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印度小儿外科医生在日常生活和职业生涯中对人工智能的应用:基于在线调查结果的采用路线图。

Artificial Intelligence Use in Daily and Professional Life among Pediatric Surgeons in India: A Roadmap for Adoption Based on Online Survey Results.

作者信息

Mandelia Ankur, Rengan Vinayak S, Mehta Alish Rajesh, Vankam Aseesh Varma, Babu Ramesh, Biswas Sumitra Kumar, Agrawal Vikesh

机构信息

Department of Pediatric Surgery, Sanjay Gandhi Post Graduate Institute of Medical Sciences, Lucknow, Uttar Pradesh, India.

Department of Pediatric Surgery, SMS Medical College, Jaipur, Rajasthan, India.

出版信息

J Indian Assoc Pediatr Surg. 2025 May-Jun;30(3):361-368. doi: 10.4103/jiaps.jiaps_306_24. Epub 2025 Apr 24.

DOI:10.4103/jiaps.jiaps_306_24
PMID:40406323
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC12094589/
Abstract

BACKGROUND

Artificial intelligence (AI) is transforming the various domains of medicine, including pediatric surgery, where precision and timely decision-making are critical. However, the real-world integration of AI in pediatric surgery, particularly in low- and middle-income countries such as India, remains underexplored. We propose a roadmap for the adoption of AI based on our survey results for (IAPS).

OBJECTIVE

This survey aimed to evaluate the awareness, utilization, and perspectives of pediatric surgeons in India regarding AI in both professional and personal settings, as well as to identify barriers and opportunities for its integration into clinical practice.

METHODOLOGY

A cross-sectional survey was conducted among the members of the IAPS, utilizing a structured online questionnaire. Quantitative data were analyzed using the descriptive statistics and Chi-square tests, whereas qualitative responses were thematically analyzed.

RESULTS

A total of 74 pediatric surgeons participated in the survey. While 60.8% were somewhat familiar with AI, only 47.3% used AI in their clinical practice, primarily for diagnostic imaging (31.1%) and administrative tasks (23%). Perceived benefits included enhanced diagnostic accuracy (45.9%) and improved surgical care (37.8%), yet barriers such as data privacy (39.2%) and concerns over reliability (51.4%) were prevalent. Personal AI adoption was high (70.3%), with virtual assistants and health-tracking apps being most common. Additionally, 86.4% of respondents anticipated AI becoming integral to pediatric surgery within the next decade.

CONCLUSION

Despite limited clinical adoption, there is strong interest among pediatric surgeons in India for AI-focused training and integration. Addressing barriers such as ethical concerns, data privacy, and cost could catalyze AI's potential to revolutionize pediatric surgical care. Our roadmap addressing these challenges through targeted education, ethical guidelines, and better integration strategies will be essential for harnessing AI's full potential in pediatric surgery.

摘要

背景

人工智能(AI)正在改变医学的各个领域,包括小儿外科,在小儿外科中,精准和及时的决策至关重要。然而,AI在小儿外科的实际应用,尤其是在印度等低收入和中等收入国家,仍未得到充分探索。我们根据对印度小儿外科医师协会(IAPS)的调查结果,提出了一个采用AI的路线图。

目的

本次调查旨在评估印度小儿外科医生在专业和个人环境中对AI的认知、使用情况及看法,并确定将其整合到临床实践中的障碍和机会。

方法

对IAPS成员进行了一项横断面调查,使用结构化在线问卷。定量数据采用描述性统计和卡方检验进行分析,而定性回答则进行主题分析。

结果

共有74名小儿外科医生参与了调查。虽然60.8%的人对AI有所了解,但只有47.3%的人在临床实践中使用AI,主要用于诊断成像(31.1%)和行政任务(23%)。感知到的好处包括提高诊断准确性(45.9%)和改善手术护理(37.8%),但数据隐私(39.2%)和对可靠性的担忧(51.4%)等障碍普遍存在。个人对AI的采用率很高(70.3%),虚拟助手和健康追踪应用最为常见。此外,86.4%的受访者预计AI将在未来十年内成为小儿外科不可或缺的一部分。

结论

尽管临床应用有限,但印度小儿外科医生对以AI为重点的培训和整合有着浓厚兴趣。解决伦理问题、数据隐私和成本等障碍可以激发AI变革小儿外科护理的潜力。我们通过有针对性的教育、道德准则和更好的整合策略来应对这些挑战的路线图,对于充分发挥AI在小儿外科的潜力至关重要。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d574/12094589/5ad69d03d40d/JIAPS-30-361-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d574/12094589/680aaae95368/JIAPS-30-361-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d574/12094589/e1f2af80f813/JIAPS-30-361-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d574/12094589/f36e0239d290/JIAPS-30-361-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d574/12094589/31984cfc0074/JIAPS-30-361-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d574/12094589/16415fd499af/JIAPS-30-361-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d574/12094589/5ad69d03d40d/JIAPS-30-361-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d574/12094589/680aaae95368/JIAPS-30-361-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d574/12094589/e1f2af80f813/JIAPS-30-361-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d574/12094589/f36e0239d290/JIAPS-30-361-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d574/12094589/31984cfc0074/JIAPS-30-361-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d574/12094589/16415fd499af/JIAPS-30-361-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d574/12094589/5ad69d03d40d/JIAPS-30-361-g006.jpg

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Deep learning improves physician accuracy in the comprehensive detection of abnormalities on chest X-rays.深度学习提高了医生在胸部 X 光片中全面检测异常的准确性。
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