AQ-MedAI/GAPS-NSCLC-preview

GAPS医学AI评估数据集是一个专门用于评估临床场景中AI模型的综合评估系统。基于GAPS（Grounded, Automated, Personalized, Scalable）方法，该数据集提供了一个精心策划的临床基准数据集和一个用于医学AI系统的自动化评估流程。数据集包含92个精心策划的临床案例，专注于胸外科场景，特别是非小细胞肺癌（NSCLC）的分期和治疗计划。数据集采用正/负评分系统，全面评估AI临床决策能力，并覆盖了胸外科的关键方面，如术前评估、诊断程序、分期评估、治疗计划、风险评估和分子诊断。

The GAPS Medical AI Evaluation Dataset is a comprehensive evaluation system designed specifically for assessing AI models in clinical scenarios. Based on the GAPS (Grounded, Automated, Personalized, Scalable) methodology, this dataset provides both a curated clinical benchmark dataset and an automated assessment pipeline for medical AI systems. It contains 92 carefully curated clinical cases focusing on thoracic surgery scenarios, particularly non-small cell lung cancer (NSCLC) staging and treatment planning. The dataset employs a positive/negative scoring system for comprehensive evaluation of AI clinical decision-making and covers critical aspects of thoracic surgery such as pre-operative evaluation, diagnostic procedures, staging assessment, treatment planning, risk assessment, and molecular diagnostics.