Application of artificial intelligence in electrochemical diagnostics for human health

Abstract Electrochemical sensors, detecting biochemical changes through electrical signals, play a pivotal role in point-of-care diagnostics, especially for detecting specific biomarkers for diseases including cancer, diabetes, obesity, cardiovascular conditions, etc. Biosensor based technology faces numerous challenges including signal complexity, noise, data interpretation, etc. These challenges influence the sensitivity and selectivity of the techniques and limit their wider applications. The modern-day miracle, Artificial Intelligence (AI) offers transformative solutions to these challenges. The applications of machine learning (ML) algorithms and AI in electrochemical data analysis have significantly enhanced the sensitivity and specificity of diagnostic methods. The AI-powered systems can easily identify the specific patterns within the electrochemical signals that otherwise remain undetectable by traditional methods. This leads to early detection, personalized treatment plans, and real-time monitoring of diseases. AI also assists in optimizing sensor design, manages large datasets, and improves the performance and reliability of electrochemical diagnostics (ED) devices. Thus, the integration of AI into ED is transforming the healthcare sector by providing faster, more precise, and cost-effective diagnostic solutions.