Chinese　medical　named　entity　recognition　(NER)　is　a　fundamental　task　in　Chinese　medical　natural　language　processing,　aiming　to　recognize　Chinese　medical　entities　within　unstructured　medical　texts.　However,　it　poses　significant　challenges　mainly　due　to　the　extensive　usage　of　medical　terms　in　Chinese　medical　texts.　Although　previous　studies　have　made　attempts　to　incorporate　lexical　or　radical　knowledge　in　order　to　improve　the　comprehension　of　medical　texts,　these　studies　either　focus　solely　on　one　of　these　aspects　or　utilize　a　basic　concatenation　operation　to　combine　these　features,　which　fails　to　fully　utilize　the　potential　of　lexical　and　radical　knowledge.　In　this　paper,　we　propose　a　novel　Cascaded　LAttice-and-Radical　Transformer　(CLART)　network　to　exploit　both　lexical　and　radical　information　for　Chinese　medical　NER.　Specifically,　given　a　sentence,　a　medical　lexicon,　and　a　radical　dictionary,　we　first　construct　a　flat　lattice　(i.e.,　character-word　sequence)　for　the　sentence　and　radical　components　of　each　Chinese　character　through　word　matching　and　radical　parsing,　respectively.　We　then　employ　a　lattice　Transformer　module　to　capture　the　dense　interactions　between　characters　and　matched　words,　facilitating　the　enhanced　utilization　of　lexical　knowledge.　Subsequently,　we　design　a　radical　Transformer　module　to　model　the　dense　interactions　between　the　lattice　and　radical　features,　facilitating　better　fusion　of　the　lexical　and　radical　knowledge.　Finally,　we　feed　the　updated　lattice-and-radical-aware　character　representations　into　a　Conditional　Random　Fields　(CRF)　decoder　to　obtain　the　predicted　labels.　Experimental　results　conducted　on　two　publicly　available　Chinese　medical　NER　datasets　show　the　effectiveness　of　the　proposed　method.　©　2023　The　Authors