PO.BCS02.02 · 生物信息与计算
AI与人工在骨关节炎低剂量放射治疗中对疼痛评分和镇痛趋势的数据提取比较：一项一致性研究
AI vs human abstraction of pain scores and analgesic trends in low-dose radiation therapy for osteoarthritis: A concordance study
作者与单位 Authors & Affiliations
摘要 Abstract
中文摘要
背景/意义：
准确的临床数据提取是结局研究的基础，但仍然资源密集且变异较大。人工智能（AI）具有在保持准确性的同时简化这一过程的潜力，但其在真实世界肿瘤学数据中的验证有限。这项由本科生主导的研究评估了Microsoft Copilot在复现人工病历数据提取以获取接受低剂量放射治疗（LDRT）的骨关节炎（OA）患者疼痛和功能结局方面的能力，重点关注一致性、效率和可重复性。
方法：
分析了2024年8月至2025年8月期间接受LDRT（3 Gy，分6次0.5 Gy分次）治疗的30名患者（55个关节）的临床记录。人工评审者手动提取了基线、治疗结束时（EOT）和一个月随访时的数字评定量表（NRS，0-10）和von Pannewitz评分（VPS，0-4）数据。Microsoft Copilot（符合HIPAA）独立提取了相同数据。差异被分类为完全一致、轻微（≤2分差异）或遗漏。使用组内相关系数（ICC）计算NRS的一致性，使用加权kappa计算VPS的一致性。记录了每份病历的数据提取时间。
结果：
AI在NRS上实现了92%的完全匹配（ICC = 0.96，95% CI 0.93-0.98），在VPS上实现了94%（kappa = 0.91）。未产生虚构数据；AI检测到一处人工遗漏。每份病历的平均数据提取时间约为2分钟，而人工为30分钟，效率提升超过10倍。
结论：
AI辅助的数据提取显示出与人工评审者近乎完美的一致性，同时将时间和人力减少了90%以上。这项由本科生主导的研究表明，负责任地实施AI能够提升放射肿瘤学结局研究的准确性、可重复性和效率，并加速肿瘤学中的证据生成。
披露：
无外部资助或利益冲突。数据提取使用Microsoft Copilot（符合HIPAA）进行；未使用生成式AI工具进行写作。
查看英文原文 English abstract
Background / Significance:
Accurate clinical data abstraction underpins outcomes research but remains resource-intensive and variable. Artificial intelligence (AI) offers potential to streamline this process while maintaining accuracy, yet its validation in real-world oncology data is limited. This undergraduate-led study evaluated Microsoft Copilot in replicating human chart abstraction for pain and functional outcomes among osteoarthritis (OA) patients treated with low-dose radiation therapy (LDRT), emphasizing concordance, efficiency, and reproducibility.
Methods:
Clinical notes from 30 patients (55 joints) treated with LDRT (3 Gy in six 0.5 Gy fractions) between August 2024 and August 2025 were analyzed. Human reviewers manually extracted Numeric Rating Scale (NRS, 0-10) and von Pannewitz Score (VPS, 0-4) data at baseline, end-of-treatment (EOT), and one-month follow-up. Microsoft Copilot (HIPAA-compliant) independently extracted identical data. Discrepancies were classified as exact, minor (≤2-point difference), or missed. Concordance was calculated using intraclass correlation coefficient (ICC) for NRS and weighted kappa for VPS. Abstraction time per chart was recorded.
Results:
AI achieved 92 percent exact match for NRS (ICC = 0.96, 95 percent CI 0.93-0.98) and 94 percent for VPS (kappa = 0.91). No fabricated data were produced; one human omission was detected by AI. Mean abstraction time was about 2 minutes per chart versus 30 minutes for humans, a greater than 10-fold efficiency gain.
Conclusions:
AI-assisted abstraction showed near-perfect concordance with human reviewers while reducing time and labor by over 90 percent. This undergraduate-led investigation demonstrates that responsible AI implementation can enhance accuracy, reproducibility, and efficiency in radiation-oncology outcomes research and accelerate evidence generation in oncology.
Disclosure:
No external funding or conflicts of interest. Data abstraction performed using Microsoft Copilot (HIPAA-compliant); no generative AI tools were used for writing.
利益披露 Disclosure
C. Schwartz, None..
M. Anderson, None..
K. Moakler, None..
B. Newby, None..
D. Davenport, None..
M. Schwartz, None.