Purpose　The　application　of　artificial　intelligence　(AI)　in　healthcare　has　seen　widespread　implementation,　with　numerous　studies　highlighting　the　development　of　robust　algorithms.　However,　limited　attention　has　been　given　to　the　secure　utilization　of　raw　data　for　medical　model　training,　and　its　subsequent　impact　on　clinical　decision-making　and　real-world　applications.　This　study　aims　to　assess　the　feasibility　and　effectiveness　of　an　advanced　diagnostic　model　that　integrates　blockchain　technology　and　AI　for　the　identification　of　tibial　plateau　fractures　(TPFs)　in　emergency　settings.　Method　In　this　study,　blockchain　technology　was　utilized　to　construct　a　distributed　database　for　trauma　orthopedics,　images　collected　from　three　independent　hospitals　for　model　training,　testing,　and　internal　validation.　Then,　a　distributed　network　combining　blockchain　and　deep　learning　was　developed　for　the　detection　of　TPFs,　with　model　parameters　aggregated　across　multiple　nodes　to　enhance　accuracy.　The　model＇s　performance　was　comprehensively　evaluated　using　metrics　including　accuracy,　sensitivity,　specificity,　F1　score,　and　the　area　under　the　receiver　operating　characteristic　curve　(AUC).　In　addition,　the　performance　of　the　centralized　model,　the　distributed　AI　model,　clinical　orthopedic　attending　physicians,　and　AI-assisted　attending　physicians　was　tested　on　an　external　validation　dataset.　Results　In　the　testing　set,　the　accuracy　of　our　distributed　model　was　0.9603　[95%　CI　(0.9598,　0.9605)]　and　the　AUC　was　0.9911　[95%　CI　(0.9893,　0.9915)]　for　TPF　detection.　In　the　external　validation　set,　the　accuracy　reached　0.9636　[95%　CI　(0.9388,　0.9762)],　was　slightly　higher　than　that　of　the　centralized　YOLOv8n　model　at　0.9632　[95%　CI　(0.9387,　0.9755)]　(p　＞　0.05),　and　exceeded　the　orthopedic　physician　at　0.9291　[95%　CI　(0.9002,　0.9482)]　and　radiology　attending　physician　at　0.9175　[95%　CI　(0.8891,　0.9393)],　with　a　statistically　significant　difference　(p　＜　0.05).　Additionally,　the　centralized　model　(4.99　+/-　0.01　min)　had　shorter　diagnosis　times　compared　to　the　orthopedic　attending　physician　(25.45　+/-　1.92　min)　and　the　radiology　attending　physician　(26.21　+/-　1.20　min),　with　a　statistically　significant　difference　(p　＜　0.05).　Conclusion　The　model　based　on　the　integration　of　blockchain　technology　and　AI　can　realize　safe,　collaborative,　and　convenient　assisted　diagnosis　of　TPF.　Through　the　aggregation　of　training　parameters　by　decentralized　algorithms,　it　can　achieve　model　construction　without　data　leaving　the　hospital　and　may　exert　clinical　application　value　in　the　emergency　settings.