AI-Based Symptom Checker Tools: Towards Accessible and Reliable Primary Healthcare Support

Abstract

The swift development of artificial intelligence (AI) is revolutionizing primary care through the introduction of new diagnostic, triage, and decision support tools. Current research demonstrates the adoption of AI-powered symptom checkers, chatbots, and clinical decision support systems to increase accessibility, efficiency, and diagnostic precision in various settings, ranging from rural areas to low-resource environments. Evidence indicates that AI-powered tools have the capacity to automate patient-provider interactions, enhance condition detection early on for diseases like Alzheimer's disease and skin conditions, and minimize waiting times via automated self- triage. Systematic reviews and scoping reviews provide encouraging evidence on accuracy, cost, and patient empowerment but also indicate problems with data quality, physician uptake, clinical validation, and ethics. In low- and middle-income nations, AI has tremendous potential to improve workforce scarcity and enhance delivery of care. But issues regarding algorithmic bias, small datasets, and regulatory blocks highlight the necessity for thorough scrutiny and proper implementation. What is emerging from case series and randomized trials is that whereas AI cannot substitute doctors, it can assist in decision-making, improve patient engagement, and facilitate sustainable health models. In general, AI-based solutions have the potential to be central to reframing primary care towards more individualized, fair, and technology- facilitated health systems.

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