Disease　diagnosis　represents　a　critical　and　arduous　endeavor　within　the　medical　field.　Artificial　intelligence　(AI)　techniques,　spanning　from　machine　learning　and　deep　learning　to　large　model　paradigms,　stand　poised　to　significantly　augment　physicians　in　rendering　more　evidence-based　decisions,　thus　presenting　a　pioneering　solution　for　clinical　practice.　Traditionally,　the　amalgamation　of　diverse　medical　data　modalities　(e.g.,　image,　text,　speech,　genetic　data,　physiological　signals)　is　imperative　to　facilitate　a　comprehensive　disease　analysis,　a　topic　of　burgeoning　interest　among　both　researchers　and　clinicians　in　recent　times.　Hence,　there　exists　a　pressing　need　to　synthesize　the　latest　strides　in　multi-modal　data　and　AI　technologies　in　the　realm　of　medical　diagnosis.　In　this　paper,　we　narrow　our　focus　to　five　specific　disorders　(Alzheimer＇s　disease,　breast　cancer,　depression,　heart　disease,　epilepsy),　elucidating　advanced　endeavors　in　their　diagnosis　and　treatment　through　the　lens　of　artificial　intelligence.　Our　survey　not　only　delineates　detailed　diagnostic　methodologies　across　varying　modalities　but　also　underscores　commonly　utilized　public　datasets,　the　intricacies　of　feature　engineering,　prevalent　classification　models,　and　envisaged　challenges　for　future　endeavors.　In　essence,　our　research　endeavors　to　contribute　to　the　advancement　of　diagnostic　methodologies,　furnishing　invaluable　insights　for　clinical　decision　making.