准备好了还是没准备好？评估尼日利亚医疗 workforce 对人工智能临床采用的准备度
Prepared Or Unprepared? Evaluating Healthcare Workforce Readiness for Clinical Adoption of Artificial Intelligence in Nigeria
- College of Health Sciences, Bayero University Kano（贝耶罗大学卡诺分校健康科学学院）
- Federal University of Health Sciences, Azare（阿扎雷联邦健康科学大学）
- African Institute for Research Advancement & Innovation（非洲研究促进与创新研究所）
- Medical Artificial Intelligence Laboratory (MAI Lab)（医学人工智能实验室）
- Aminu Kano Teaching Hospital（阿米努·卡诺教学医院）
- Nnamdi Azikiwe University Teaching Hospital（纳姆迪·阿齐基韦大学教学医院）
- Lagos State Teaching Hospital（拉各斯州立教学医院）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
本研究调查尼日利亚761名医疗专业人员，发现AI意识高（92.6%）但准备度不足（仅63%感到充分），需加强培训、基础设施和伦理框架以弥合差距。
AI中文摘要：
人工智能（AI）正日益融入全球医疗系统，然而其成功的临床采用关键取决于劳动力的准备度，尤其是在基础设施和培训差距持续存在的中低收入国家（LMICs）。这项横断面研究评估了尼日利亚761名跨多个学科和实践环境的医疗专业人员对AI采用的意识、态度、准备度和障碍。数据于2025年12月至2026年3月期间使用结构化、经过验证的问卷收集。对医疗AI的总体意识较高（92.6%）；然而，客观知识和自我报告的准备度仍然有限，40.9%报告知识水平低或非常低，仅63.0%感到充分准备。采用AI的意愿较高：92.5%表示对培训感兴趣，78.7%支持在本科课程中加入AI教育。主要障碍包括缺乏培训（84.7%）、基础设施差（71.1%）、AI工具成本高（61.0%）、担心工作被取代（60.6%）、伦理问题（52.9%）和数据隐私问题（52.7%）。不同地缘政治区域的准备度存在显著差异（卡方(5)=24.28，p<0.001），不同专业群体的意识存在差异（卡方(6)=68.38，p<0.001）。不同专业群体对AI的态度存在显著差异（F=3.32，p=0.003），感到准备充分的专业人员表现出更积极的态度（平均值=3.74），相比之下未感到准备充分者（平均值=3.46）。这些发现揭示了高意识与实际准备度之间的关键脱节，强调了在资源受限环境中，需要有针对性的培训、基础设施投资和明确的实施框架，以弥合AI技术潜力与临床现实之间的差距。
英文摘要：
Artificial intelligence (AI) is increasingly integrated into healthcare systems worldwide, yet its successful clinical adoption depends critically on workforce readiness, particularly in low- and middle-income countries (LMICs) where infrastructural and training gaps persist. This cross-sectional study evaluated awareness, attitudes, preparedness, and barriers to AI adoption among 761 healthcare professionals across multiple disciplines and practice settings in Nigeria. Data were collected between December 2025 and March 2026 using a structured, validated questionnaire. Overall awareness of AI in healthcare was high (92.6%); however, objective knowledge and self-reported preparedness remained limited, with 40.9% reporting low or very low knowledge and only 63.0% feeling adequately prepared. Willingness to adopt AI was high: 92.5% expressed interest in training, and 78.7% supported inclusion of AI education in undergraduate curricula. Key barriers included lack of training (84.7%), poor infrastructure (71.1%), high cost of AI tools (61.0%), fear of job displacement (60.6%), ethical concerns (52.9%), and data privacy concerns (52.7%). Significant differences in preparedness were observed across geopolitical zones (chi-square (5) = 24.28, p < 0.001), and awareness differed across professional groups (chi-square (6) = 68.38, p < 0.001). Attitudes toward AI differed significantly across professional groups (F = 3.32, p = 0.003), with professionals who felt prepared demonstrating more positive attitudes (mean = 3.74) compared to those who did not (mean = 3.46). These findings reveal a critical disconnect between high awareness and actual readiness, underscoring the need for targeted training, infrastructure investment, and clear implementation frameworks to bridge the gap between AI technological potential and clinical reality in resource-constrained settings.
