国外动物智能军事应用情报研究

目的：阐述动物智能在军事学的应用及意义。方法：以学术论文、新闻、报纸、智库报告等作为信息源，综合研究国外动物智能军事应用实例及内在关联，描述动物智能在未来战场应用场景。结果：动物智能军事应用主要是动物特殊能力的军事应用和动物智能的仿生武器应用。结论：动物部队、仿生作战部队和动物战术是未来战争不可忽视的重要组成部分，动物智能的隐匿、灵活、多变等特点正逐渐被广泛应用于战场。     

下一代诊断病理学

随着人类基因组测序、生物大数据信息分析、分子病理检测和人工智能辅助病理诊断等技术进步及其应用, 临床医学发展迈向精准诊疗时代。这一时代背景下, 传统诊断病理学迎来前所未有的历史机遇, 正在向"下一代诊断病理学(next-generation diagnostic pathology)"迈进。下一代诊断病理学以病理形态和临床信息为诊断基础, 以分子检测与生物信息分析、智慧制样与流程质控、智能诊断与远程会诊、病灶活体可视化与"无创"病理诊断等创新前沿交叉技术为主要特征, 以多组学和跨尺度整合诊断为病理报告内容, 实现对疾病的"最后诊断", 并预测疾病演进和结局、建议治疗方案和评估治疗反应, 形成新的疾病诊断"金标准"。未来, 需要激发病理学科创新活力, 加快下一代诊断病理学成熟和应用, 重塑病理学科理论和技术体系, 发挥诊断病理学在疾病"防、诊、治、养"等过程中的重要作用, 促进临床医学进一步发展, 服务健康中国战略。     

实习护生情绪智力、临床实习环境与病人安全感知能力的中介效应

目的:了解实习护生情绪智力、临床实习环境与病人安全感知能力现状并探讨三者关系。方法:采用情绪智力量表、临床实习环境量表和卫生职业教育病人安全感知量表对492名实习护生进行调查。结果:实习护生情绪智力总分为(123.81±18.69)分,临床实习环境总分为(119.48±22.16)分,病人安全感知能力总分为(67.16±12.06)分,情绪智力、临床实习环境与病人安全感知能力呈正相关(r值分别为0.723和0.720,P<0.01);情绪智力在临床实习环境和病人安全感知能力间起部分中介效应,中介效应估计值为0.286(28.6%)。结论:学校要举办人文课程活动以培养情绪智力;医院应制订计划增加学习机会,选拔给予护生充分支持的带教老师,改善临床实习环境。     

新时代检验医学发展定位与思考

检验医学作为医学科学的重要支撑学科,在疾病早期诊断、病情监测、预后判断与风险评估等方面发挥着重要作用。21世纪是数字信息时代,高新检测技术、计算机科学及互联网大数据等,为检验医学的发展带来巨大的机遇与挑战,新时代下如何借助信息科技革命实现检验医学新发展是检验工作者面临的重要课题。该文回顾检验医学的发展历程,重点关注检验医学在新时代的发展定位及未来发展方向,以求开创检验医学发展新局面。     

医疗领域人工智能应用的研究进展＊

人工智能引发了医疗领域的数字革命，在推动行业发展方面具有极大潜力。本研究围绕临床诊疗、医学研究和公共卫生三个基本场景，聚焦人工智能与传统中医药的交叉与融合，并着重介绍人工智能在疾病诊断、决策支持、医学研究以及重大公共卫生事件中的应用。虽然人工智能在诸多方面显示出独特优势，但仍存在透明度不高、缺乏安全性评估和相关法律法规监管等问题需要谨慎解决，以促进人工智能技术在医疗领域的推广。     

Systematic Review of Health Economic Evaluations Focused on Artificial Intelligence in Healthcare: The Tortoise and the Cheetah

ObjectivesThis study aimed to systematically review recent health economic evaluations (HEEs) of artificial intelligence (AI) applications in healthcare. The aim was to discuss pertinent methods, reporting quality and challenges for future implementation of AI in healthcare, and additionally advise future HEEs.MethodsA systematic literature review was conducted in 2 databases (PubMed and Scopus) for articles published in the last 5 years. Two reviewers performed independent screening, full-text inclusion, data extraction, and appraisal. The Consolidated Health Economic Evaluation Reporting Standards and Philips checklist were used for the quality assessment of included studies.ResultsA total of 884 unique studies were identified; 20 were included for full-text review, covering a wide range of medical specialties and care pathway phases. The most commonly evaluated type of AI was automated medical image analysis models (n = 9, 45%). The prevailing health economic analysis was cost minimization (n = 8, 40%) with the costs saved per case as preferred outcome measure. A total of 9 studies (45%) reported model-based HEEs, 4 of which applied a time horizon >1 year. The evidence supporting the chosen analytical methods, assessment of uncertainty, and model structures was underreported. The reporting quality of the articles was moderate as on average studies reported on 66% of Consolidated Health Economic Evaluation Reporting Standards items.ConclusionsHEEs of AI in healthcare are limited and often focus on costs rather than health impact. Surprisingly, model-based long-term evaluations are just as uncommon as model-based short-term evaluations. Consequently, insight into the actual benefits offered by AI is lagging behind current technological developments.     

Comparing Radiomics features of tumour and healthy liver tissue in a limited CT dataset: A machine learning study

IntroductionLiver cancer lesions on Computed Tomography (CT) withholds a great amount of data, which is not visible to the radiologists and radiographer. Radiomics features can be extracted from the lesions and used to train Machine Learning (ML) algorithms to predict between tumour and liver tissue. The purpose of this study was to investigate and classify Radiomics features extracted from liver tumours and normal liver tissue in a limited CT dataset.MethodsThe Liver Tumour Segmentation Benchmark (LiTS) dataset consisting of 131 CT scans of the liver with segmentations of tumour tissue and healthy liver was used to extract Radiomic features. Extracted Radiomic features included size, shape, and location extracted with morphological and statistical techniques according to the International Symposium on Biomedical Imaging manual. Relevant features was selected with chi2 correlation and principal component analysis (PCA) with tumour and healthy liver tissue as outcome according to a consensus between three experienced radiologists. Logistic regression, random forest and support vector machine was used to train and validate the dataset with a 10-fold cross-validation method and the Grid Search as hyper-parameter tuning. Performance was evaluated with sensitivity, specificity and accuracy.ResultsThe performance of the ML algorithms achieved sensitivities, specificities and accuracy ranging from 96.30% (95% CI: 81.03%–99.91%) to 100.00% (95% CI: 86.77%–100.00%), 91.30% (95% CI: 71.96%–98.93%) to 100.00% (95% CI: 83.89%–100.00%)and 94.00% (95% CI: 83.45%–98.75%) to 100.00% (95% CI: 92.45%–100.00%), respectively.ConclusionML algorithms classifies Radiomics features extracted from healthy liver and tumour tissue with perfect accuracy. The Radiomics signature allows for a prognostic biomarker for hepatic tumour screening on liver CT.Implications for practiceDifferentiation between tumour and liver tissue with Radiomics ML algorithms have the potential to increase the diagnostic accuracy, assist in the decision-making of supplementary multiphasic enhanced medical imaging, as well as for developing novel prognostic biomarkers for liver cancer patients.