{"id":"https://openalex.org/W7225626643","doi":"https://doi.org/10.1007/s10791-026-10704-x","title":"Asymmetric verification for detecting hallucinated claims in clinical information extraction","display_name":"Asymmetric verification for detecting hallucinated claims in clinical information extraction","publication_year":2026,"publication_date":"2026-11-09","ids":{"openalex":"https://openalex.org/W7225626643","doi":"https://doi.org/10.1007/s10791-026-10704-x"},"language":"en","primary_location":{"id":"doi:10.1007/s10791-026-10704-x","doi":"https://doi.org/10.1007/s10791-026-10704-x","is_oa":true,"landing_page_url":"https://doi.org/10.1007/s10791-026-10704-x","pdf_url":"https://link.springer.com/content/pdf/10.1007/s10791-026-10704-x.pdf","source":{"id":"https://openalex.org/S5407036663","display_name":"Discover Computing","issn_l":"2948-2992","issn":["2948-2992"],"is_oa":true,"is_in_doaj":true,"is_core":false,"listed_in":["ccf-c","doaj","dongbi-d","jufo-3","norway-2","russia-white-list-1"],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":"cc-by-nc-nd","license_id":"https://openalex.org/licenses/cc-by-nc-nd","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Discover Computing","raw_type":"journal-article"},"volume":null,"type":"article","is_traditional":true,"indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://link.springer.com/content/pdf/10.1007/s10791-026-10704-x.pdf","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5143847610","display_name":"Hemachandran Babu","orcid":"https://orcid.org/0009-0002-7560-2551","observed_orcids":["https://orcid.org/0009-0002-7560-2551"]},"institutions":[{"id":"https://openalex.org/I145286018","display_name":"SRM Institute of Science and Technology","ror":"https://ror.org/050113w36","country_code":"IN","type":"education","lineage":["https://openalex.org/I145286018"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Hemachandran B","raw_affiliation_strings":["Department of Data Science and Business Systems, School of Computing, SRM Institute of Science and Technology, Chennai, India"],"raw_orcid":"https://orcid.org/0009-0002-7560-2551","affiliations":[{"raw_affiliation_string":"Department of Data Science and Business Systems, School of Computing, SRM Institute of Science and Technology, Chennai, India","institution_ids":["https://openalex.org/I145286018"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5143866633","display_name":"Priyadarsini K","orcid":"https://orcid.org/0000-0003-2374-3184","observed_orcids":["https://orcid.org/0000-0003-2374-3184"]},"institutions":[{"id":"https://openalex.org/I145286018","display_name":"SRM Institute of Science and Technology","ror":"https://ror.org/050113w36","country_code":"IN","type":"education","lineage":["https://openalex.org/I145286018"]}],"countries":["IN"],"is_corresponding":true,"raw_author_name":"Priyadarsini K","raw_affiliation_strings":["Department of Data Science and Business Systems, School of Computing, SRM Institute of Science and Technology, Chennai, India"],"raw_orcid":"https://orcid.org/0000-0003-2374-3184","affiliations":[{"raw_affiliation_string":"Department of Data Science and Business Systems, School of Computing, SRM Institute of Science and Technology, Chennai, India","institution_ids":["https://openalex.org/I145286018"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":["https://openalex.org/A5143866633"],"corresponding_institution_ids":["https://openalex.org/I145286018"],"apc_list":{"value":1520,"currency":"USD","value_usd":1520},"apc_paid":{"value":1520,"currency":"USD","value_usd":1520},"fwci":0.0,"has_fulltext":true,"cited_by_count":0,"citation_normalized_percentile":{"value":0.0,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"29","issue":"1","first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11710","display_name":"Biomedical Text Mining and Ontologies","score":0.2883000075817108,"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/T11710","display_name":"Biomedical Text Mining and Ontologies","score":0.2883000075817108,"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/T10028","display_name":"Topic Modeling","score":0.19949999451637268,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T13702","display_name":"Machine Learning in Healthcare","score":0.14180000126361847,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/hallucination-detection","display_name":"hallucination detection","score":0.9539999961853027},{"id":"https://openalex.org/keywords/clinical-information-extraction","display_name":"clinical information extraction","score":0.7850000262260437},{"id":"https://openalex.org/keywords/large-language-models","display_name":"large language models","score":0.75},{"id":"https://openalex.org/keywords/retrieval-augmented-generation","display_name":"retrieval-augmented generation","score":0.8429999947547913},{"id":"https://openalex.org/keywords/clinical-notes","display_name":"clinical notes","score":0.5929999947547913},{"id":"https://openalex.org/keywords/adversarial-stress-testing","display_name":"adversarial stress testing","score":0.7879999876022339},{"id":"https://openalex.org/keywords/negation-detection","display_name":"negation detection","score":0.4410000145435333}],"concepts":[{"id":"https://openalex.org/C2911011789","wikidata":"https://www.wikidata.org/wiki/Q130741","display_name":"Hallucinating","level":2,"score":0.9126999974250793},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5949000120162964},{"id":"https://openalex.org/C2781181686","wikidata":"https://www.wikidata.org/wiki/Q4226068","display_name":"Coherence (philosophical gambling strategy)","level":2,"score":0.5210000276565552},{"id":"https://openalex.org/C100660578","wikidata":"https://www.wikidata.org/wiki/Q18733","display_name":"Recall","level":2,"score":0.4814000129699707},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.4731999933719635},{"id":"https://openalex.org/C37736160","wikidata":"https://www.wikidata.org/wiki/Q1801315","display_name":"Adversarial system","level":2,"score":0.4634999930858612},{"id":"https://openalex.org/C148220186","wikidata":"https://www.wikidata.org/wiki/Q7111912","display_name":"Outcome (game theory)","level":2,"score":0.45350000262260437},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.4426000118255615},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3582000136375427},{"id":"https://openalex.org/C15744967","wikidata":"https://www.wikidata.org/wiki/Q9418","display_name":"Psychology","level":0,"score":0.3522999882698059},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.33340001106262207},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.32919999957084656},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.3147999942302704},{"id":"https://openalex.org/C2777267654","wikidata":"https://www.wikidata.org/wiki/Q3519023","display_name":"Test (biology)","level":2,"score":0.2976999878883362},{"id":"https://openalex.org/C148524875","wikidata":"https://www.wikidata.org/wiki/Q6975395","display_name":"F1 score","level":2,"score":0.287200003862381},{"id":"https://openalex.org/C81669768","wikidata":"https://www.wikidata.org/wiki/Q2359161","display_name":"Precision and recall","level":2,"score":0.28279998898506165},{"id":"https://openalex.org/C195807954","wikidata":"https://www.wikidata.org/wiki/Q1662562","display_name":"Information extraction","level":2,"score":0.2808000147342682},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.2759999930858612}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1007/s10791-026-10704-x","doi":"https://doi.org/10.1007/s10791-026-10704-x","is_oa":true,"landing_page_url":"https://doi.org/10.1007/s10791-026-10704-x","pdf_url":"https://link.springer.com/content/pdf/10.1007/s10791-026-10704-x.pdf","source":{"id":"https://openalex.org/S5407036663","display_name":"Discover Computing","issn_l":"2948-2992","issn":["2948-2992"],"is_oa":true,"is_in_doaj":true,"is_core":false,"listed_in":["ccf-c","doaj","dongbi-d","jufo-3","norway-2","russia-white-list-1"],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":"cc-by-nc-nd","license_id":"https://openalex.org/licenses/cc-by-nc-nd","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Discover Computing","raw_type":"journal-article"}],"best_oa_location":{"id":"doi:10.1007/s10791-026-10704-x","doi":"https://doi.org/10.1007/s10791-026-10704-x","is_oa":true,"landing_page_url":"https://doi.org/10.1007/s10791-026-10704-x","pdf_url":"https://link.springer.com/content/pdf/10.1007/s10791-026-10704-x.pdf","source":{"id":"https://openalex.org/S5407036663","display_name":"Discover Computing","issn_l":"2948-2992","issn":["2948-2992"],"is_oa":true,"is_in_doaj":true,"is_core":false,"listed_in":["ccf-c","doaj","dongbi-d","jufo-3","norway-2","russia-white-list-1"],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":"cc-by-nc-nd","license_id":"https://openalex.org/licenses/cc-by-nc-nd","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Discover Computing","raw_type":"journal-article"},"sustainable_development_goals":[{"display_name":"Good health and well-being","score":0.5683000087738037,"id":"https://metadata.un.org/sdg/3"}],"study_designs":[],"awards":[],"funders":[],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W7225626643.pdf","grobid_xml":"https://content.openalex.org/works/W7225626643.grobid-xml"},"referenced_works_count":18,"referenced_works":["https://openalex.org/W2159583324","https://openalex.org/W2911489562","https://openalex.org/W4309674289","https://openalex.org/W4389894040","https://openalex.org/W4391135101","https://openalex.org/W4400771582","https://openalex.org/W4404138521","https://openalex.org/W4404518047","https://openalex.org/W4404534210","https://openalex.org/W4406152263","https://openalex.org/W4407776428","https://openalex.org/W4410343193","https://openalex.org/W4411203672","https://openalex.org/W4415781674","https://openalex.org/W4416037066","https://openalex.org/W4416571241","https://openalex.org/W7123968264","https://openalex.org/W7151910642"],"related_works":["https://openalex.org/W7129092247","https://openalex.org/W7222311225","https://openalex.org/W7204466892","https://openalex.org/W7207592016","https://openalex.org/W7223558991","https://openalex.org/W7166776119","https://openalex.org/W4406475488","https://openalex.org/W4412163797","https://openalex.org/W7167629678","https://openalex.org/W4412853478"],"abstract_inverted_index":{"Although":[0],"large":[1],"language":[2],"models":[3],"are":[4,247,269],"capable":[5],"of":[6,49,69,75,84,104,112,120,129,136,140,146,157,172,219],"turning":[7],"clinical":[8,20,70,265],"notes":[9],"into":[10],"structured":[11],"claims":[12,32,116,161],"they":[13,298,312],"might":[14],"also":[15,152],"create,":[16],"modify":[17],"or":[18,282],"exaggerate":[19],"details.":[21],"This":[22],"study":[23],"looks":[24],"at":[25],"a":[26,134,138,288,295],"simplified":[27],"asymmetric":[28,88,303],"verification":[29,290,304,309],"method":[30,259],"for":[31,201,205],"that":[33,72,232,244,268,302],"have":[34],"already":[35],"been":[36],"written;":[37],"it":[38],"does":[39],"not":[40,178,236,239,300],"assess":[41],"the":[42,50,57,64,67,76,81,113,121,154,158,175,182,187,192,196,225,230,233,245,253,256,261,314],"entire":[43],"end-to-end":[44],"extraction":[45],"system.":[46],"The":[47,91,107,249],"concept":[48],"Asymmetric":[51],"Agentic":[52],"Network":[53],"(AAN)":[54],"involves":[55],"separating":[56],"validation":[58],"process":[59],"which":[60],"is":[61,73,214,305],"based":[62],"on":[63,186],"source":[65],"from":[66,101],"review":[68],"coherence":[71],"independent":[74],"source,":[77],"and":[78,117,142,162,203,221,260,293],"then":[79],"combining":[80],"two":[82],"types":[83],"signals":[85],"using":[86],"an":[87,126,143,169],"consensus":[89],"score.":[90],"main":[92,188],"controlled":[93,289],"adversarial":[94],"stress":[95,291],"test":[96,292],"included":[97],"123":[98],"paired":[99,211],"evaluations":[100],"28":[102],"cases":[103],"held-out":[105],"PMC-Patients.":[106],"AAN":[108,176,202,250],"rejected":[109,153],"69":[110,156],"out":[111],"71":[114,159],"hallucinated":[115,160],"kept":[118],"51":[119],"52":[122,165],"correct":[123,166],"ones,":[124],"giving":[125],"accepted-hallucination":[127,197],"rate":[128,198],"2.8%":[130],"(95%":[131],"CI":[132],"0.0\u20136.9%),":[133],"precision":[135],"0.986,":[137],"recall":[139],"0.972":[141],"F1":[144,170],"score":[145,171],"0.979.":[147],"Naive":[148],"retrieval-augmented":[149],"generation":[150],"(RAG)":[151],"same":[155],"retained":[163],"all":[164],"claims,":[167],"achieving":[168],"0.986;":[173],"thus":[174],"did":[177],"perform":[179],"better":[180,306],"than":[181,279,307],"simpler":[183],"naive-RAG":[184],"comparator":[185],"outcome":[189],"measure.":[190],"In":[191],"matched":[193],"pooled":[194],"analysis,":[195],"was":[199,228,235],"5.1%":[200],"3.7%":[204],"naive":[206],"RAG":[207],"(the":[208],"historically":[209],"reported":[210],"p":[212],"value":[213],"0.360),":[215],"with":[216],"false-rejection":[217],"rates":[218],"15.0%":[220],"2.1%":[222],"respectively.":[223],"Since":[224],"primary":[226],"analysis":[227],"underpowered,":[229],"fact":[231],"comparison":[234],"significant":[237],"should":[238],"be":[240],"taken":[241],"to":[242,271,277],"mean":[243],"methods":[246],"equivalent.":[248],"greatly":[251],"outperformed":[252],"unverified":[254],"baseline,":[255],"symmetric":[257],"voting":[258],"call-count-matched":[262],"comparator,":[263],"while":[264],"entity":[266],"substitutions":[267],"similar":[270],"each":[272],"other":[273],"remained":[274],"more":[275],"difficult":[276],"detect":[278],"explicit":[280],"numeric":[281],"negation":[283],"perturbations.":[284],"These":[285],"findings":[286],"describe":[287],"illustrate":[294],"safety-coverage":[296],"trade-off;":[297],"do":[299,311],"prove":[301],"retrieval-grounded":[308],"nor":[310],"validate":[313],"full":[315],"conceptual":[316],"architecture.":[317],"Clinical":[318],"trial":[319],"number":[320],"Not":[321],"applicable.":[322]},"counts_by_year":[],"updated_date":"2026-11-11T12:16:26.678958","created_date":"2026-11-10T00:00:00"}