Organs-on-chips　(OoCs)　are　miniature　microfluidic　systems　that　have　arguably　become　a　class　of　advanced　in　vitro　models.　Deep　learning,　as　an　emerging　topic　in　machine　learning,　has　the　ability　to　extract　a　hidden　statistical　relationship　from　the　input　data.　Recently,　these　two　areas　have　become　integrated　to　achieve　synergy　for　accelerating　drug　screening.　This　review　provides　a　brief　description　of　the　basic　concepts　of　deep　learning　used　in　OoCs　and　exemplifies　the　successful　use　cases　for　different　types　of　OoCs.　These　microfluidic　chips　are　of　potential　to　be　assembled　as　highly　potent　human-on-chips　with　complex　physiological　or　pathological　functions.　Finally,　we　discuss　the　future　supply　with　perspectives　and　potential　challenges　in　terms　of　combining　OoCs　and　deep　learning　for　image　processing　and　automation　designs.