Review-Machine Learning-Driven Advances in Electrochemical Sensing: A Horizon Scan

The burgeoning intersection of machine learning (ML) with electrochemical sensing heralds a transformative era in analytical science, pushing the boundaries of what's possible in detecting and quantifying chemical substances with unprecedented precision and efficiency. This convergence has accelerated a number of discoveries, improving electrochemical sensors' sensitivity, selectivity, and ability to comprehend complicated data streams in real-time. Such advancements are crucial across various applications, from monitoring health biomarkers to detecting environmental pollutants and ensuring industrial safety. Yet, this integration is not without its challenges; it necessitates navigating intricate ethical considerations around data use, ensuring robust data privacy measures, and developing specialized software tools that balance accessibility and security. As the field progresses, addressing these challenges head-on is essential for harnessing the full potential of ML-enhanced electrochemical sensing. This review briefly explores these dimensions, spotlighting the significant technological strides, the ethical landscape, and the dynamic interplay between open-source and proprietary software solutions while also casting a forward gaze at the promising future directions of this interdisciplinary venture.