Nivethitha V, Daniel R A, Surya B N, Logeswari G
School of Computer Science Engineering (SCOPE), Vellore Institute of Technology (VIT), Chennai, Tamil Nadu, India.
Department of Community Medicine, ESIC Medical College and Hospital, KK Nagar, Chennai, Tamil Nadu, India.
J Postgrad Med. 2025 Apr 1;71(2):74-81. doi: 10.4103/jpgm.jpgm_634_24. Epub 2025 Jun 9.
India's healthcare system faces substantial challenges, including a high burden of communicable and non-communicable diseases, limited access to healthcare in rural areas, and a shortage of skilled healthcare professionals. Artificial intelligence (AI) offers promising solutions to address these gaps by enhancing diagnostic accuracy, improving disease prediction, and optimizing treatment management. This scoping review examines AI's role in early detection, treatment, and disease prevention in community health settings. A comprehensive literature search was conducted in PubMed, Embase, Scopus, and Google Scholar from January 2013 to July 2024. Eligible studies focused on the application of AI in public health, emphasizing early detection, disease prevention, and treatment interventions. Data on AI models, health outcomes, and performance metrics were extracted and analyzed in line with PRISMA-ScR guidelines. Forty-eight studies were analyzed and categorized into diagnostic accuracy, disease prediction, treatment management, and clinical validation. AI-based tools, such as AIDMAN for malaria detection, demonstrated high diagnostic accuracy (95%) and AUC (0.96). Predictive models for chronic kidney disease (93% accuracy) and diabetes (91% accuracy) showed substantial promise. TB screening using AI-powered cough analysis achieved 86% accuracy. The studies also emphasized AI's role in managing chronic diseases, facilitating early interventions, and reducing healthcare burdens in resource-limited settings. AI has the potential to revolutionize healthcare delivery in India, particularly in underserved regions, by enhancing early detection and treatment. However, challenges related to data privacy, algorithmic bias, and infrastructure require attention. Continued research and policy development are essential to fully harness AI's capabilities in improving public health outcomes.
印度的医疗保健系统面临着诸多重大挑战，包括传染病和非传染病的高负担、农村地区医疗服务可及性有限以及熟练医疗保健专业人员短缺。人工智能（AI）通过提高诊断准确性、改善疾病预测和优化治疗管理，为解决这些差距提供了有前景的解决方案。本综述探讨了人工智能在社区卫生环境中的早期检测、治疗和疾病预防方面的作用。2013年1月至2024年7月，我们在PubMed、Embase、Scopus和谷歌学术上进行了全面的文献检索。符合条件的研究聚焦于人工智能在公共卫生中的应用，强调早期检测、疾病预防和治疗干预。根据PRISMA-ScR指南提取并分析了有关人工智能模型、健康结果和性能指标的数据。共分析了48项研究，并将其分为诊断准确性、疾病预测、治疗管理和临床验证四类。基于人工智能的工具，如用于疟疾检测的AIDMAN，显示出较高的诊断准确性（95%）和曲线下面积（AUC，0.96）。慢性肾病（准确率93%）和糖尿病（准确率91%）的预测模型显示出巨大潜力。使用人工智能驱动的咳嗽分析进行结核病筛查的准确率达到86%。这些研究还强调了人工智能在管理慢性病、促进早期干预以及减轻资源有限环境中的医疗负担方面的作用。人工智能有潜力通过加强早期检测和治疗，彻底改变印度的医疗服务提供方式，尤其是在服务不足的地区。然而，与数据隐私、算法偏差和基础设施相关的挑战需要关注。持续的研究和政策制定对于充分发挥人工智能在改善公共卫生结果方面的能力至关重要。
