关于人工智能在纤维肌痛中应用的医学态度横断面研究:纤维肌痛综合征年度思维实验室(ATLAS 2024)的启示。

IF 1.1 Q4 MEDICINE, RESEARCH & EXPERIMENTAL
Translational Medicine at UniSa Pub Date : 2024-12-06 eCollection Date: 2024-01-01 DOI:10.37825/2239-9747.1066
Marco Cascella, Cosimo Guerra, Rosario De Feo, Valentina Cerrone, Sonia Farah, Piercarlo Sarzi-Puttini, Fausto Salaffi
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引用次数: 0

摘要

背景:人工智能(AI)在医疗保健中的整合有可能彻底改变临床实践,特别是在复杂疾病(如纤维肌痛(FM))的管理方面。尽管前景光明,但在实践中采用该技术面临着一些挑战,包括医疗保健专业人员的知识和准备有限。目的:评价不同学科临床医生在FM人工智能研讨会前后的知识水平。方法:实验结束时进行问卷调查。对参与者进行了一份21项匿名问卷调查。结果:这项调查(n = 26)显示,虽然大多数人有丰富的临床经验,并且之前接触过人工智能,但大多数人缺乏足够的知识,并且对将人工智能整合到FM管理中感到准备不足。大会后,许多人对人工智能的看法有所改善,但仍存在重大障碍,包括缺乏培训、抵制变革和成本担忧。确定的主要好处是症状监测和决策支持。有针对性的培训和技术支持是在临床实践中有效采用人工智能的关键。结论:尽管大会后人们对人工智能的看法发生了普遍的积极转变,但许多医生仍然感到措手不及,缺乏有效利用人工智能工具的必要知识。这些结果强调了有针对性的培训和支持的重要性,以实施研究并促进人工智能工具在FM和其他临床环境中的整合。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Cross-sectional Study on Medical Attitude Towards Artificial Intelligence Use in Fibromyalgia: Insights From the Annual Thinking Lab on Fibromyalgia Syndrome (ATLAS 2024).

Background: The integration of artificial intelligence (AI) in healthcare has the potential to revolutionize clinical practice, particularly in the management of complex conditions such as fibromyalgia (FM). Despite its promise, the adoption of this technology in practice faces several challenges, including limited knowledge and preparedness among healthcare professionals.

Aim: To evaluate the level of knowledge before and after a workshop on AI in FM among clinicians of different disciplines.

Methods: A survey was conducted at the end of the lab. An anonymous 21-item questionnaire was administered to participants.

Results: This survey (n = 26) revealed that while most had extensive clinical experience and some prior exposure to AI, the majority lacked sufficient knowledge and felt unprepared to integrate AI into FM management. Post-congress, perceptions of AI improved for many, but significant barriers remained, including lack of training, resistance to change, and cost concerns. Key benefits identified were symptom monitoring and decision support. Targeted training and technical support were highlighted as essential for effective AI adoption in clinical practice.

Conclusion: Despite a generally positive shift in perception following the congress, many doctors still feel unprepared and lack the necessary knowledge to effectively utilize AI tools. These results underscore the importance of targeted training and support to implement research and facilitate the integration of AI tools in FM and other clinical settings.

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Translational Medicine at UniSa
Translational Medicine at UniSa MEDICINE, RESEARCH & EXPERIMENTAL-
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