Laser　speckle　contrast　blood　flow　imaging　(LSCI)　analyzes　the　spatial　or　temporal　statistical　characteristics　of　laser　speckle　patterns　to　obtain　a　signal　proportional　to　blood　flow.　As　it　can　achieve　real　time　blood　flow　imaging　at　high　resolution　with　simple　instrumental　setup,　LSCI　has　been　widely　applied　in　both　clinic　and　research.　The　lack　of　quantitative　blood　flow　measurement　is　the　primary　limitation.　Efforts　have　been　made　to　optimize　LSCI　from　the　aspects　of　measurement　accuracy　and　linearity　such　as　multi-exposure　laser　speckle　contrast　imaging　(MELSCI)　and　AI-based　(artificial　intelligence)　LSCI.　This　paper　reviews　LSCI　in　terms　of　basic　principles,　system　development　and　quantitative　measurement　of　LSCI.　The　application　of　machine　learning　in　LSCI　is　discussed　in　detail.　By　comparing　the　estimated　perfusion　results　of　LSCI,　MELSCI　and　LDI　(laser　Doppler　blood　flow　imaging),　we　propose　that　using　machine　learning　to　correct　LSCI　to　MELSCI　has　great　potential　for　improving　measurement　linearity　while　retaining　system　simplicity.　©　2022　IEEE.