{"id":"https://openalex.org/W4407759244","doi":"https://doi.org/10.48550/arxiv.2502.12183","title":"Leveraging large language models for structured information extraction from pathology reports","display_name":"Leveraging large language models for structured information extraction from pathology reports","publication_year":2025,"publication_date":"2025-02-14","ids":{"openalex":"https://openalex.org/W4407759244","doi":"https://doi.org/10.48550/arxiv.2502.12183"},"language":"en","primary_location":{"id":"pmh:oai:arXiv.org:2502.12183","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2502.12183","pdf_url":"https://arxiv.org/pdf/2502.12183","source":{"id":"https://openalex.org/S4393918464","display_name":"ArXiv.org","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"type":"preprint","indexed_in":["arxiv","datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2502.12183","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5041874845","display_name":"Jeya Balaji Balasubramanian","orcid":"https://orcid.org/0000-0002-0025-8410"},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Balasubramanian, Jeya Balaji","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5035705255","display_name":"Daniel E. Adams","orcid":"https://orcid.org/0000-0001-5799-7807"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Adams, Daniel","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5102951270","display_name":"Ioannis Roxanis","orcid":"https://orcid.org/0000-0002-0450-2206"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Roxanis, Ioannis","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5054794121","display_name":"Amy Berrington de Gonz\u00e1lez","orcid":"https://orcid.org/0000-0002-7332-8387"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"de Gonzalez, Amy Berrington","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5016756940","display_name":"Penny Coulson","orcid":"https://orcid.org/0000-0001-7173-2581"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Coulson, Penny","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5012322429","display_name":"Jonas S. Almeida","orcid":"https://orcid.org/0000-0002-7883-7922"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Almeida, Jonas S.","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5076591561","display_name":"Montserrat Garc\u00eda\u2010Closas","orcid":"https://orcid.org/0000-0003-1033-2650"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Garc\u00eda-Closas, Montserrat","raw_affiliation_strings":[],"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":7,"corresponding_author_ids":["https://openalex.org/A5041874845"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"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":0.98089998960495,"subfield":{"id":"https://openalex.org/subfields/1312","display_name":"Molecular Biology"},"field":{"id":"https://openalex.org/fields/13","display_name":"Biochemistry, Genetics and Molecular Biology"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}},"topics":[{"id":"https://openalex.org/T11710","display_name":"Biomedical Text Mining and Ontologies","score":0.98089998960495,"subfield":{"id":"https://openalex.org/subfields/1312","display_name":"Molecular Biology"},"field":{"id":"https://openalex.org/fields/13","display_name":"Biochemistry, Genetics and Molecular Biology"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}},{"id":"https://openalex.org/T10028","display_name":"Topic Modeling","score":0.9326000213623047,"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/computer-science","display_name":"Computer science","score":0.600096583366394},{"id":"https://openalex.org/keywords/information-extraction","display_name":"Information extraction","score":0.5513455867767334},{"id":"https://openalex.org/keywords/natural-language-processing","display_name":"Natural language processing","score":0.4991881847381592},{"id":"https://openalex.org/keywords/extraction","display_name":"Extraction (chemistry)","score":0.4893011748790741},{"id":"https://openalex.org/keywords/information-retrieval","display_name":"Information retrieval","score":0.4066488742828369},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.33834290504455566},{"id":"https://openalex.org/keywords/data-science","display_name":"Data science","score":0.3371281027793884},{"id":"https://openalex.org/keywords/chemistry","display_name":"Chemistry","score":0.09214422106742859},{"id":"https://openalex.org/keywords/chromatography","display_name":"Chromatography","score":0.07706791162490845}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.600096583366394},{"id":"https://openalex.org/C195807954","wikidata":"https://www.wikidata.org/wiki/Q1662562","display_name":"Information extraction","level":2,"score":0.5513455867767334},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.4991881847381592},{"id":"https://openalex.org/C4725764","wikidata":"https://www.wikidata.org/wiki/Q844704","display_name":"Extraction (chemistry)","level":2,"score":0.4893011748790741},{"id":"https://openalex.org/C23123220","wikidata":"https://www.wikidata.org/wiki/Q816826","display_name":"Information retrieval","level":1,"score":0.4066488742828369},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.33834290504455566},{"id":"https://openalex.org/C2522767166","wikidata":"https://www.wikidata.org/wiki/Q2374463","display_name":"Data science","level":1,"score":0.3371281027793884},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.09214422106742859},{"id":"https://openalex.org/C43617362","wikidata":"https://www.wikidata.org/wiki/Q170050","display_name":"Chromatography","level":1,"score":0.07706791162490845}],"mesh":[],"locations_count":2,"locations":[{"id":"pmh:oai:arXiv.org:2502.12183","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2502.12183","pdf_url":"https://arxiv.org/pdf/2502.12183","source":{"id":"https://openalex.org/S4393918464","display_name":"ArXiv.org","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},{"id":"doi:10.48550/arxiv.2502.12183","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2502.12183","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"article"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:2502.12183","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2502.12183","pdf_url":"https://arxiv.org/pdf/2502.12183","source":{"id":"https://openalex.org/S4393918464","display_name":"ArXiv.org","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":["https://openalex.org/W2377297411","https://openalex.org/W3148217948","https://openalex.org/W2375788636","https://openalex.org/W2358561207","https://openalex.org/W2975617233","https://openalex.org/W2388704129","https://openalex.org/W2392827053","https://openalex.org/W2377877252","https://openalex.org/W2362914816","https://openalex.org/W2368651715"],"abstract_inverted_index":{"Background:":[0],"Structured":[1],"information":[2,52,205],"extraction":[3,15,31,61,88,159,206,223],"from":[4,53,122,229],"unstructured":[5,230],"histopathology":[6,56,120],"reports":[7,121,232],"facilitates":[8],"data":[9,42,114,138,228],"accessibility":[10,238],"for":[11,80,113,195,203,221],"clinical":[12],"research.":[13],"Manual":[14],"by":[16,62,211],"experts":[17],"is":[18],"time-consuming":[19],"and":[20,104,155,170,239],"expensive,":[21],"limiting":[22],"scalability.":[23],"Large":[24],"language":[25,38],"models":[26],"(LLMs)":[27],"offer":[28],"efficient":[29],"automated":[30,81],"through":[32,236],"zero-shot":[33],"prompting,":[34],"requiring":[35],"only":[36],"natural":[37,214],"instructions":[39],"without":[40],"labeled":[41],"or":[43],"training.":[44],"We":[45,68,83,198],"evaluate":[46,91],"LLMs'":[47],"accuracy":[48,160,183],"in":[49,135],"extracting":[50,130],"structured":[51,204,227],"breast":[54],"cancer":[55],"reports,":[57],"compared":[58],"to":[59,90,162,233],"manual":[60],"a":[63,75,85,100,192],"trained":[64],"human":[65,93,164,182],"annotator.":[66],"Methods:":[67],"developed":[69,84,199],"the":[70,92,105,123,136,143,163],"Medical":[71],"Report":[72],"Information":[73],"Extractor,":[74],"web":[76],"application":[77],"leveraging":[78],"LLMs":[79,97],"extraction.":[82],"gold":[86,144],"standard":[87,145],"dataset":[89,146],"annotator":[94,165],"alongside":[95],"five":[96],"including":[98],"GPT-4o,":[99],"leading":[101],"proprietary":[102],"model,":[103],"Llama":[106,150,176],"3":[107],"model":[108],"family,":[109],"which":[110],"allows":[111],"self-hosting":[112],"privacy.":[115],"Our":[116],"assessment":[117],"involved":[118],"111":[119],"Breast":[124],"Cancer":[125],"Now":[126],"(BCN)":[127],"Generations":[128],"Study,":[129],"51":[131],"pathology":[132],"features":[133],"specified":[134],"study's":[137],"dictionary.":[139],"Results:":[140],"Evaluation":[141],"against":[142],"showed":[147],"that":[148,207],"both":[149],"3.1":[151,177],"405B":[152],"(94.7%":[153],"accuracy)":[154],"GPT-4o":[156],"(96.1%)":[157],"achieved":[158],"comparable":[161],"(95.4%;":[166],"p":[167,171],"=":[168,172],"0.146":[169],"0.106,":[173],"respectively).":[174],"While":[175],"70B":[178],"(91.6%)":[179],"performed":[180],"below":[181],"(p":[184],"<0.001),":[185],"its":[186],"reduced":[187],"computational":[188],"requirements":[189],"make":[190],"it":[191],"viable":[193],"option":[194],"self-hosting.":[196],"Conclusion:":[197],"an":[200],"open-source":[201],"tool":[202],"can":[208],"be":[209],"customized":[210],"non-programmers":[212],"using":[213],"language.":[215],"Its":[216],"modular":[217],"design":[218],"enables":[219],"reuse":[220],"various":[222],"tasks,":[224],"producing":[225],"standardized,":[226],"text":[231],"facilitate":[234],"analytics":[235],"improved":[237],"interoperability.":[240]},"counts_by_year":[{"year":2025,"cited_by_count":1}],"updated_date":"2026-04-20T07:46:08.049788","created_date":"2025-10-10T00:00:00"}