Smart Diagnostics with Biosensors and Machine Learning: Developments, Applications, and Hurdles

Biosensors are quickly developing as important instruments for biomolecular and environmental analysis, as well as food safety. Machine learning (ML) and artificial intelligence (AI) are powerful tools that can be utilized to enhance the sensitivity, specificity, and interpretability of biosensors. This review looks at the integration of ML with different biosensor qualities, electrochemical sensors, portable electronics, surface-enhanced Raman spectroscopy (SERS), fluorescence, and colorimetric devices. Summarized key algorithms applied in biosensing, such as support vector machines (SVM), convolutional neural networks (CNN), recurrent neural networks (RNN), and ensemble methods, are also included amongst other information, such as the following obstacles: noisy signals, limited datasets, and reproducibility. Moreover, the methodology of review, key summary details, and critical analysis of the involvement of ML in biosensing. Lastly, views on rising trends like IoT-enabled biosensor networks, multi-sensor data fusion, and clinical translation are included.