Exploring the Role of AI-Driven Decision Support Systems in Reducing Diagnostic Errors in Rural Healthcare Settings

Abstract

Excessive diagnosis errors are a hurdling problem in rural healthcare systems, which are usually aggravated by poor infrastructure, insufficiency of workers, and lack of specialist care. The conceptual review examines how using Artificial Intelligence Decision Support Systems (AI-DSS) in rural areas can fix errors in decision-making regarding diagnostic errors. It starts with clarifying the definition of diagnostic errors, AI-DSS infrastructure, pattern of functioning, and peculiarities of rural healthcare settings. Based on international publications and theoretical models, namely, the Technology Acceptance Model and the Health Belief Model, the paper discusses the advantages of AI-DSS: accuracy of diagnosis, profiling, and decreased clinician workload. The case examples of India, Nigeria, and Rwanda prove the effectiveness of implementing customised AI solutions in low-resource settings. Nevertheless, significant impediments remain, such as infrastructural shortages, digital illiteracy, data privacy and sustainability. The three opportunities revealed during the review are the critical areas where the customised AI tool could work, collaboration across different sectors, and capacity-building initiatives. It also provides strategic advice on ethical and scaleable integration, such as the publication of regulatory frameworks and investment into offline-compatible technologies. According to the study, it can be concluded that the AI-DSS has shown promising ways to fill the gaps in diagnosing rural healthcare but will only be successful when tailored to local conditions and empirically verified. Additional interdisciplinary studies are required to evaluate real-world effects, inform responsible adoption, and ensure that AI technologies are used to the point of fair healthcare delivery.

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