{"id":"https://openalex.org/W3111351897","doi":"https://doi.org/10.1145/3429889.3429938","title":"Artificial Intelligence in Medicine in the United States, China and India","display_name":"Artificial Intelligence in Medicine in the United States, China and India","publication_year":2020,"publication_date":"2020-09-11","ids":{"openalex":"https://openalex.org/W3111351897","doi":"https://doi.org/10.1145/3429889.3429938","mag":"3111351897"},"language":"en","primary_location":{"id":"doi:10.1145/3429889.3429938","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3429889.3429938","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 1st International Symposium on Artificial Intelligence in Medical Sciences","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5100359213","display_name":"Juan Chen","orcid":"https://orcid.org/0000-0002-5407-8239"},"institutions":[{"id":"https://openalex.org/I200296433","display_name":"Chinese Academy of Medical Sciences & Peking Union Medical College","ror":"https://ror.org/02drdmm93","country_code":"CN","type":"education","lineage":["https://openalex.org/I200296433"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Juan Chen","raw_affiliation_strings":["Institute of Medical Information/Medical Library, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Institute of Medical Information/Medical Library, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China","institution_ids":["https://openalex.org/I200296433"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5076757832","display_name":"Yan Lu","orcid":"https://orcid.org/0000-0002-7718-7999"},"institutions":[{"id":"https://openalex.org/I200296433","display_name":"Chinese Academy of Medical Sciences & Peking Union Medical College","ror":"https://ror.org/02drdmm93","country_code":"CN","type":"education","lineage":["https://openalex.org/I200296433"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yan Lu","raw_affiliation_strings":["Institute of Medical Information/Medical Library, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Institute of Medical Information/Medical Library, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China","institution_ids":["https://openalex.org/I200296433"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100458346","display_name":"Ting Zhang","orcid":"https://orcid.org/0000-0003-0035-0011"},"institutions":[{"id":"https://openalex.org/I200296433","display_name":"Chinese Academy of Medical Sciences & Peking Union Medical College","ror":"https://ror.org/02drdmm93","country_code":"CN","type":"education","lineage":["https://openalex.org/I200296433"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Ting Zhang","raw_affiliation_strings":["Institute of Medical Information/Medical Library, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Institute of Medical Information/Medical Library, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China","institution_ids":["https://openalex.org/I200296433"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5053075196","display_name":"Zhaolian Ouyang","orcid":"https://orcid.org/0000-0002-2220-3007"},"institutions":[{"id":"https://openalex.org/I200296433","display_name":"Chinese Academy of Medical Sciences & Peking Union Medical College","ror":"https://ror.org/02drdmm93","country_code":"CN","type":"education","lineage":["https://openalex.org/I200296433"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhaolian Ouyang","raw_affiliation_strings":["Institute of Medical Information/Medical Library, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Institute of Medical Information/Medical Library, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China","institution_ids":["https://openalex.org/I200296433"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5100359213"],"corresponding_institution_ids":["https://openalex.org/I200296433"],"apc_list":null,"apc_paid":null,"fwci":0.1288,"has_fulltext":false,"cited_by_count":4,"citation_normalized_percentile":{"value":0.59310345,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":95},"biblio":{"volume":null,"issue":null,"first_page":"257","last_page":"264"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11636","display_name":"Artificial Intelligence in Healthcare and Education","score":0.9970999956130981,"subfield":{"id":"https://openalex.org/subfields/2718","display_name":"Health Informatics"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}},"topics":[{"id":"https://openalex.org/T11636","display_name":"Artificial Intelligence in Healthcare and Education","score":0.9970999956130981,"subfield":{"id":"https://openalex.org/subfields/2718","display_name":"Health Informatics"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}},{"id":"https://openalex.org/T12422","display_name":"Radiomics and Machine Learning in Medical Imaging","score":0.986299991607666,"subfield":{"id":"https://openalex.org/subfields/2741","display_name":"Radiology, Nuclear Medicine and Imaging"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}},{"id":"https://openalex.org/T10102","display_name":"scientometrics and bibliometrics research","score":0.9072999954223633,"subfield":{"id":"https://openalex.org/subfields/1804","display_name":"Statistics, Probability and Uncertainty"},"field":{"id":"https://openalex.org/fields/18","display_name":"Decision Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/china","display_name":"China","score":0.7837103605270386},{"id":"https://openalex.org/keywords/web-of-science","display_name":"Web of science","score":0.5730587840080261},{"id":"https://openalex.org/keywords/bibliometrics","display_name":"Bibliometrics","score":0.49862122535705566},{"id":"https://openalex.org/keywords/library-science","display_name":"Library science","score":0.48737794160842896},{"id":"https://openalex.org/keywords/history","display_name":"History","score":0.3328538239002228},{"id":"https://openalex.org/keywords/geography","display_name":"Geography","score":0.32415056228637695},{"id":"https://openalex.org/keywords/medline","display_name":"MEDLINE","score":0.2975538372993469},{"id":"https://openalex.org/keywords/political-science","display_name":"Political science","score":0.2822083532810211},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.2257394790649414},{"id":"https://openalex.org/keywords/law","display_name":"Law","score":0.105092853307724}],"concepts":[{"id":"https://openalex.org/C191935318","wikidata":"https://www.wikidata.org/wiki/Q148","display_name":"China","level":2,"score":0.7837103605270386},{"id":"https://openalex.org/C3020774429","wikidata":"https://www.wikidata.org/wiki/Q1201886","display_name":"Web of science","level":3,"score":0.5730587840080261},{"id":"https://openalex.org/C178315738","wikidata":"https://www.wikidata.org/wiki/Q603441","display_name":"Bibliometrics","level":2,"score":0.49862122535705566},{"id":"https://openalex.org/C161191863","wikidata":"https://www.wikidata.org/wiki/Q199655","display_name":"Library science","level":1,"score":0.48737794160842896},{"id":"https://openalex.org/C95457728","wikidata":"https://www.wikidata.org/wiki/Q309","display_name":"History","level":0,"score":0.3328538239002228},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.32415056228637695},{"id":"https://openalex.org/C2779473830","wikidata":"https://www.wikidata.org/wiki/Q1540899","display_name":"MEDLINE","level":2,"score":0.2975538372993469},{"id":"https://openalex.org/C17744445","wikidata":"https://www.wikidata.org/wiki/Q36442","display_name":"Political science","level":0,"score":0.2822083532810211},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.2257394790649414},{"id":"https://openalex.org/C199539241","wikidata":"https://www.wikidata.org/wiki/Q7748","display_name":"Law","level":1,"score":0.105092853307724}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3429889.3429938","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3429889.3429938","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 1st International Symposium on Artificial Intelligence in Medical Sciences","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Partnerships for the goals","id":"https://metadata.un.org/sdg/17","score":0.5299999713897705}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":45,"referenced_works":["https://openalex.org/W1536573696","https://openalex.org/W1681604381","https://openalex.org/W1754157057","https://openalex.org/W1767272795","https://openalex.org/W1859149515","https://openalex.org/W1892595029","https://openalex.org/W2005792051","https://openalex.org/W2050185763","https://openalex.org/W2063101309","https://openalex.org/W2111649209","https://openalex.org/W2150220236","https://openalex.org/W2168218485","https://openalex.org/W2178471959","https://openalex.org/W2437701420","https://openalex.org/W2486654687","https://openalex.org/W2493070718","https://openalex.org/W2534299759","https://openalex.org/W2551040224","https://openalex.org/W2563222081","https://openalex.org/W2574038793","https://openalex.org/W2580906503","https://openalex.org/W2596872392","https://openalex.org/W2617669016","https://openalex.org/W2766518316","https://openalex.org/W2777186991","https://openalex.org/W2793968687","https://openalex.org/W2808477779","https://openalex.org/W2886599162","https://openalex.org/W2894996849","https://openalex.org/W2898309196","https://openalex.org/W2899717480","https://openalex.org/W2901388302","https://openalex.org/W2903010436","https://openalex.org/W2904682781","https://openalex.org/W2905167757","https://openalex.org/W2911398689","https://openalex.org/W2911462778","https://openalex.org/W2918816719","https://openalex.org/W2921613140","https://openalex.org/W2922380304","https://openalex.org/W2935375569","https://openalex.org/W2941658346","https://openalex.org/W2970877963","https://openalex.org/W3100572281","https://openalex.org/W4234432301"],"related_works":["https://openalex.org/W4386031268","https://openalex.org/W2125982917","https://openalex.org/W2294191624","https://openalex.org/W2608652318","https://openalex.org/W3048743861","https://openalex.org/W4382048646","https://openalex.org/W2367205687","https://openalex.org/W3080420152","https://openalex.org/W3032219833","https://openalex.org/W2150273412"],"abstract_inverted_index":{"Objective:":[0],"To":[1],"compare":[2],"the":[3,13,49,53,59,70,81,85,90,96,103,110,145,161,192,222,234,240,251,262,318,331,346,353,359,392,397,409,413,426,435],"development":[4],"status":[5],"of":[6,42,52,61,72,80,83,87,92,109,147,163,194,224,233,242,255,297,355,364],"artificial":[7],"intelligence":[8],"(AI)":[9],"in":[10,27,58,63,113,127,144,149,179,209,239,276,357,367,408,425],"medicine":[11,28,64],"among":[12],"United":[14],"States":[15],"(US),":[16],"China":[17,55,170,236,308,348,389],"and":[18,48,56,95,106,124,140,160,175,191,205,221,237,247,268,291,294,320,342,349,369,383,428],"India":[19,57,200,238,321,411],"with":[20,67,151,182,212,289,307,361,386,400,417,429],"bibliometric":[21],"analysis.":[22],"Methods:":[23],"Articles":[24],"involving":[25],"AI":[26,62,148,356,368],"published":[29,137],"from":[30,40],"2015":[31,131],"to":[32,132,157,166,188,197,218,227,324,328,336,434],"2019":[33],"were":[34,65,117,244,258,272,339],"retrieved":[35],"on":[36,304,312],"March":[37],"30,":[38],"2020":[39],"Web":[41],"Science":[43],"Core":[44],"Collection.":[45],"The":[46,134,230,300],"country-level":[47],"institution-level":[50],"performance":[51],"US,":[54,235,347,360,427],"field":[60,146,354],"compared":[66,118],"indicators":[68,257],"including":[69],"amount":[71,82,86],"papers,":[73,84,89],"5-year":[74,231],"Compound":[75],"Annual":[76],"Growth":[77],"Rate":[78],"(CAGR)":[79],"highly-cited":[88,93,142,164,177,195,207,225],"proportion":[91,162,193,223],"papers":[94,139,143,165,174,178,196,204,208,226],"average":[97,153,184,214,332],"citations":[98,154,185,215],"per":[99,155,186,216,334],"paper.":[100],"In":[101,352],"addition,":[102],"research":[104,274,430],"hotspots":[105,275,431],"international":[107,329],"cooperation":[108],"three":[111,301],"countries":[112,302,338],"recent":[114],"5":[115],"years":[116],"by":[119],"conducting":[120],"keywords":[121],"co-occurrence":[122],"analysis":[123,126],"co-authorship":[125],"VOSviewer.":[128],"Results:":[129],"From":[130],"2019,":[133],"US":[135,263,319],"has":[136,171,201],"7838":[138],"154":[141],"medicine,":[150,358],"an":[152,183,213],"paper":[156,187,217,335],"be":[158,167,189,198,219,228],"9.3,":[159],"2.0":[168],"%.":[169],"output":[172,202],"6635":[173],"73":[176],"this":[180,210,277],"field,":[181,211,278],"5.3,":[190],"1.1%.":[199],"3895":[203],"22":[206],"3.6,":[220],"0.6%.":[229],"CAGR":[232],"period":[241],"2015~2019":[243],"16.0%,":[245],"25.4%":[246],"2.4%,":[248],"respectively.":[249],"At":[250],"institutional":[252],"level,":[253,399],"most":[254],"these":[256],"significantly":[259],"better":[260],"for":[261,266,345],"institutions":[264,366,402,419],"than":[265,406,423],"Chinese":[267],"Indian":[269],"ones.":[270],"There":[271],"four":[273],"namely":[279],"medical":[280,370],"imaging":[281],"technology,":[282],"health":[283,313],"big":[284,314],"data":[285,315],"mining,":[286,316],"disease":[287],"prediction":[288],"biomarkers":[290],"genetic":[292],"information,":[293],"early":[295],"diagnosis":[296],"neurological":[298],"disease.":[299],"focused":[303],"different":[305],"hotspots,":[306],"focusing":[309],"relatively":[310],"less":[311,404,421],"while":[317],"being":[322],"complementary":[323,433],"each":[325],"other.":[326],"As":[327],"cooperation,":[330],"links":[333],"other":[337,387],"0.60,":[340],"0.40":[341],"0.20,":[343],"respectively,":[344],"India.":[350],"Conclusions:":[351],"a":[362,374],"number":[363],"competitive":[365],"researches,":[371],"is":[372,390,412],"taking":[373,391],"definitely":[375],"leading":[376,394],"role,":[377],"having":[378],"conducted":[379],"many":[380],"innovative":[381],"researches":[382],"cooperated":[384],"extensively":[385],"countries.":[388],"second":[393],"role":[395],"at":[396],"country":[398],"top":[401,418],"somewhat":[403],"productive":[405,415,422],"those":[407,424],"US.":[410,436],"third":[414],"country,":[416],"obvious":[420],"exactly":[432]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":1},{"year":2022,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}