摘要
由一名新闻记者-机器人与机器学习的工作人员新闻编辑-每日新闻-调查人员发布了关于人工智能的新报告。根据来自瑞典Halmstad的Newsrx编辑的新闻，该研究称：“基于背景的实践(EBP)inv olves根据三个信息来源做出临床决策：证据、临床经验和患者偏好。尽管EBP已经普及，研究表明，实现EBP模型的目标存在许多障碍，人工智能(AI)在医疗保健领域的应用被认为是改善临床决策的一种手段。我们的新闻编辑从Halmstad大学的研究中获得了一句话：“本文的目的是找出与EBP的三粒AR有关的关键挑战，并调查人工智能在克服这些挑战和促进更基于证据的医疗实践方面的潜力。”我们对EBP的文献和人工智能在HealthCar E中的整合进行了选择性回顾。EBP临床决策的三个组成部分与EBP模型相一致的挑战提出了几个挑战。可靠证据的可用性和存在有时会由于缓慢的生成和传播过程而造成限制。以及高质量证据的稀缺性。直接应用证据并不总是可行的，因为研究往往涉及不同于常规医疗保健中遇到的患者群体。临床医生需要依靠他们的临床经验来解释证据的相关性，并将其与患者的独特需求联系起来。此外，临床决策可能受到认知偏差和内隐偏差的影响。由于患者健康知识水平低、不愿积极参与、临床医生态度障碍、对患者知识的怀疑和沟通策略无效等因素,临床医生和患者之间的积极参与和共享决策仍然是一个挑战。繁忙的医疗保健环境和有限的资源。对EBPAI三个组成部分的人工智能援助为解决研究过程中固有的几个挑战提供了一个有希望的解决方案，包括进行研究、生成证据、综合发现、人工智能系统在处理特定类型的数据和信息方面比人类临床医生有着明显的优势，在图像分析等领域的应用显示出巨大的前景。人工智能为临床医生节省了时间，提高病人参与度提供了很有希望的途径，尽管存在一些问题，但也有可能提高病人的自主性结论本综述强调了人工智能增强循证医疗实践的潜力，可能标志着EBP 2.0.的出现
Abstract
By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Investigators publish new report on ar tificial intelligence. According to news originating from Halmstad, Sweden, by N ewsRx editors, the research stated, "BackgroundEvidence-based practice (EBP) inv olves making clinical decisions based on three sources of information: evidence, clinical experience and patient preferences. Despite popularization of EBP, res earch has shown that there are many barriers to achieving the goals of the EBP m odel. The use of artificial intelligence (AI) in healthcare has been proposed as a means to improve clinical decision-making." Our news editors obtained a quote from the research from Halmstad University: "T he aim of this paper was to pinpoint key challenges pertaining to the three pill ars of EBP and to investigate the potential of AI in surmounting these challenge s and contributing to a more evidence-based healthcare practice. We conducted a selective review of the literature on EBP and the integration of AI in healthcar e to achieve this. Challenges with the three components of EBPClinical decision- making in line with the EBP model presents several challenges. The availability and existence of robust evidence sometimes pose limitations due to slow generati on and dissemination processes, as well as the scarcity of high-quality evidence . Direct application of evidence is not always viable because studies often invo lve patient groups distinct from those encountered in routine healthcare. Clinic ians need to rely on their clinical experience to interpret the relevance of evi dence and contextualize it within the unique needs of their patients. Moreover, clinical decision-making might be influenced by cognitive and implicit biases. A chieving patient involvement and shared decision-making between clinicians and p atients remains challenging in routine healthcare practice due to factors such a s low levels of health literacy among patients and their reluctance to actively participate, barriers rooted in clinicians' attitudes, scepticism towards patien t knowledge and ineffective communication strategies, busy healthcare environmen ts and limited resources. AI assistance for the three components of EBPAI presen ts a promising solution to address several challenges inherent in the research p rocess, from conducting studies, generating evidence, synthesizing findings, and disseminating crucial information to clinicians to implementing these findings into routine practice. AI systems have a distinct advantage over human clinician s in processing specific types of data and information. The use of AI has shown great promise in areas such as image analysis. AI presents promising avenues to enhance patient engagement by saving time for clinicians and has the potential t o increase patient autonomy although there is a lack of research on this issue. ConclusionThis review underscores AI's potential to augment evidence-based healt hcare practices, potentially marking the emergence of EBP 2.0."
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