The　field　of　hematology　is　highly　progressive　and　dynamic　requiring　researchers　to　commit　significant　amounts　of　time　and　effort　towards　staying　abreast　of　the　most　crucial　research　areas.　As　such,　in　this　work　we　assess　the　potential　of　ChatGPT　for　identifying　research　priorities　within　five　key　topics　in　hematology:　acute　lymphocytic　leukemia,　immunotherapy,　targeted　therapy,　hematopoietic　stem　cell　transplantation,　and　acute　myeloid　leukemia.　After　ChatGPT　was　employed　to　generate　specific　research　questions　in　these　areas,　a　panel　of　seven　experienced　hematologists　independently　reviewed　and　rated　resultant　research　questions　based　on　four　parameters:　relevance,　originality,　clarity,　and　specificity　on　a　scale　of　1　to　5,　with　5　denoting　the　highest　score.　Excitingly,　the　mean　and　median　grades　of　the　four　parameters　were　all　above　4,　indicating　that　the　hematologists　strongly　agreed　that　the　problems　generated　by　ChatGPT　were　generally　highly　specific,　clear,　relevant,　and　original.　As　such,　although　further　work　will　clearly　be　required,　we　suggest　our　current　study　indicates　that　Large　Language　Models　(LLMs),　such　as　ChatGPT,　may　very　well　represent　valuable　new　tools　for　more　efficiently　identifying　and　prioritizing　impactful　research　questions　in　the　field　of　hematology.　©　2023　IEEE.