PDF(Pubmed)
Abstract：
UNASSIGNED: In recent years, the implementation of artificial intelligence (AI) in health care is progressively transforming medical fields, with the use of clinical decision support systems (CDSSs) as a notable application. Laboratory tests are vital for accurate diagnoses, but their increasing reliance presents challenges. The need for effective strategies for managing laboratory test interpretation is evident from the millions of monthly searches on test results\' significance. As the potential role of CDSSs in laboratory diagnostics gains significance, however, more research is needed to explore this area.
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.
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.
摘要：
■近年来，人工智能(AI)在医疗保健中的实施正在逐步改变医疗领域，使用临床决策支持系统(CDS)作为一个值得注意的应用。实验室检查对准确诊断至关重要,但是他们日益依赖带来了挑战。从每月对测试结果的数百万次搜索中可以明显看出，需要有效的策略来管理实验室测试解释。随着CDS在实验室诊断中的潜在作用越来越重要，然而,需要更多的研究来探索这个领域。
■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。
■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。
