Purpose　This　study　aimed　to　construct　a　differential　diagnostic　model　to　distinguish　autoimmune　pancreatitis　(AIP)　from　pancreatic　ductal　adenocarcinoma　(PDCA)　using　ultrasound　clinical　features　and　machine　learning　algorithms.Methods　Retrospective　ultrasound　clinical　data　of　patients　with　AIP　and　PDCA　from　three　different　centers　were　used　as　the　training　cohort,　external　validation　cohort　1,　and　external　validation　cohort　2.　Feature　selection　was　conducted　via　variance　filtering　and　LASSO　regression,　followed　by　the　construction　of　a　random　forest　(RF)　model.　The　hyperparameters　were　optimized　in　the　training　cohort,　and　the　final　model　was　evaluated　in　the　external　validation　cohorts.　The　model＇s　performance　was　assessed　using　sensitivity,　specificity,　positive　predictive　value　(PPV),　negative　predictive　value　(NPV),　the　F1　score,　accuracy,　and　area　under　curve(AUC).　The　clinical　application　value　of　the　model　was　clarified　through　a　comparison　between　humans　and　machines.Results　An　RF　model　was　constructed　using　six　features:　Ca　19-9　level,　abdominal　pain,　jaundice,　focal/diffuse-type　AIP,　blood　flow　signals,　and　morphology.　In　external　validation　cohort　1,　the　model＇s　sensitivity,　specificity,　PPV,　NPV,　F1　score,　accuracy,　and　AUC　were　86.0%,　80.0%,　81.0%,　86.0%,　84.0%,　83.0%,　and　89.0%,　respectively;　in　external　validation　cohort　2,　these　values　were　72.0%,　94.0%,　93.0%,　77.0%,　81.0%,　83.0%,　and　91.0%,　respectively.　The　predictive　performance　of　experienced　radiologists　using　clinical　information　and　ultrasound　images　demonstrated　a　sensitivity　of　81%,　specificity　of　79%,　PPV　of　78%,　NPV　of　76%,　F1　score　of　80%,　and　accuracy　of　80%.　For　radiologists　with　intermediate　experience,　the　sensitivity　was　75%,　specificity　was　74%,　PPV　was　73%,　NPV　was　76%,　F1　score　was　74%,　and　accuracy　was　75%.　less　experienced　radiologist　had　a　sensitivity　of　55%,　specificity　of　56%,　PPV　of　62%,　NPV　of　49%,　F1　score　of　58%,　and　accuracy　of　55%.Conclusion　The　RF　model　constructed　using　clinical　ultrasound　features　achieved　diagnostic　levels　comparable　to　those　of　experienced　radiologists　and　can　assist　in　differentiating　AIP　from　PDCA,　potentially　guiding　clinical　practice.