UNASSIGNED: The primary objective of our study was to assess the accuracy and safety of LabTest Checker (LTC), a CDSS designed to support medical diagnoses by analyzing both laboratory test results and patients\' medical histories.
UNASSIGNED: This cohort study embraced a prospective data collection approach. A total of 101 patients aged ≥18 years, in stable condition, and requiring comprehensive diagnosis were enrolled. A panel of blood laboratory tests was conducted for each participant. Participants used LTC for test result interpretation. The accuracy and safety of the tool were assessed by comparing AI-generated suggestions to experienced doctor (consultant) recommendations, which are considered the gold standard.
UNASSIGNED: The system achieved a 74.3% accuracy and 100% sensitivity for emergency safety and 92.3% sensitivity for urgent cases. It potentially reduced unnecessary medical visits by 41.6% (42/101) and achieved an 82.9% accuracy in identifying underlying pathologies.
UNASSIGNED: This study underscores the transformative potential of AI-based CDSSs in laboratory diagnostics, contributing to enhanced patient care, efficient health care systems, and improved medical outcomes. LTC\'s performance evaluation highlights the advancements in AI\'s role in laboratory medicine.
■我们研究的主要目的是评估LabTestChecker(LTC)的准确性和安全性,CDSS旨在通过分析实验室检查结果和患者病史来支持医疗诊断。
■这项队列研究采用了前瞻性数据收集方法。共有101名年龄≥18岁的患者,在稳定状态下,并要求综合诊断。对每个参与者进行一组血液实验室测试。参与者使用LTC解释测试结果。通过将AI生成的建议与经验丰富的医生(顾问)建议进行比较,来评估该工具的准确性和安全性。这被认为是黄金标准。
■该系统在紧急安全方面达到了74.3%的准确性和100%的灵敏度,在紧急情况下达到了92.3%的灵敏度。它可能减少了41.6%(42/101)的不必要的医疗访问,并在识别潜在病理方面实现了82.9%的准确性。
■这项研究强调了基于AI的CDS在实验室诊断中的变革潜力,有助于加强病人护理,高效的医疗保健系统,改善医疗结果。LTC的绩效评估突出了AI在实验室医学中的作用。