Pillay Tahir S, Khan Adil I, Yenice Sedef
Department of Chemical Pathology, Faculty of Health Sciences and National Health Laboratory Service, Tshwane Academic Division, University of Pretoria, Pretoria, South Africa; Division of Chemical Pathology, Department of Pathology, University of Cape Town, Cape Town, South Africa.
Dept. of Pathology & Laboratory Medicine, Lewis Katz School of Medicine, Temple University, Philadelphia, PA, USA.
Clin Chim Acta. 2025 Jun 15;574:120341. doi: 10.1016/j.cca.2025.120341. Epub 2025 May 3.
The integration of artificial intelligence (AI) into point-of-care testing (POCT) represents a transformative leap in modern healthcare, addressing critical challenges in diagnostic accuracy, workflow efficiency, and equitable access. While POCT has revolutionized decentralized care through rapid results, its potential is hindered by variability in accuracy, integration hurdles, and resource constraints. AI technologies-encompassing machine learning, deep learning, and natural language processing-offer robust solutions: convolutional neural networks improve malaria detection in sub-Saharan Africa to 95 % sensitivity, while predictive analytics reduce device downtime by 20 % in resource-limited settings. AI-driven decision support systems curtail antibiotic misuse by 40 % through real-time data synthesis, and portable AI devices enable anaemia screening in rural India with 94 % accuracy, slashing diagnostic delays from weeks to hours. Despite these advancements, challenges persist, including data privacy risks, algorithmic opacity, and infrastructural gaps in low- and middle-income countries. Explainable AI frameworks and blockchain encryption are critical to building clinician trust and ensuring regulatory compliance. Future directions emphasize the convergence of AI with Internet of Things (IoT) and blockchain for predictive diagnostics, as demonstrated by AI-IoT systems forecasting dengue outbreaks 14 days in advance. Personalized medicine, powered by genomic and wearable data integration, further underscores AI potential to tailor therapies, reducing cardiovascular events by 25 %. Realizing this vision demands interdisciplinary collaboration, ethical governance, and equitable implementation to bridge global health disparities. By harmonizing innovation with accessibility, AI-enhanced POCT emerges as a cornerstone of proactive, patient-centered healthcare, poised to democratize diagnostics and drive sustainable health equity worldwide.
将人工智能（AI）集成到即时检验（POCT）中代表了现代医疗保健领域的一次变革性飞跃，解决了诊断准确性、工作流程效率和公平可及性方面的关键挑战。虽然即时检验通过快速出结果彻底改变了分散式医疗，但它的潜力受到准确性差异、集成障碍和资源限制的阻碍。包括机器学习、深度学习和自然语言处理在内的人工智能技术提供了强大的解决方案：卷积神经网络将撒哈拉以南非洲地区疟疾检测的灵敏度提高到95%，而预测分析在资源有限的环境中将设备停机时间减少了20%。人工智能驱动的决策支持系统通过实时数据合成将抗生素滥用减少了40%，便携式人工智能设备在印度农村地区进行贫血筛查的准确率达到94%，将诊断延迟从数周缩短至数小时。尽管取得了这些进展，但挑战依然存在，包括数据隐私风险、算法不透明以及低收入和中等收入国家的基础设施差距。可解释人工智能框架和区块链加密对于建立临床医生的信任和确保监管合规至关重要。未来的发展方向强调人工智能与物联网（IoT）和区块链的融合以进行预测诊断，人工智能-物联网系统提前14天预测登革热疫情就是例证。由基因组和可穿戴数据集成驱动的个性化医疗进一步凸显了人工智能在定制治疗方案方面的潜力，将心血管事件减少了25%。要实现这一愿景，需要跨学科合作、道德治理和公平实施，以弥合全球健康差距。通过将创新与可及性相协调，人工智能增强的即时检验成为积极主动、以患者为中心的医疗保健的基石，有望使诊断民主化并推动全球可持续的健康公平。
