Alzheimer＇s　disease　(AD)　is　an　irreversible　and　neurodegenerative　disease　that　slowly　impairs　memory　and　neurocognitive　function,　but　the　etiology　of　AD　is　still　unclear.　With　the　explosive　growth　of　electronic　health　data,　the　application　of　artificial　intelligence　(Al)　in　the　healthcare　setting　provides　excellent　potential　for　exploring　etiology　and　personalized　treatment　approaches,　and　improving　the　disease＇s　diagnostic　and　prognostic　outcome.　This　paper　first　briefly　introduces　Al　technologies　and　applications　in　medicine,　and　then　presents　a　comprehensive　review　of　Al　in　AD.　In　simple,　it　includes　etiology　discovery　based　on　genetic　data,　computer-aided　diagnosis　(CAD),　computer-aided　prognosis　(CAP)　of　AD　using　multi-modality　data　(genetic,　neuroimaging　and　linguistic　data),　and　pharmacological　or　non-pharmacological　approaches　for　treating　AD.　Later,　some　popular　publicly　available　AD　datasets　are　introduced,　which　are　important　for　advancing　Al　technologies　in　AD　analysis.　Finally,　core　research　challenges　and　future　research　directions　are　discussed.