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　dy-namics　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.