Intracranial　hemorrhage　poses　a　critical　threat　to　patient　survival,　necessitating　rapid　intervention　to　prevent　devastating　outcomes.　Traditional　segmentation　methods　in　computer-aided　diagnosis　face　significant　challenges　due　to　the　inherent　variability　of　hemorrhage　regions.　Recent　advancements　in　segmentation,　powered　by　foundation　models　and　innovative　utilization　of　prior　knowledge,　have　shown　promise;　however,　existing　methods　predominantly　rely　on　point　or　bounding　box　prompts,　which　often　fail　to　account　for　the　intricate　variability　inherent　in　hemorrhage　presentations.　To　tackle　this　challenge,　we　propose　a　knowledge-　prompted　segment　anything　model　(KP-SAM)　that　integrates　the　specialized　knowledge　of　neurologists　into　the　segmentation　process.　By　collaborating　with　expert　neurologist,　our　method　captures　the　nuanced　characteristics　of　hemorrhage　regions,　effectively　augmenting　the　limitations　of　using　only　points　or　bounding　boxes.　Furthermore,　we　developed　a　diagnostic　support　system　for　intracranial　hemorrhage　at　the　Affiliated　Hospital　of　Qingdao　University.　Leveraging　concise　semantic　information　provided　by　radiologists,　our　system　facilitates　rapid　and　accurate　diagnostic　support　for　clinicians.　Experimental　results　demonstrate　that　our　method　achieves　state-of-the-art　performance　in　real-world　segmentation　tasks　and　significantly　enhances　diagnostic　accuracy　for　neurologists.　This　advancement　not　only　enhances　diagnostic　precision　but　also　highlights　the　transformative　potential　of　integrating　diverse　data　modalities　in　medical　applications.