5G+AI技术赋能医学本科教学改革的相关探索

医学本科教学一直面临着理论与实践不均衡、教学模式陈旧等问题。近年来,5G和人工智能（AI）的迅猛发展为医学本科教学改革提供了新机遇。本综述旨在分析5G+AI技术在医学本科教学中的应用,探讨其对教学内容、教学方法、基础设施与资源、数据隐私与安全等方面的影响,并展望未来的发展方向。通过对医学本科教学现状进行详细分析,发现传统教学存在的问题,探讨5G+AI技术如何赋能医学本科教学改革,包括理论课程的在线图书馆应用、AI辅助教学、临床技能中心的5G化等方面,综合考察该技术在推进AI应用、革新医学课程体系、实现仿真模拟等方面的优势,同时对5G+AI技术在医学本科教学中面临的挑战进行分析。5G+AI技术在医学本科教学改革中具有广阔前景,可以通过提高教学效果、个性化教学、实现虚拟实践等方式全面推进医学本科教学水平。然而,要充分发挥其优势,还需解决基础设施建设、教师培训、数据隐私等方面的挑战。期望通过该技术的应用,实现医学本科教学更科学、更高效的目标。     

基于大数据和人工智能的超声医学发展现状及问题研究

超声医学进入大数据时代,与实际业务的融合促进日益加深。本研究梳理了大数据和人工智能(artificial intelligence,AI)技术在甲状腺结节、乳腺肿瘤等疾病的超声诊断中的应用现状。超声AI的优势是减轻医务人员工作量、提高诊断效率、提高诊断准确率、辅助疾病预测、提高基层服务能力。应用方面的问题包括缺乏准入的监管制度、诊断流程,责任界定不清晰。因此,超声AI应强化超声影像数据基础,优化算法算力;注重人机协同;加强准入管理,加强应用监管。     

高质量循证医学证据获取与应用研究

随着医疗大数据时代的到来,循证医学的发展迎来了新的变革。本文介绍了数据驱动的循证医学思维定义,并阐释其为循证医学发展带来的变化。从越来越科学的原始证据来源、智能化的循证医学文献信息提取、基于AI的真实世界健康医疗数据分析、创新的临床科研专病数据库、基于AI的临床指南等场景,探讨了智慧化高质量循证医学证据的获取与应用过程,总结了循证医学证据质量的影响因素,包括数据集成、数据质量、技术偏差、数据安全和伦理、人才队伍等。     

人工智能对我国检验医学的机遇与挑战

人工智能(AI)应用于医疗健康领域是大势所趋,将全方位推动检验医学的变革。该文结合我国检验医学的发展现状,探讨了AI相关技术在提升检验流程的自动化程度、挖掘检验数据的辅助诊断价值、重塑检验行业服务模式中的应用潜力,进而从检验医学工作者的角度,设想未来面临AI取代检验科日常工作带来的冲击,如何实现向检验数据管理人员或检验医师的职能转型,开创人-机协同的检验医学新时代。期望该文对检验医学领域AI发展方向的推演能为广大检验同仁及智能医疗从业者提供启发和参考。     

“检验医学”遇上“人工智能”

陈鸣:人工智能(AI)是研究、开发用于模拟、延伸和扩展人的智能理论、方法、技术及应用系统的一门新兴学科,其发展最早可溯源至20世纪50年代,1956年McCarthy在美国达特默斯的一次学术会议上第一次提出“人工智能”的概念。近年来,随着AI相关学科发展、理论建模、技术创新、软硬件的整体发展,AI技术取得了突破性的进展。2017年7月8日国务院印发的《新一代人工智能发展规划》正式将AI上升到国家发展规划高度,其中针对医疗领域提出了“推广应用人工智能治疗新模式新手段,建立快速精准的智能医疗体系”的任务部署。我国检验医学发展从原始的手工检验起步,经历了半自动化分析到全自动化分析的检验现代化阶段,目前正处在全实验室自动化和实验室信息化时代,而AI可能为检验医学的下一步发展注入新的活力。目前,以专家系统(MES)、人工神经网络(ANN)、数据挖掘(DM)为支撑的AI技术在疾病诊断、提升检验流程自动化程度、个体化结果的分析和DM等医学检验领域得到了广泛应用。本期主持人邀请了国内从事智能检验研究的多位专家,一起来探讨AI技术目前在智能检验领域的优势与挑战,同时对下一步AI技术领域的方法方向进行了展望。     

大数据分析在智慧医疗辅助诊断中的应用与发展趋势

智慧医疗以临床大数据为基础,以物联网、云计算、人工智能等技术为手段,是一种以患者数据为中心的医疗服务模式。该文介绍了当前智慧医疗和大数据概况,分析总结了当前大数据分析在智慧医疗辅助诊断中的应用,包括在医学检验、医学图像分析、临床决策支持系统以及远程诊疗中的应用,并对大数据分析在智慧医疗诊断中的挑战及未来发展趋势进行了展望。     

重症医学专科大数据平台的建设及应用

目的:基于医院各业务系统,建立重症医学专科大数据平台,支持人工智能和大数据应用,为临床科研提供数据基础和支持。方法:整合医院信息系统、电子病历系统、监护信息系统、检验信息系统、放射信息系统、手术麻醉临床信息系统等业务系统的数据,并对数据进行清洗,形成重症医学专科大数据平台。同时,基于重症医学专科大数据平台建立APACH...     

丹麦国际循证医学研讨会 2009年4月22日在哥本哈根举行

大会主题为“循证医学与Cochrane系统评价：挑战与机遇”,共分4个专题,包括Cochrane系统评价对循证医学的作用、循证医学的传播、循证医学在低和中低收入国家和循证医学面临的挑战。来自北欧、英国、德国和中国Cochrane中心的主任分别联合主持了4个专题、16位国际顶级循证医学专家的讲座。大会在高质量的讲座和踊跃的讨论与互动中结束。     

人工智能在检验医学领域的应用进展

人工智能为检验医学的划时代发展提供了良好机遇。目前人工智能在检验医学领域的应用主要包括样本处理环节、形态学检验、检验结果审核及检验报告解读等,其在检验各阶段的参与均有效提高检验质量。利用机器学习对检验及相关临床数据进行深度挖掘从而建立疾病诊断模型已成为人工智能在检验医学领域的潜在应用思路。大数据与人工智能对于检验医学精准化的转变具有无可替代的作用,发展前景广阔,但当前仍面临诸多挑战。正确应对随之而来的挑战,促进二者的融合,势必将推动检验医学的高质量发展。     

卫生技术评估与循证医学

随着人口增长、年龄老化、新技术和新药物的应用、人类健康需求层次的提高,使全世界都面临着严峻的挑战：有限卫生资源与无限增长的卫生需求之间的矛盾。本文通过简要介绍卫生技术、卫生技术评估的基本概念,国际上卫生技术评估的产生背景、现况、评估的范畴和特点及评估结果对卫生技术合理应用的影响,国内卫生技术应用和评估的现状、存在问题,说明中我国建立具有权威性的卫生技术评估机构的必要性;同时阐述了循证医学与卫生技术     

Big Data,Machine Learning,and Artificial Intelligence to Advance Cancer Care: Opportunities and Challenges

ObjectivesThe rapid advances in artificial intelligence (AI), big data, and machine learning (ML) technologies hold promise for personalized, equitable cancer care and improved health outcomes within the context of cancer and beyond. Furthermore, integrating these technologies into cancer research has been effective in addressing many of the challenges for cancer control and cure. This can be achieved through the insights generated from massive amounts of data, in ways that can help inform decisions, interventions, and precision cancer care. AI, big data, and ML technologies offer, either in isolation or in combination, unconventional pathways that facilitate the better understanding and management of cancer and its impact on the person. The value of AI, big data, and ML technologies has been acknowledged and integrated within the Cancer Moonshot program in the U.S. and the EU Beating Cancer Plan in Europe.Data SourcesRelevant studies on the topic have formed the basis for this article.ConclusionIn a shifting health care environment where cancer care is becoming more complex and demanding, big data and AI technologies can act as a vehicle to facilitating the care continuum. An increasing body of literature demonstrates their impactful contributions in areas such as treatment and diagnosis. These technologies, however, create additional requirements from health care professionals in terms of capacity and preparedness to integrate them effectively and efficiently in clinical practice. Therefore, there is an increasing need for investment and training in oncology to combat and overcome some of the challenges posed by cancer control.Implications for Nursing PracticeAI, big data, and ML are increasingly integrated in various aspects of health care. As a result, health care professionals, including nurses, will need to adjust in an ever-changing practice environment where these technologies have potential applications in clinical settings to improve risk stratification, early detection, and surveillance management of cancer patients.     

Digital Transformation of Cancer Care in the Era of Big Data,Artificial Intelligence and Data-Driven Interventions: Navigating the Field

ObjectivesTo navigate the field of digital cancer care and define and discuss key aspects and applications of big data analytics, artificial intelligence (AI), and data-driven interventions.Data SourcesPeer-reviewed scientific publications and expert opinion.ConclusionThe digital transformation of cancer care, enabled by big data analytics, AI, and data-driven interventions, presents a significant opportunity to revolutionize the field. An increased understanding of the lifecycle and ethics of data-driven interventions will enhance development of innovative and applicable products to advance digital cancer care services.Implications for Nursing PracticeAs digital technologies become integrated into cancer care, nurse practitioners and scientists will be required to increase their knowledge and skills to effectively use these tools to the patient's benefit. An enhanced understanding of the core concepts of AI and big data, confident use of digital health platforms, and ability to interpret the outputs of data-driven interventions are key competencies. Nurses in oncology will play a crucial role in patient education around big data and AI, with a focus on addressing any arising questions, concerns, or misconceptions to foster trust in these technologies. Successful integration of data-driven innovations into oncology nursing practice will empower practitioners to deliver more personalized, effective, and evidence-based care.     

Disruptive Innovation and Technological Influences on Healthcare

The purpose of this article is to discuss how disruptive innovation and technologies, such as artificial intelligence (AI) and clinical decision support systems (CDSS), are used in healthcare practice, the impact of use, and implications for healthcare staff and leaders associated with the protection of sensitive patient data. Additionally, this article will address the implications associated with using disruptive technologies in practice and the need to safeguard health information. The rising use of AI and CDSS, the need for cohesive management of data, and the use of analytic software have been identified as one of the major issues facing healthcare leaders today. In this brief, a summary of the issues and the implications for future healthcare leaders will be discussed.     

Medical Informatics Standards Applicable to Emergency Department Information Systems: Making Sense of the Jumble

The adoption of medical informatics standards by emergency department information systems (EDISs) is not universal, despite obvious benefits. Clinicians and administrators looking to obtain an EDIS need to know exactly what the various standards can do for them and how the systems they depend on can be integrated and extended. In addition to the standard methods for systems to communicate (chiefly Health Level 7 [HL7]) and those required for submission of claims (Current Procedural Terminology [CPT]-4, International Classification of Diseases, Ninth Revision, Clinical Modification [ICD-9-CM], and X12N), there are several other available standards that are clinically useful and can greatly improve the ability to access and exchange patient information. Major advances in the Unified Medical Language System of the National Library of Medicine have made the patient medical record information standards (Systematized Nomenclature of Medicine [SNOMED], Logical Observation Identifiers, Names, and Codes [LOINC], RxNorm) easily accessible. Detailed knowledge of the arcana associated with the technical aspects of the standards is not needed (or desired) by clinicians to use standards-based systems. However, some knowledge about the commonly used standards is helpful in choosing an EDIS, interfacing the EDIS with the other hospital information systems, extending or upgrading systems, and adopting decision support technologies.     

医疗大数据的“欺骗性”及其对策

当前,针对医疗大数据的研究和应用越来越广泛,但毋庸置疑,医疗大数据本身具有一定欺骗性,在某些特殊场景下,可能会产生错误的结论和影响。本文从数据本身的欺骗性以及机器学习可能存在的陷阱展开,对医疗大数据产生欺骗性的原因进行分析;针对医疗大数据的欺骗性,从统计学角度阐述如何避免大数据陷阱;从模型角度分析模型被攻击的应对策略以及模型可解释性在医疗领域的重要性和方法。     

The use of big data in transfusion medicine

‘Big data’ refers to the huge quantities of digital information now available that describe much of human activity. The science of data management and analysis is rapidly developing to enable organisations to convert data into useful information and knowledge. Electronic health records and new developments in Pathology Informatics now support the collection of ‘big laboratory and clinical data’, and these digital innovations are now being applied to transfusion medicine. To use big data effectively, we must address concerns about confidentiality and the need for a change in culture and practice, remove barriers to adopting common operating systems and data standards and ensure the safe and secure storage of sensitive personal information. In the UK, the aim is to formulate a single set of data and standards for communicating test results and so enable pathology data to contribute to national datasets. In transfusion, big data has been used for benchmarking, detection of transfusion‐related complications, determining patterns of blood use and definition of blood order schedules for surgery. More generally, rapidly available information can monitor compliance with key performance indicators for patient blood management and inventory management leading to better patient care and reduced use of blood. The challenges of enabling reliable systems and analysis of big data and securing funding in the restrictive financial climate are formidable, but not insurmountable. The promise is that digital information will soon improve the implementation of best practice in transfusion medicine and patient blood management globally.     

Current and potential applications of artificial intelligence in medical imaging practice: A narrative review

Background and purposeArtificial intelligence (AI) is present in many areas of our lives. Much of the digital data generated in health care can be used for building automated systems to bring improvements to existing workflows and create a more personalised healthcare experience for patients. This review outlines select current and potential AI applications in medical imaging practice and provides a view of how diagnostic imaging suites will operate in the future. Challenges associated with potential applications will be discussed and healthcare staff considerations necessary to benefit from AI-enabled solutions will be outlined.MethodsSeveral electronic databases, including PubMed, ScienceDirect, Google Scholar, and University College Dublin Library Database, were used to identify relevant articles with a Boolean search strategy. Textbooks, government sources and vendor websites were also considered.Results/DiscussionMany AI-enabled solutions in radiographic practice are available with more automation on the horizon. Traditional workflow will become faster, more effective, and more user friendly. AI can handle administrative or technical types of work, meaning it is applicable across all aspects of medical imaging practice.ConclusionAI offers significant potential to automate most of the manual tasks, ensure service consistency, and improve patient care. Radiographers, radiation therapists, and clinicians should ensure they have adequate understanding of the technology to enable ethical oversight of its implementation.     

Knowledge,perceptions, and expectations of Artificial intelligence in radiography practice: A global radiography workforce survey

BackgroundArtificial Intelligence (AI) technologies have already started impacting clinical practice across various settings worldwide, including the radiography profession. This study is aimed at exploring a world-wide view on AI technologies in relation to knowledge, perceptions, and expectations of radiography professionals.MethodsAn online survey (hosted on Qualtrics) on key AI concepts was open to radiography professionals worldwide (August 1st to December 31st 2020). The survey sought both quantitative and qualitative data on topical issues relating to knowledge, perceptions, and expectations in relation to AI implementation in radiography practice. Data obtained was analysed using the Statistical Package for Social Sciences (SPSS) (v.26) and the six-phase thematic analysis approach.ResultsA total of 314 valid responses were obtained with a fair geographical distribution. Of the respondents, 54.1% (157/290) were from North America and were predominantly clinical practicing radiographers (60.5%, 190/314). Our findings broadly relate to different perceived benefits and misgivings/shortcomings of AI implementation in radiography practice. The benefits relate to enhanced workflows and optimised workstreams while the misgivings/shortcomings revolve around de-skilling and impact on patient-centred care due to over-reliance on advanced technology following AI implementation.DiscussionArtificial intelligence is a tool but to operate optimally it requires human input and validation. Radiographers working at the interface between technology and the patient are key stakeholders in AI implementation. Lack of training and of transparency of AI tools create a mixed response of radiographers when they discuss their perceived benefits and challenges. It is also possible that their responses are nuanced by different regional and geographical contexts when it comes to AI deployment. Irrespective of geography, there is still a lot to be done about formalised AI training for radiographers worldwide. This is a vital step to ensure safe and effective AI implementation, adoption, and faster integration into clinical practice by healthcare workers including radiographers.ConclusionAdvancement of AI technologies and implementation should be accompanied by proportional training of end-users in radiography and beyond. There are many benefits of AI-enabled radiography workflows and improvement on efficiencies but equally there will be widespread disruption of traditional roles and patient-centred care, which can be managed by a well-educated and well-informed workforce.     

The Internet and Electronic Transmission of Medical Records

Objective. To review, from a legal perspective, the potential for using the Internet for inter-institutional transfer of patient medical records. Methods. Basic issues and recent legislation that relate to protection of both medical data, and those transferring that data over public network systems is reviewed. Results. Many laws already in existence can be applied to Internet transmission, but questions of jurisdiction remain. Providing signatures on requests for information, which are in essence contracts, is a problem. Signatures must both prove the identity of the participants and provide for non-repudiation of the agreement. Cryptographic digital signatures appear secure and effective, but their use is difficult to implement. Simpler methods are fraught with risks, yet are more easily accomplished. The patient's rights of privacy must be balanced against the need for access by government, physician, or healthcare institutions to confidential information. In general, information holders must put forth reasonable efforts to keep information confidential. The development of acknowledged standards will provide guidance. Multiple laws provide some deterrence and hence some reassurance to healthcare institutions, for example, by criminalizing acts of electronic interception of patient records in transit. Conclusion. Some believe the expense of secure transfer of medical records by electronic means is a major obstacle; this is false: such transfers are now technologically quite easy. The greatest obstacle to electronic transfer of medical records at this point is the development of workable standards for signing agreements and protecting transmissions, but the perceived advantages will likely drive the necessary developments.     

Medical imaging part of multi-media

The main objective of CEN/TC251/WG4 is to create standards for image communication to allow interoperability between heterogeneous systems in a distributed environment. The scope includes all forms of medical imaging (not only radiology) and also the image related data. The Working Group (WG) is monitoring developments in the whole field of medical imaging and multimedia, in particular image formats, image management, image processing, interface control, security and multimedia (including related standards). International co-operation is crucial resulting in one internationally acceptable standard. Technology independents for cost and evolution reasons is a perquisite of the approach, which results in a focus towards more generic standards. The work items define concrete steps, where the WG considers it important and feasible to produce a deliverable (ENV or CEN Report) within a definite time schedule. New work items will be proposed where needed. The first two Project Teams, which have finished their work, are: - Medical Image and Related Data interchange format standards (WI 4.3) - Profiles for Medical Image interchange (EWOS/EG MED, PTN-024). In both work items (WI), contributions from the United States and Europe are brought together. Europe is presenting a framework which includes an image communication model and a registration procedure for the objects. This ‘road map’ for migration towards the use of IT & T base-standards also contains a medical ISP based on ISO-IPI (Image Processing Interchange (ISO 10227)). Two other Project Teams have been launched at the beginning of 1994: - Medical Image Management standard (WI 4.2) - Medical Data Interchange: HIS/RIS-PACS and HIS/RIS Modality Interface (WI 4.9). These Project Teams have almost completed their tasks and produced ENVs on committed storage of images and on the connection of image producing modalities to information systems respectively. A next step will be the integration of other more dynamic imaging domains like cardiology and endoscopy.