This article presents a novel deep learning-assisted approach for rapid point-of-care testing (POCT), specifically for lateral flow assays (LFA). The proposed method, called TIMESAVER, integrates time-series deep learning with AI-based verification to significantly reduce assay time to as short as 1–2 minutes, surpassing the accuracy of human analysis at 15 minutes. The system is applicable to both infectious diseases and non-infectious biomarkers, including COVID-19, Influenza, Troponin I, and hCG. The TIMESAVER algorithm combines YOLO for object detection, CNN-LSTM for time-series image analysis, and a fully connected (FC) layer for result prediction. It was validated using clinical samples and demonstrated high accuracy in diagnosing various conditions, including infectious diseases and non-infectious biomarkers. The algorithm was tested across multiple LFA models, showing strong performance and adaptability. The TIMESAVER system also enables rapid diagnosis in emergency settings, where timely intervention is critical. The study highlights the potential of AI in enhancing diagnostic efficiency and accuracy in POCT, offering a transformative solution for healthcare professionals and non-experts alike. The results demonstrate that TIMESAVER can achieve high sensitivity and specificity in a very short time, making it a promising tool for rapid, accurate, and affordable diagnostics.This article presents a novel deep learning-assisted approach for rapid point-of-care testing (POCT), specifically for lateral flow assays (LFA). The proposed method, called TIMESAVER, integrates time-series deep learning with AI-based verification to significantly reduce assay time to as short as 1–2 minutes, surpassing the accuracy of human analysis at 15 minutes. The system is applicable to both infectious diseases and non-infectious biomarkers, including COVID-19, Influenza, Troponin I, and hCG. The TIMESAVER algorithm combines YOLO for object detection, CNN-LSTM for time-series image analysis, and a fully connected (FC) layer for result prediction. It was validated using clinical samples and demonstrated high accuracy in diagnosing various conditions, including infectious diseases and non-infectious biomarkers. The algorithm was tested across multiple LFA models, showing strong performance and adaptability. The TIMESAVER system also enables rapid diagnosis in emergency settings, where timely intervention is critical. The study highlights the potential of AI in enhancing diagnostic efficiency and accuracy in POCT, offering a transformative solution for healthcare professionals and non-experts alike. The results demonstrate that TIMESAVER can achieve high sensitivity and specificity in a very short time, making it a promising tool for rapid, accurate, and affordable diagnostics.