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人类之心,机器之智:人工智能如何改变尼日利亚的儿科护理

Human Hearts, Machine Minds: How Artificial Intelligence Can Transform Paediatric Care in Nigeria.

作者信息

Abdu-Raheem Fadlulai, Musa Sani

机构信息

Department of Paediatrics, Afe Babalola University, Ado-Ekiti, Nigeria.

Department of Paediatrics, Ahmadu Bello University, Zaria, Nigeria.

出版信息

Niger Med J. 2025 Jun 16;66(2):841-844. doi: 10.71480/nmj.v66i2.627. eCollection 2025 Mar-Apr.

DOI:10.71480/nmj.v66i2.627
PMID:40703908
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC12280307/
Abstract

Due to its ubiquitous use in diverse sectors, artificial intelligence (AI) has been hailed as the fourth industrial revolution. AI's potential has not been fully realised in Nigeria's medical space, notably in paediatric care. We discuss how AI can improve paediatric care in Nigeria, focusing on early diagnosis, precision medicine, and remote monitoring. AI must overcome several obstacles to be appropriately integrated into paediatric care. The disparity between training data and healthcare settings can cause algorithmic bias, diagnostic errors, and flawed treatment recommendations. More substantial patient privacy rights and transparent AI algorithms are needed to build healthcare workers and patient trust in AI and ease AI adoption. This paper contends that medical curricula must include training in AI and related fields to equip future Nigerian paediatricians with the skills to maximise AI's potential. Meanwhile, Nigerian paediatricians and AI specialists must join forces to adapt AI technologies to Nigeria's particular needs.

摘要

由于人工智能(AI)在各个领域的广泛应用,它被誉为第四次工业革命。在尼日利亚的医疗领域,尤其是儿科护理方面,人工智能的潜力尚未得到充分发挥。我们将探讨人工智能如何改善尼日利亚的儿科护理,重点关注早期诊断、精准医学和远程监测。人工智能要想被妥善整合到儿科护理中,必须克服几个障碍。训练数据与医疗环境之间的差异可能导致算法偏差、诊断错误和有缺陷的治疗建议。需要更切实的患者隐私权和透明的人工智能算法,以建立医护人员和患者对人工智能的信任,并促进人工智能的采用。本文认为,医学课程必须包括人工智能及相关领域的培训,以使未来的尼日利亚儿科医生具备充分发挥人工智能潜力的技能。与此同时,尼日利亚的儿科医生和人工智能专家必须携手合作,使人工智能技术适应尼日利亚的特殊需求。

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本文引用的文献

1
Diagnostic Accuracy of a Large Language Model in Pediatric Case Studies.大型语言模型在儿科病例研究中的诊断准确性。
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PLOS Digit Health. 2023 Feb 9;2(2):e0000198. doi: 10.1371/journal.pdig.0000198. eCollection 2023 Feb.
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