人工智能放射预测丨ai和放射科的发展

【人工智能放射预测丨ai和放射科的发展】lot物联网小编为你整理了的相关内容，希望能为你解答。

Implementation of artificial intelligence (AI) applications in radiology: hindering and facilitating factors

影响因子： 3.962PMID：32458173期刊年卷：Eur Radiol 2020 May 26;DOI：10.1007/s00330-020-06946-y作者列表: Strohm L, Hehakaya C, Ranschaert ER, Boon WPC, Moors EHM,

OBJECTIVE:The objective was to identify barriers and facilitators to the implementation of artificial intelligence (AI) applications in clinical radiology in The Netherlands.

MATERIALS AND METHODS:Using an embedded multiple case study, an exploratory, qualitative research design was followed. Data collection consisted of 24 semi-structured interviews from seven Dutch hospitals. The analysis of barriers and facilitators was guided by the recently published Non-adoption, Abandonment, Scale-up, Spread, and Sustainability (NASSS) framework for new medical technologies in healthcare organizations.

RESULTS:Among the most important facilitating factors for implementation were the following: (i) pressure for cost containment in the Dutch healthcare system, (ii) high expectations of AI's potential added value, (iii) presence of hospital-wide innovation strategies, and (iv) presence of a "local champion." Among the most prominent hindering factors were the following: (i) inconsistent technical performance of AI applications, (ii) unstructured implementation processes, (iii) uncertain added value for clinical practice of AI applications, and (iv) large variance in acceptance and trust of direct (the radiologists) and indirect (the referring clinicians) adopters.

CONCLUSION:In order for AI applications to contribute to the improvement of the quality and efficiency of clinical radiology, implementation processes need to be carried out in a structured manner, thereby providing evidence on the clinical added value of AI applications.

KEY POINTS:• Successful implementation of AI in radiology requires collaboration between radiologists and referring clinicians. • Implementation of AI in radiology is facilitated by the presence of a local champion. • Evidence on the clinical added value of AI in radiology is needed for successful implementation.

人工智能(AI)在放射学中应用的实施：阻碍和促进因素

目的：目标是确定在荷兰临床放射学中实施人工智能(AI)应用的障碍和促进者。

材料和方法：采用嵌入式多病例研究，采用探索性、定性研究设计。数据收集包括来自7家荷兰医院的24次半结构化访谈。对障碍和促进者的分析以最近发布的新医疗技术在医疗组织中的不采用、放弃、扩大、传播和可持续性(NASSS)框架为指导。

结果：实施的最重要的促进因素如下：(i)荷兰医疗系统成本控制的压力，(Ii)对人工智能潜在附加值的高度期望，(iii)医院范围内创新战略的存在，以及(iv)“本地冠军”的存在。其中最突出的阻碍因素如下：(i)人工智能应用程序的技术表现不一致，(ii)非结构化的实施过程，(iii)人工智能应用程序在临床实践中的附加值不确定，以及(iv)直接(放射科医生)和间接(转介临床医生)采用者的接受和信任差异很大。

结论：为了使人工智能应用有助于提高临床放射学的质量和效率，实施过程需要有组织地进行，从而为人工智能应用的临床附加值提供证据。

要点：·放射学人工智能的成功实施需要放射科医生和转诊临床医生之间的合作。·由于当地冠军的存在，促进了放射学中人工智能的实施。·需要证明人工智能在放射学中的临床附加值的证据才能成功实施。

以上内容为【人工智能放射预测丨ai和放射科的发展】的相关内容，更多相关内容关注lot物联网。