The article "Role of Machine Learning Assisted Biosensors in Point-of-Care-Testing For Clinical Decisions" by Manish Bhaiyya, Debatta Panigrahi, Prakash Rewatkar, and Hossam Haick explores the integration of Machine Learning (ML) into biosensors for point-of-care testing (PoCT). PoCT has emerged as a crucial component of modern healthcare, offering rapid, low-cost, and simple diagnostic options. The authors discuss how ML algorithms, capable of processing complex biological data, have transformed the accuracy, sensitivity, and speed of diagnostic procedures in various healthcare contexts. The review covers the multifaceted applications of ML models, including classification and regression, highlighting their contributions to improving the diagnostic capabilities of biosensors. It details the roles of ML-assisted electrochemical sensors, lab-on-a-chip sensors, electrochemiluminescence/chemiluminescence sensors, colorimetric sensors, and wearable sensors in diagnosis. The article emphasizes the importance of ML in enhancing the precision and effectiveness of diagnosis, enabling real-time monitoring, individualized treatment, and prompt responses to medical emergencies. The authors also provide a comprehensive overview of emerging trends and future directions in ML-assisted biosensor technology, including the broad classification of ML, the working principles of various ML models, and their practical applications. They discuss the performance metrics for regression and classification in supervised ML, such as Mean Absolute Error (MAE), Mean Squared Error (MSE), Root Mean Squared Error (RMSE), R² score, accuracy, precision, recall, and F1 score. The article further explores the application of ML in different PoCT modalities, such as electrochemical, colorimetric, ECL, lab-on-a-chip, and wearable sensors. It highlights case studies demonstrating the effectiveness of ML-assisted sensors in detecting various biomarkers, improving diagnostic accuracy, and enhancing patient care. The integration of ML with these sensors has led to significant advancements in personalized medicine, real-time monitoring, and efficient medical interventions. Despite the benefits, the article acknowledges operational challenges in clinical settings, such as signal quantification, data processing, and accessibility. Innovative solutions are proposed to address these challenges, leveraging ML's capabilities to optimize sensor performance and ensure reliable and precise results. Overall, the article serves as a valuable reference for researchers, clinicians, and policymakers interested in understanding the emerging landscape of ML in point-of-care diagnostics, highlighting the potential of ML-assisted biosensors to revolutionize healthcare.The article "Role of Machine Learning Assisted Biosensors in Point-of-Care-Testing For Clinical Decisions" by Manish Bhaiyya, Debatta Panigrahi, Prakash Rewatkar, and Hossam Haick explores the integration of Machine Learning (ML) into biosensors for point-of-care testing (PoCT). PoCT has emerged as a crucial component of modern healthcare, offering rapid, low-cost, and simple diagnostic options. The authors discuss how ML algorithms, capable of processing complex biological data, have transformed the accuracy, sensitivity, and speed of diagnostic procedures in various healthcare contexts. The review covers the multifaceted applications of ML models, including classification and regression, highlighting their contributions to improving the diagnostic capabilities of biosensors. It details the roles of ML-assisted electrochemical sensors, lab-on-a-chip sensors, electrochemiluminescence/chemiluminescence sensors, colorimetric sensors, and wearable sensors in diagnosis. The article emphasizes the importance of ML in enhancing the precision and effectiveness of diagnosis, enabling real-time monitoring, individualized treatment, and prompt responses to medical emergencies. The authors also provide a comprehensive overview of emerging trends and future directions in ML-assisted biosensor technology, including the broad classification of ML, the working principles of various ML models, and their practical applications. They discuss the performance metrics for regression and classification in supervised ML, such as Mean Absolute Error (MAE), Mean Squared Error (MSE), Root Mean Squared Error (RMSE), R² score, accuracy, precision, recall, and F1 score. The article further explores the application of ML in different PoCT modalities, such as electrochemical, colorimetric, ECL, lab-on-a-chip, and wearable sensors. It highlights case studies demonstrating the effectiveness of ML-assisted sensors in detecting various biomarkers, improving diagnostic accuracy, and enhancing patient care. The integration of ML with these sensors has led to significant advancements in personalized medicine, real-time monitoring, and efficient medical interventions. Despite the benefits, the article acknowledges operational challenges in clinical settings, such as signal quantification, data processing, and accessibility. Innovative solutions are proposed to address these challenges, leveraging ML's capabilities to optimize sensor performance and ensure reliable and precise results. Overall, the article serves as a valuable reference for researchers, clinicians, and policymakers interested in understanding the emerging landscape of ML in point-of-care diagnostics, highlighting the potential of ML-assisted biosensors to revolutionize healthcare.