首页|Study Data from Halmstad University Provide New Insights into Artificial Intelli gence (Towards evidence-based practice 2.0: leveraging artificial intelligence i n healthcare)
Study Data from Halmstad University Provide New Insights into Artificial Intelli gence (Towards evidence-based practice 2.0: leveraging artificial intelligence i n healthcare)
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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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