nine interventional radiology residents (2018â2024). A subset of 414 reports was manually annotated for 39 procedures spanning vascular diagnosis, vascular intervention, and non-vascular intervention. Candidate models, Qwen-2.5 and Claude-3.5, were chosen based on privacy, hardware constraints, and availability, and tested under instruction and chain-of-thought prompting. A crosswalk baseline using structured exam codes provided comparison. Performance was measured by sensitivity, specificity, and F1-score, along with inference time and token efficiency to estimate operational cost. Both local and commercial LLMs outperform..., , # Intelligent Documentation in Medical Education: Can AI Replace Manual Case Logging? This repository contains the code and prompt templates used to evaluate whether large language models (LLMs) can automate structured medical case log generation from clinical encounter notes, benchmarked against manual documentation by medical residents. Raw clinical data is not included due to patient privacy constraints. ## Description of the data and file structure ``` PCL-Fetcher-master/ âââ Dockerfile âââ requirements.txt âââ readme.md âââ code/ â âââ _constant_func.py â âââ _stat_gen.py â âââ 00_format_prompt_appendix.py â âââ 00_preprocess_prompt.py â âââ 00_proc_desc_table.py â âââ 00_procedure_count_distribution.py â âââ 00_report_token_count_distribution.py â âââ 00_resident_count_distribution.py â âââ 00_run_check_bedrock.py â âââ 01_run_llm.py â âââ 01_run_llm.sh â âââ 02_run_llm_bedrock.sh â âââ 02_run_llm_bedrock_converse.py â âââ 02_run_llm_bedrock_invoke.p..., ,
# 医学教育中的智能文档记录：人工智能能否替代人工病例日志录入？ 本研究探讨了利用大语言模型（Large Language Model，LLM）实现放射科培训中操作类病例日志记录自动化的可行性。我们评估了人工智能能否替代人工日志录入工作，识别出提取难度最高的操作类型，并评估其融入临床工作流程的可行性。本研究回顾性分析了2018年至2024年间由9名介入放射科住院医师撰写的36659份放射科报告。其中414份报告作为子集被人工标注，涵盖血管性诊断、血管介入及非血管介入共39类操作。我们基于隐私要求、硬件限制与模型可用性选取了Qwen-2.5与Claude-3.5作为候选模型，并通过指令提示与思维链提示（Chain-of-Thought Prompting）对其进行测试。我们采用基于结构化检查编码的对照基线模型作为对比基准。模型性能通过灵敏度、特异度、F1分数，以及推理时长与Token效率进行评估，以估算运营成本。本地部署与商用大语言模型的表现均优于…… 本仓库包含用于评估大语言模型能否从临床就诊记录自动生成结构化医学病例日志的代码与提示模板，以住院医师的人工文档记录作为基准进行对比。由于患者隐私保护限制，本仓库未包含原始临床数据。 ## 数据与文件结构 PCL-Fetcher-master/ ├── Dockerfile ├── requirements.txt ├── readme.md ├── code/ │ ├── _constant_func.py │ ├── _stat_gen.py │ ├── 00_format_prompt_appendix.py │ ├── 00_preprocess_prompt.py │ ├── 00_proc_desc_table.py │ ├── 00_procedure_count_distribution.py │ ├── 00_report_token_count_distribution.py │ ├── 00_resident_count_distribution.py │ ├── 00_run_check_bedrock.py │ ├── 01_run_llm.py │ ├── 01_run_llm.sh │ ├── 02_run_llm_bedrock.sh │ ├── 02_run_llm_bedrock_converse.py │ ├── 02_run_llm_bedrock_invoke.p...
