Enhanced　External　Counterpulsation　(EECP),　as　a　non-invasive,　cost-effective,　and　efficient　adjunctive　circulatory　technique,　has　been　widely　applied　in　in　the　cardiovascular　field.　Numerous　studies　and　clinical　observations　have　confirmed　the　obvious　advantages　of　EECP　in　promoting　blood　flow　perfusion　to　vital　organs　such　as　the　heart,　brain,　and　kidneys.　However,　many　potential　mechanisms　of　EECP　remain　insufficiently　validated,　necessitating　researchers　to　dedicate　substantial　time　and　effort　to　in-depth　investigations.　In　this　work,　large　language　models　(such　as　ChatGPT　and　Ernie　Bot)　were　used　to　identify　top　research　priorities　in　five　key　topics　in　the　field　of　EECP:　mechanisms,　device　improvements,　cardiovascular　applications,　neurological　applications,　and　other　applications.　After　generating　specific　research　priorities　in　each　domain　through　language　models,　a　panel　of　nine　experienced　EECP　experts　was　invited　to　independently　evaluate　and　score　them　based　on　four　parameters:　relevance,　originality,　clarity,　and　specificity.　Notably,　high　average　and　median　scores　for　these　evaluation　parameters　were　obtained,　indicating　a　strong　endorsement　from　experts　in　the　EECP　field.　This　study　preliminarily　suggests　that　large　language　models　like　ChatGPT　and　Ernie　Bot　could　serve　as　powerful　tools　for　identifying　and　prioritizing　research　priorities　in　the　EECP　domain.