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风湿病学中的人工智能:来自美国风湿病学研究员全国性调查的观点与见解

Artificial intelligence in rheumatology: perspectives and insights from a nationwide survey of U.S. rheumatology fellows.

作者信息

Purohit Richa, Saineni Sathvik, Chalise Sweta, Mathai Reanne, Sambandam Rajan, Medina-Perez Richard, Bhanusali Neha

机构信息

Concentra Urgent Care, 8119 S Orange Avenue, Orlando, FL, 32809, USA.

Department of Internal Medicine, University of Central Florida College of Medicine, Orlando, FL, USA.

出版信息

Rheumatol Int. 2024 Dec;44(12):3053-3061. doi: 10.1007/s00296-024-05737-8. Epub 2024 Oct 25.


DOI:10.1007/s00296-024-05737-8
PMID:39453506
Abstract

Artificial Intelligence (AI) is poised to revolutionize healthcare by enhancing clinical practice, diagnostics, and patient care. Although AI offers potential benefits through data-driven insights and personalized treatments, challenges related to implementation, barriers, and ethical considerations necessitate further investigation. We conducted a cross-sectional survey using Qualtrics from October to December 2023 to evaluate U.S. rheumatology fellows' perspectives on AI in healthcare. The survey was disseminated via email to program directors, who forwarded it to their fellows. It included multiple-choice, Likert scale, and open-ended questions covering demographics, AI awareness, usage, and perceptions. Statistical analyses were performed using Spearman correlation and Chi-Square tests. The study received IRB approval and adhered to STROBE guidelines. The survey aimed to reach 528 U.S. rheumatology fellows. 95 fellows accessed the survey with response rate to each question varying between 85 and 95 participants. 57.6% were females, 66.3% aged 30-35, and 60.2% in their first fellowship year. There was a positive correlation between AI familiarity and confidence (Spearman's rho = 0.216, p = 0.044). Furthermore, 67.9% supported incorporating AI education into fellowship programs, with a significant relationship (p < 0.005) between AI confidence and support for AI education. Fellows recognized AI's benefits in reducing chart time (86.05%) and automating tasks (73.26%), but expressed concerns about charting errors (67.86%) and over-reliance (61.90%). Most (84.52%) disagreed with the notion of AI replacing them. Rheumatology fellows exhibit enthusiasm for AI integration yet have reservations about its implementation and ethical implications. Addressing these challenges through collaborative efforts can ensure responsible AI integration, prioritizing patient safety and ethical standards in rheumatology and beyond.

摘要

人工智能(AI)有望通过改善临床实践、诊断和患者护理来彻底改变医疗保健行业。尽管人工智能通过数据驱动的见解和个性化治疗提供了潜在的好处,但与实施、障碍和伦理考量相关的挑战仍需要进一步研究。我们于2023年10月至12月使用Qualtrics进行了一项横断面调查,以评估美国风湿病学研究员对医疗保健领域人工智能的看法。该调查通过电子邮件分发给项目主任,他们再将其转发给他们的研究员。调查包括多项选择题、李克特量表和开放式问题,涵盖人口统计学、人工智能意识、使用情况和看法。使用斯皮尔曼相关性和卡方检验进行统计分析。该研究获得了机构审查委员会(IRB)的批准,并遵循了加强流行病学观察性研究报告规范(STROBE)指南。该调查旨在覆盖528名美国风湿病学研究员。95名研究员访问了该调查,每个问题的回复率在85至95名参与者之间。57.6%为女性,66.3%年龄在30 - 35岁之间,60.2%处于第一年研究员培训期。人工智能熟悉程度与信心之间存在正相关(斯皮尔曼相关系数ρ = 0.216,p = 0.044)。此外,67.9%的人支持将人工智能教育纳入研究员培训项目,人工智能信心与对人工智能教育的支持之间存在显著关系(p < 0.005)。研究员认识到人工智能在减少图表记录时间(86.05%)和自动化任务(73.26%)方面的好处,但对图表记录错误(67.86%)和过度依赖(61.90%)表示担忧。大多数人(84.52%)不同意人工智能会取代他们的观点。风湿病学研究员对人工智能的整合表现出热情,但对其实施和伦理影响有所保留。通过合作努力应对这些挑战可以确保负责任地整合人工智能,在风湿病学及其他领域将患者安全和伦理标准放在首位。

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本文引用的文献

[1]
A survey of artificial intelligence in rheumatoid arthritis.

Rheumatol Immunol Res. 2023-7-22

[2]
A manifesto on explainability for artificial intelligence in medicine.

Artif Intell Med. 2022-11

[3]
Undergraduate Medical Students' and Interns' Knowledge and Perception of Artificial Intelligence in Medicine.

Adv Med Educ Pract. 2022-8-23

[4]
Improved diagnosis of rheumatoid arthritis using an artificial neural network.

Sci Rep. 2022-6-13

[5]
Harnessing Big Data, Smart and Digital Technologies and Artificial Intelligence for Preventing, Early Intercepting, Managing, and Treating Psoriatic Arthritis: Insights From a Systematic Review of the Literature.

Front Immunol. 2022-3-10

[6]
Wearable activity trackers and artificial intelligence in the management of rheumatic diseases : Where are we in 2021?

Z Rheumatol. 2021-12

[7]
Perceptions of Artificial Intelligence Among Healthcare Staff: A Qualitative Survey Study.

Front Artif Intell. 2020-10-21

[8]
COVID-Net: a tailored deep convolutional neural network design for detection of COVID-19 cases from chest X-ray images.

Sci Rep. 2020-11-11

[9]
Data mining for health: staking out the ethical territory of digital phenotyping.

NPJ Digit Med. 2018

[10]
High-performance medicine: the convergence of human and artificial intelligence.

Nat Med. 2019-1-7

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