人工智能在医学影像领域的应用与挑战

人工智能与医学影像的结合被认为是最具发展前景的领域。其在医学影像中的应用,主要包括计算机辅助诊断、影像组学、影像基因组学等场景。当前,人工智能在医学影像领域的应用还处于初级阶段,面临着诸多来自技术和伦理的挑战,如数据质量问题、机器性能问题、算法偏见问题、隐私泄露问题以及责任划分问题等,需采取措施加以规避和制约。如:制定相应的法律法规和伦理规范,建立质控管理系统和监管体系,加强理论攻关和技术研发力度,推动人工智能技术不断走向成熟和完善等。     

智能诊室资源调配系统的开发与应用

门急诊诊室资源分配问题是医院工作人员日常需要处理的问题之一。为提高门急诊患者就诊效率和患者满意度,该文利用大数据平台与人工智能算法技术,设计了一套智能诊室资源调配系统,以优化传统诊室资源调配流程。经上海某三甲医院实际应用后结果显示,该系统能有效提升门急诊就诊效率,优化患者就医体验。     

人工智能在药物靶点的筛选及验证方面的应用进展

近年来人工智能发展迅速，随着算力的提升、算法的迭代，人工智能极大方便了生物信息、化学信息和临床数据的收集及处理，为新药研发注入了新的活力。本综述对人工智能在制药领域的发展历程及其主要算法进行了简要介绍，随后结合具体实例对人工智能在药物靶点筛选及验证方面的不同阶段进行了详细描述，包括药物靶点发现、蛋白结构预测以及苗头化合物生成与优化等。最后对人工智能平台“端到端”的一次高效应用过程进行了具体讨论。     

电子病历的产科专病早期预警服务构建与应用

为提升产科专病诊疗水平,保障孕产妇和胎儿健康,某院基于电子病历系统数据构建了产科专病早期智能预警服务。设计了自底向上共六层的架构模式,包括硬件设备层、数据基础层、技术支撑层、项目管理层和应用展示层。为实现智能化预警,服务集成了5类产科专病相关的人工智能算法,用于早期预警服务的全流程数据挖掘和模型预警。服务以微信小程序的形式,为患者用户提供疾病早期预警信息和后续诊疗建议,支持用户选择多种不同的人工智能预警算法。该服务自上线以来,使用量逐月平均增长率达到33.4%,患者满意度和就诊体验逐月提升,从而有效促进了该院的妇幼保健水平提升,显示了良好的实际应用价值。     

人工智能在ICU辅助诊疗应用初探

介绍重症患者临床数据研究与应用发展现状,从关键技术、系统设计、功能实现等方面详细阐述人工智能诊断系统在ICU辅助诊疗应用,指出人工智能应用于ICU辅助诊疗可提高医护工作效率和准确率,为医生提供辅助临床决策工具。     

人工智能在临床麻醉教学领域应用现状研究

随着互联网技术和大数据的发展，人工智能在麻醉学领域的应用日益广泛。在临床教学领域，人工智能同样也引发教学模式、内容和评价等一系列的创新和变革。本文结合人工智能在麻醉学领域的应用现状，分析人工智能在临床麻醉教学中对教学模式、教学效果评价、教学管理及涉及伦理问题等方面可能产生的影响，为后期人工智能融入临床麻醉教学实践进行理论铺垫和前期探讨。     

优质护理干预在改善维持性血液透析患者心功能及消极情绪的作用探讨

长期进行血液透析的患者,易引发各种并发症,严重威胁患者健康及生命安全。血压异常、心功能不全、消化道疾病、肺水肿、肌肉痉挛等是临床常见血液透析并发症,对患者的心理造成很大压力,患者的消极情绪使其对治疗的依从性大大降低。因此,如何最大程度降低血液透析对患者心功能等的伤害,是临床医护工作者着重思考的问题。本文深入分析血液透析患者产生不良情绪原因,并针对此提出实施优质护理干预措施的建议。     

人工智能视域下社区老年人医养健康服务需求影响因素分析

目的 将人工智能（AI）技术应用于社区老年人医养健康服务中，调查在人工智能视域下社区老年人医养健康服务需求影响因素。方法 采用便利抽样法和对比实验法，随机选取120名老年人作为调查对象，采用医养结合模式服务满意度问卷收集数据。结果 是否患有慢性病、居住方式、文化程度、对AI的认知程度是老年人医养健康服务需求的影响因素。结论加强AI技术在医养健康服务中的应用，需提高老年人对AI的认知程度、降低AI应用成本、加大政策扶持力度及提升AI服务日常照料功能的多样性，以满足社区老年人养老及医疗的双重需求，为其提供适宜的医养健康服务。     

医疗物联网:慢性病智能生命体征监测

介绍慢性病体征监测现状及物联网在慢性病体征监测方面的作用,详细阐述物联网技术采集慢性病患者体征数据的方法、人工智能处理体征数据的算法,分析医疗数据安全问题并提出展望。     

大数据视阈下糖尿病患者管理模式探析

据WHO报道,糖尿病已成为21世纪的一种流行病,其是继肿瘤、心血管病之后的第三大严重威胁人类健康的慢性终身性疾病。文章基于大数据视阈下,立足于我国糖尿病患者管理现状与存在问题,运用互联网的信息交互技术和移动应用技术,通过探讨建立一体化信息系统、移动互联网、物联网、虚拟货币、人工智能5种模式,旨在满足糖尿病患者医疗服务的数据信息化、便捷化需求,有效预防和控制糖尿病,增进医患和谐,提高患者的生存质量,减轻疾病负担,保障人民健康。     

新型冠状病毒肺炎疫情下的基层医疗卫生发展策略

基层医疗卫生机构是新型冠状病毒肺炎疫情防控的“网底”,加强基层新型冠状病毒感染防控工作,提升基层医疗卫生机构服务提供和疫情应对能力,对全国疫情控制工作至关重要。本文通过实地调研,检索国家及各地卫生健康委官方网站疫情防控相关信息,梳理了基层医疗卫生机构在疫情时期发挥的重要作用,并提出疫情平战结合时期基层卫生工作重点,以及今后基层医疗卫生发展策略。针对疫情的复杂形势,目前基层医疗卫生机构应重点做好社区疫情防控、协助开展医疗救治服务、做好常规诊疗服务和公共卫生服务、做好医疗安全和院感防控、充分发挥好县域医共体作用五方面工作。同时,针对疫情应对和基层“短板”,提出今后基层医疗卫生发展策略：加强全科医生制度和分级诊疗制度建设;增强基层医务人员对重大疫情的预警灵敏性、报告及时性和应急处置能力;充分利用人工智能、信息技术工具等手段,持续加强基层信息化建设和应用;推进医联体、医共体建设,探索医防融合的有效模式;大力开展爱国卫生运动,强化群防群控机制,做好健康社区健康乡村建设。     

Towards gender equity in artificial intelligence and machine learning applications in dermatology

There has been increased excitement around the use of machine learning (ML) and artificial intelligence (AI) in dermatology for the diagnosis of skin cancers and assessment of other dermatologic conditions. As these technologies continue to expand, it is essential to ensure they do not create or widen sex- and gender-based disparities in care. While desirable bias may result from the explicit inclusion of sex or gender in diagnostic criteria of diseases with gender-based differences, undesirable biases can result from usage of datasets with an underrepresentation of certain groups. We believe that sex and gender differences should be taken into consideration in ML/AI algorithms in dermatology because there are important differences in the epidemiology and clinical presentation of dermatologic conditions including skin cancers, sex-specific cancers, and autoimmune conditions. We present recommendations for ensuring sex and gender equity in the development of ML/AI tools in dermatology to increase desirable bias and avoid undesirable bias.     

Sharing Personal Health Information Via Service-Oriented Computing: A Case of Long-Term Care

Sharing personal health information among healthcare providers is a crucial business process not only for saving limited healthcare resources but also for increasing patient's healthcare quality. Building an effective personal health information sharing process from established healthcare systems is a challenge in terms of coordination different business operations among healthcare providers and restructuring technical details existed in different healthcare information systems. This study responds this challenge with a service-oriented approach and develops a business software application to describe how the challenge can be alleviated from both managerial and technical perspectives. The software application in this study depicts personal health information sharing process among different providers in a long-term care setting. The information sharing scenario is based on an industrial initiative, such as Integrating the Healthcare Enterprise (IHE) from healthcare domain and the technologies for implementing the scenario are Web Service technologies from Service-oriented computing paradigm. The implementation in this study can inform healthcare researchers and practitioners applying technologies from service-oriented computing to design and develop healthcare collaborative systems to meet the increasing need for personal health information sharing.     

人工智能在医疗领域的应用现状和思考

人工智能在医疗领域有着广泛的应用前景和发展空间,已成为影响医疗健康产业发展的重要科技手段。本文首先分析人工智能在医疗领域的应用现状,其次讨论目前人工智能技术在各医疗领域应用中存在的问题,并提出应对措施和建议,为人工智能技术在我国医疗领域的应用发展提供参考,使人工智能技术更好地助力于医疗领域。     

智能视频分析系统在防范药品差错中的初步构建

目的:药品差错存在于临床用药的各个环节,直接关系到患者的生命健康。利用人工智能技术,构建药品差错智能视频分析系统,保障临床用药安全。方法:根据视频监控,采用人工智能中的深度神经网络技术,实时分析识别监控视频中药品的名称、规格、剂型、厂家和数量,与HIS中处方/医嘱的药品信息进行对比,若有药品差错,实时报警提醒。结果:药品差错智能视频分析系统与医院HIS的处方/医嘱对接,适用于分析防范开具处方/医嘱后调剂、发药、配置和给药环节的药品差错。结论:基于人工智能的视频分析系统可防范临床药品差错,保障临床安全用药。     

A bias evaluation checklist for predictive models and its pilot application for 30-day hospital readmission models

ObjectiveHealth care providers increasingly rely upon predictive algorithms when making important treatment decisions, however, evidence indicates that these tools can lead to inequitable outcomes across racial and socio-economic groups. In this study, we introduce a bias evaluation checklist that allows model developers and health care providers a means to systematically appraise a model’s potential to introduce bias.Materials and MethodsOur methods include developing a bias evaluation checklist, a scoping literature review to identify 30-day hospital readmission prediction models, and assessing the selected models using the checklist.ResultsWe selected 4 models for evaluation: LACE, HOSPITAL, Johns Hopkins ACG, and HATRIX. Our assessment identified critical ways in which these algorithms can perpetuate health care inequalities. We found that LACE and HOSPITAL have the greatest potential for introducing bias, Johns Hopkins ACG has the most areas of uncertainty, and HATRIX has the fewest causes for concern.DiscussionOur approach gives model developers and health care providers a practical and systematic method for evaluating bias in predictive models. Traditional bias identification methods do not elucidate sources of bias and are thus insufficient for mitigation efforts. With our checklist, bias can be addressed and eliminated before a model is fully developed or deployed.ConclusionThe potential for algorithms to perpetuate biased outcomes is not isolated to readmission prediction models; rather, we believe our results have implications for predictive models across health care. We offer a systematic method for evaluating potential bias with sufficient flexibility to be utilized across models and applications.     

Brief communication: Louisiana Clinical Data Research Network: establishing an infrastructure for efficient conduct of clinical research

The state of Louisiana, like the nation as a whole, is facing the salient challenge of improving population health and efficiency of healthcare delivery. Research to inform innovations in healthcare will best enhance this effort if it is timely, efficient, and patient-centered. The Louisiana Clinical Data Research Network (LACDRN) will increase the capacity to conduct robust comparative effectiveness research by building a health information technology infrastructure that provides access to comprehensive clinical data for more than 1 million patients statewide. To ensure that network-based research best serves its end-users, the project will actively engage patients and providers as key informants and decision-makers in the implementation of LACDRN. The network''s patient-centered research agenda will prioritize patients’ and clinicians’ needs and aim to support evidence-based decisions on the healthcare they receive and provide, to optimize patient outcomes and quality of life.     

人工智能技术在基于电子病历研究中的应用与展望

人工智能技术在临床医学领域已取得突破性进展,如诊断、影像、疾病分期分级等。电子病历蕴含疾病描述、诊断、检查、治疗等大量临床数据,在医学专家和信息学家的共同参与下,利用人工智能技术挖掘电子病历数据的研究急剧增加。虽然该方法目前存在一些局限性,但与传统人工研究相比其具有更快速、经济、方便等优势,有望更好地服务于人类健康医学事业的发展。本文对利用人工智能技术挖掘电子病历数据的现状,包括相关技术、具体实例、局限性等进行综述。     

Latent bias and the implementation of artificial intelligence in medicine

Increasing recognition of biases in artificial intelligence (AI) algorithms has motivated the quest to build fair models, free of biases. However, building fair models may be only half the challenge. A seemingly fair model could involve, directly or indirectly, what we call “latent biases.” Just as latent errors are generally described as errors “waiting to happen” in complex systems, latent biases are biases waiting to happen. Here we describe 3 major challenges related to bias in AI algorithms and propose several ways of managing them. There is an urgent need to address latent biases before the widespread implementation of AI algorithms in clinical practice.     

互联网医疗在老年人健康管理中的应用及启示

阐述互联网医疗在老年人健康管理中的应用逻辑、应用现状,分析其存在的挑战并提出相应对策,包括推进供给型政策、增设环境型政策,促进互联网医疗、医药、医保联动发展,做好患者隐私与数据安全工作等。