Different　terms　such　as　trust,　certainty,　and　uncertainty　are　of　great　importance　in　the　real　world　and　play　a　critical　role　in　artificial　intelligence　(AI)　applications.　The　implied　assumption　is　that　the　level　of　trust　in　AI　can　be　measured　in　different　ways.　This　principle　can　be　achieved　by　distinguishing　uncertainties　in　predicting　AI　methods　used　in　medical　studies.　Hence,　it　is　necessary　to　propose　effective　uncertainty　quantification　(UQ)　and　measurement　methods　to　have　trustworthy　AI　(TAI)　clinical　decision　support　systems　(CDSSs).　In　this　study,　we　present　practical　guidelines　for　developing　and　using　UQ　methods　while　applying　various　AI　techniques　for　medical　data　analysis.　©　2015　IEEE.