Artificial　intelligence　(AI)　technology　is　widely　applied　in　different　medical　fields,　including　the　diagnosis　of　various　diseases　on　the　basis　of　facial　phenotypes,　but　there　is　no　evaluation　or　quantitative　synthesis　regarding　the　performance　of　artificial　intelligence.　Here,　for　the　first　time,　we　summarized　and　quantitatively　analyzed　studies　on　the　diagnosis　of　heterogeneous　diseases　on　the　basis　on　facial　features.　In　pooled　data　from　20　systematically　identified　studies　involving　7　single　diseases　and　12,557　subjects,　quantitative　random-effects　models　revealed　a　pooled　sensitivity　of　89%　(95%　CI　82%　to　93%)　and　a　pooled　specificity　of　92%　(95%　CI　87%　to　95%).　A　new　index,　the　facial　recognition　intensity　(FRI),　was　established　to　describe　the　complexity　of　the　association　of　diseases　with　facial　phenotypes.　Meta-regression　revealed　the　important　contribution　of　FRI　to　heterogeneous　diagnostic　accuracy　(p　=　0.021),　and　a　similar　result　was　found　in　subgroup　analyses　(p　=　0.003).　An　appropriate　increase　in　the　training　size　and　the　use　of　deep　learning　models　helped　to　improve　the　diagnostic　accuracy　for　diseases　with　low　FRI,　although　no　statistically　significant　association　was　found　between　accuracy　and　photographic　resolution,　training　size,　AI　architecture,　and　number　of　diseases.　In　addition,　a　novel　hypothesis　is　proposed　for　universal　rules　in　AI　performance,　providing　a　new　idea　that　could　be　explored　in　other　AI　applications.