Point-of-Care-Testing
(PoCT) has emerged as an essential component
of modern healthcare, providing rapid, low-cost, and simple diagnostic
options. The integration of Machine Learning (ML) into biosensors
has ushered in a new era of innovation in the field of PoCT. This
article investigates the numerous uses and transformational possibilities
of ML in improving biosensors for PoCT. ML algorithms, which are capable
of processing and interpreting complicated biological data, have transformed
the accuracy, sensitivity, and speed of diagnostic procedures in a
variety of healthcare contexts. This review explores the multifaceted
applications of ML models, including classification and regression,
displaying how they contribute to improving the diagnostic capabilities
of biosensors. The roles of ML-assisted electrochemical sensors, lab-on-a-chip
sensors, electrochemiluminescence/chemiluminescence sensors, colorimetric
sensors, and wearable sensors in diagnosis are explained in detail.
Given the increasingly important role of ML in biosensors for PoCT,
this study serves as a valuable reference for researchers, clinicians,
and policymakers interested in understanding the emerging landscape
of ML in point-of-care diagnostics.