Pandemics　such　as　COVID-19　have　exposed　global　inequalities　in　essential　health　care.　Here,　we　proposed　a　novel　analytics　of　nucleic　acid　amplification　tests　(NAATs)　by　combining　paper　microfluidics　with　deep　learning　and　cloud　computing.　Real-time　amplifications　of　synthesized　SARS-CoV-2　RNA　templates　were　performed　in　paper　devices.　Information　pertained　to　on-chip　reactions　in　time-series　format　were　transmitted　to　cloud　server　on　which　deep　learning　(DL)　models　were　preloaded　for　data　analysis.　DL　models　enable　prediction　of　NAAT　results　using　partly　gathered　real-time　fluorescence　data.　Using　information　provided　by　the　G-channel,　accurate　prediction　can　be　made　as　early　as　9　min,　a　78%　reduction　from　the　conventional　40　min　mark.　Reaction　dynamics　hidden　in　amplification　curves　were　effectively　leveraged.　Positive　and　negative　samples　can　be　unbiasedly　and　automatically　distinguished.　Practical　utility　of　the　approach　was　validated　by　cross-platform　study　using　clinical　datasets.　Predicted　clinical　accuracy,　sensitivity　and　specificity　were　98.6%,　97.6%　and　99.1%.　Not　only　the　approach　reduced　the　need　for　the　use　of　bulky　apparatus,　but　also　provided　intelligent,　distributable　and　robotic　insights　for　NAAT　analysis.　It　set　a　novel　paradigm　for　analyzing　NAATs,　and　can　be　combined　with　the　most　cutting-edge　technologies　in　fields　of　biosensor,　artificial　intelligence　and　cloud　computing　to　facilitate　fundamental　and　clinical　research.　©　2023　Elsevier　Ltd