{"id":"https://openalex.org/W7203769963","doi":"https://doi.org/10.2196/98819","title":"Conditional Perplexity Scoring for Large Language Model\u2212Generated Differential Diagnoses in Case Reports: Preliminary Computational Evaluation","display_name":"Conditional Perplexity Scoring for Large Language Model\u2212Generated Differential Diagnoses in Case Reports: Preliminary Computational Evaluation","publication_year":2026,"publication_date":"2026-08-19","ids":{"openalex":"https://openalex.org/W7203769963","doi":"https://doi.org/10.2196/98819","pmid":"https://pubmed.ncbi.nlm.nih.gov/42715414","pmcid":"https://www.ncbi.nlm.nih.gov/pmc/articles/13557325"},"language":"en","primary_location":{"id":"doi:10.2196/98819","doi":"https://doi.org/10.2196/98819","is_oa":true,"landing_page_url":"https://doi.org/10.2196/98819","pdf_url":null,"source":{"id":"https://openalex.org/S4210234749","display_name":"JMIR Formative Research","issn_l":"2561-326X","issn":["2561-326X"],"is_oa":true,"is_in_doaj":true,"is_core":true,"listed_in":["cwts-core","doaj","doyens","medline","norway-1","oarl","poland-20","russia-white-list-3","vabb-shw"],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"JMIR Formative Research","raw_type":"journal-article"},"volume":null,"type":"article","is_traditional":true,"indexed_in":["crossref","doaj","pubmed"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://doi.org/10.2196/98819","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5043968090","display_name":"Takanobu Hirosawa","orcid":"https://orcid.org/0000-0002-3573-8203","observed_orcids":["https://orcid.org/0000-0002-3573-8203"]},"institutions":[{"id":"https://openalex.org/I78274823","display_name":"Dokkyo Medical University","ror":"https://ror.org/05k27ay38","country_code":"JP","type":"education","lineage":["https://openalex.org/I78274823"]}],"countries":["JP"],"is_corresponding":true,"raw_author_name":"Takanobu Hirosawa","raw_affiliation_strings":["Department of Diagnostic and Generalist Medicine, Dokkyo Medical University, 880 Kitakobayashi, Mibu-cho, Shimotsuga, Tochigi, 321-0293, Japan, 81 282-87-2498"],"raw_orcid":"https://orcid.org/0000-0002-3573-8203","affiliations":[{"raw_affiliation_string":"Department of Diagnostic and Generalist Medicine, Dokkyo Medical University, 880 Kitakobayashi, Mibu-cho, Shimotsuga, Tochigi, 321-0293, Japan, 81 282-87-2498","institution_ids":["https://openalex.org/I78274823"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5043660785","display_name":"Yukinori Harada","orcid":"https://orcid.org/0000-0001-6042-7397","observed_orcids":["https://orcid.org/0000-0001-6042-7397"]},"institutions":[{"id":"https://openalex.org/I78274823","display_name":"Dokkyo Medical University","ror":"https://ror.org/05k27ay38","country_code":"JP","type":"education","lineage":["https://openalex.org/I78274823"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Yukinori Harada","raw_affiliation_strings":["Department of Diagnostic and Generalist Medicine, Dokkyo Medical University, 880 Kitakobayashi, Mibu-cho, Shimotsuga, Tochigi, 321-0293, Japan, 81 282-87-2498"],"raw_orcid":"https://orcid.org/0000-0001-6042-7397","affiliations":[{"raw_affiliation_string":"Department of Diagnostic and Generalist Medicine, Dokkyo Medical University, 880 Kitakobayashi, Mibu-cho, Shimotsuga, Tochigi, 321-0293, Japan, 81 282-87-2498","institution_ids":["https://openalex.org/I78274823"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5030355121","display_name":"Ren Kawamura","orcid":"https://orcid.org/0000-0002-5632-3218","observed_orcids":["https://orcid.org/0000-0002-5632-3218"]},"institutions":[{"id":"https://openalex.org/I78274823","display_name":"Dokkyo Medical University","ror":"https://ror.org/05k27ay38","country_code":"JP","type":"education","lineage":["https://openalex.org/I78274823"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Ren Kawamura","raw_affiliation_strings":["Department of Diagnostic and Generalist Medicine, Dokkyo Medical University, 880 Kitakobayashi, Mibu-cho, Shimotsuga, Tochigi, 321-0293, Japan, 81 282-87-2498"],"raw_orcid":"https://orcid.org/0000-0002-5632-3218","affiliations":[{"raw_affiliation_string":"Department of Diagnostic and Generalist Medicine, Dokkyo Medical University, 880 Kitakobayashi, Mibu-cho, Shimotsuga, Tochigi, 321-0293, Japan, 81 282-87-2498","institution_ids":["https://openalex.org/I78274823"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5012449169","display_name":"Taro Shimizu","orcid":"https://orcid.org/0000-0002-3788-487X","observed_orcids":["https://orcid.org/0000-0002-3788-487X"]},"institutions":[{"id":"https://openalex.org/I78274823","display_name":"Dokkyo Medical University","ror":"https://ror.org/05k27ay38","country_code":"JP","type":"education","lineage":["https://openalex.org/I78274823"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Taro Shimizu","raw_affiliation_strings":["Department of Diagnostic and Generalist Medicine, Dokkyo Medical University, 880 Kitakobayashi, Mibu-cho, Shimotsuga, Tochigi, 321-0293, Japan, 81 282-87-2498"],"raw_orcid":"https://orcid.org/0000-0002-3788-487X","affiliations":[{"raw_affiliation_string":"Department of Diagnostic and Generalist Medicine, Dokkyo Medical University, 880 Kitakobayashi, Mibu-cho, Shimotsuga, Tochigi, 321-0293, Japan, 81 282-87-2498","institution_ids":["https://openalex.org/I78274823"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":["https://openalex.org/A5043968090"],"corresponding_institution_ids":["https://openalex.org/I78274823"],"apc_list":{"value":1900,"currency":"USD","value_usd":1900},"apc_paid":{"value":1900,"currency":"USD","value_usd":1900},"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":"10","issue":null,"first_page":"e98819","last_page":"e98819"},"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":0.9702000021934509,"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":0.9702000021934509,"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/T13702","display_name":"Machine Learning in Healthcare","score":0.01640000008046627,"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/T11396","display_name":"Artificial Intelligence in Healthcare","score":0.00419999985024333,"subfield":{"id":"https://openalex.org/subfields/3605","display_name":"Health Information Management"},"field":{"id":"https://openalex.org/fields/36","display_name":"Health Professions"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/large-language-models","display_name":"large language models","score":0.8880000114440918},{"id":"https://openalex.org/keywords/differential-diagnosis","display_name":"differential diagnosis","score":0.7990000247955322},{"id":"https://openalex.org/keywords/clinical-decision-support","display_name":"clinical decision support","score":0.8640000224113464},{"id":"https://openalex.org/keywords/diagnostic-reasoning","display_name":"diagnostic reasoning","score":0.7289999723434448},{"id":"https://openalex.org/keywords/llama-2","display_name":"Llama 2","score":0.5370000004768372},{"id":"https://openalex.org/keywords/gpt-4","display_name":"GPT-4","score":0.871999979019165},{"id":"https://openalex.org/keywords/bards","display_name":"bards","score":0.6039999723434448},{"id":"https://openalex.org/keywords/clinical-narratives","display_name":"clinical text","score":0.4410000145435333},{"id":"https://openalex.org/keywords/hallucination-detection","display_name":"hallucination detection","score":0.40700000524520874}],"concepts":[{"id":"https://openalex.org/C534262118","wikidata":"https://www.wikidata.org/wiki/Q177719","display_name":"Medical diagnosis","level":2,"score":0.751800000667572},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6347000002861023},{"id":"https://openalex.org/C100279451","wikidata":"https://www.wikidata.org/wiki/Q372193","display_name":"Perplexity","level":3,"score":0.5888000130653381},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5285000205039978},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.49939998984336853},{"id":"https://openalex.org/C93226319","wikidata":"https://www.wikidata.org/wiki/Q193137","display_name":"Differential (mechanical device)","level":2,"score":0.33160001039505005},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.33070001006126404},{"id":"https://openalex.org/C44492722","wikidata":"https://www.wikidata.org/wiki/Q327069","display_name":"Conditional probability","level":2,"score":0.322299987077713},{"id":"https://openalex.org/C137293760","wikidata":"https://www.wikidata.org/wiki/Q3621696","display_name":"Language model","level":2,"score":0.28209999203681946},{"id":"https://openalex.org/C155092808","wikidata":"https://www.wikidata.org/wiki/Q182557","display_name":"Computational linguistics","level":2,"score":0.27129998803138733},{"id":"https://openalex.org/C195324797","wikidata":"https://www.wikidata.org/wiki/Q33742","display_name":"Natural language","level":2,"score":0.26429998874664307},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.25999999046325684}],"mesh":[{"descriptor_ui":"D000094143","descriptor_name":"Case Reports as Topic","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true},{"descriptor_ui":"D000098342","descriptor_name":"Large Language Models","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true},{"descriptor_ui":"D003937","descriptor_name":"Diagnosis, Differential","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D006801","descriptor_name":"Humans","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D020000","descriptor_name":"Decision Support Systems, Clinical","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true}],"locations_count":5,"locations":[{"id":"doi:10.2196/98819","doi":"https://doi.org/10.2196/98819","is_oa":true,"landing_page_url":"https://doi.org/10.2196/98819","pdf_url":null,"source":{"id":"https://openalex.org/S4210234749","display_name":"JMIR Formative Research","issn_l":"2561-326X","issn":["2561-326X"],"is_oa":true,"is_in_doaj":true,"is_core":true,"listed_in":["cwts-core","doaj","doyens","medline","norway-1","oarl","poland-20","russia-white-list-3","vabb-shw"],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"JMIR Formative Research","raw_type":"journal-article"},{"id":"doi:10.2196/preprints.98819","doi":"https://doi.org/10.2196/preprints.98819","is_oa":true,"landing_page_url":"https://doi.org/10.2196/preprints.98819","pdf_url":null,"source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"posted-content"},{"id":"pmh:oai:pubmedcentral.nih.gov:13557325","doi":null,"is_oa":true,"landing_page_url":"https://pmc.ncbi.nlm.nih.gov/articles/PMC13557325/","pdf_url":"https://pmc.ncbi.nlm.nih.gov/articles/PMC13557325/pdf/formative-v10-e98819.pdf","source":{"id":"https://openalex.org/S2764455111","display_name":"PubMed Central","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I1299303238","host_organization_name":"National Institutes of Health","host_organization_lineage":["https://openalex.org/I1299303238"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"JMIR Form Res","raw_type":"Text"},{"id":"pmid:42715414","doi":null,"is_oa":false,"landing_page_url":"https://pubmed.ncbi.nlm.nih.gov/42715414","pdf_url":null,"source":{"id":"https://openalex.org/S4306525036","display_name":"PubMed","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I1299303238","host_organization_name":"National Institutes of Health","host_organization_lineage":["https://openalex.org/I1299303238"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"JMIR formative research","raw_type":"Journal Article"},{"id":"pmh:oai:doaj.org/article:40e6693220c545278a1e5e3553bd665e","doi":null,"is_oa":false,"landing_page_url":"https://doaj.org/article/40e6693220c545278a1e5e3553bd665e","pdf_url":null,"source":{"id":"https://openalex.org/S4306401280","display_name":"DOAJ (DOAJ: Directory of Open Access Journals)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"acceptedVersion","is_accepted":true,"is_published":false,"raw_source_name":"JMIR Formative Research, Vol 10, Pp e98819-e98819 (2026)","raw_type":"article"}],"best_oa_location":{"id":"doi:10.2196/98819","doi":"https://doi.org/10.2196/98819","is_oa":true,"landing_page_url":"https://doi.org/10.2196/98819","pdf_url":null,"source":{"id":"https://openalex.org/S4210234749","display_name":"JMIR Formative Research","issn_l":"2561-326X","issn":["2561-326X"],"is_oa":true,"is_in_doaj":true,"is_core":true,"listed_in":["cwts-core","doaj","doyens","medline","norway-1","oarl","poland-20","russia-white-list-3","vabb-shw"],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"JMIR Formative Research","raw_type":"journal-article"},"sustainable_development_goals":[{"display_name":"Good health and well-being","score":0.8371999859809875,"id":"https://metadata.un.org/sdg/3"}],"study_designs":[],"awards":[{"id":"https://openalex.org/G1057556112","display_name":"\u751f\u6210AI\u3092\u6d3b\u7528\u3057\u305f\u7dcf\u5408\u8a3a\u7642\u652f\u63f4\u30c4\u30fc\u30eb\u306e\u958b\u767a\u3068\u81e8\u5e8a\u5fdc\u7528\u306e\u53ef\u80fd\u6027\u306b\u3064\u3044\u3066\u306e\u7814\u7a76","funder_award_id":"26K13028","funder_id":"https://openalex.org/F4320334764","funder_display_name":"Japan Society for the Promotion of Science"},{"id":"https://openalex.org/G3036062093","display_name":"\u300c\u8a3a\u65ad\u306e\u9045\u308c\u300d\u3092\u6700\u5c0f\u9650\u306b\u3059\u308b\u81d3\u5668\u5c02\u9580\u79d1\u3078\u306e\u7d39\u4ecb\u524d\u30a2\u30eb\u30b4\u30ea\u30ba\u30e0\u306e\u958b\u767a","funder_award_id":"24K20178","funder_id":"https://openalex.org/F4320334764","funder_display_name":"Japan Society for the Promotion of Science"}],"funders":[{"id":"https://openalex.org/F4320321607","display_name":"Dokkyo Medical University","ror":"https://ror.org/05k27ay38"},{"id":"https://openalex.org/F4320334764","display_name":"Japan Society for the Promotion of Science","ror":"https://ror.org/00hhkn466"}],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W7203769963.pdf","grobid_xml":"https://content.openalex.org/works/W7203769963.grobid-xml"},"referenced_works_count":62,"referenced_works":["https://openalex.org/W1481389199","https://openalex.org/W1985153860","https://openalex.org/W2029907219","https://openalex.org/W2058373514","https://openalex.org/W2074932800","https://openalex.org/W2080213370","https://openalex.org/W2141222241","https://openalex.org/W2166750785","https://openalex.org/W2167373494","https://openalex.org/W2168490582","https://openalex.org/W2317648909","https://openalex.org/W2408123483","https://openalex.org/W2430577464","https://openalex.org/W2545846385","https://openalex.org/W2904422658","https://openalex.org/W2910707576","https://openalex.org/W2914598080","https://openalex.org/W2991379615","https://openalex.org/W3004612364","https://openalex.org/W3023766558","https://openalex.org/W3048817558","https://openalex.org/W3090309736","https://openalex.org/W3118443063","https://openalex.org/W3126897851","https://openalex.org/W3159307263","https://openalex.org/W3182546273","https://openalex.org/W3213120545","https://openalex.org/W3214024265","https://openalex.org/W4200133306","https://openalex.org/W4297014655","https://openalex.org/W4378576258","https://openalex.org/W4380730209","https://openalex.org/W4384524772","https://openalex.org/W4384561707","https://openalex.org/W4385381951","https://openalex.org/W4386568605","https://openalex.org/W4390041933","https://openalex.org/W4392504747","https://openalex.org/W4394956892","https://openalex.org/W4396570449","https://openalex.org/W4396669640","https://openalex.org/W4400864443","https://openalex.org/W4401076218","https://openalex.org/W4402683949","https://openalex.org/W4403210291","https://openalex.org/W4403362497","https://openalex.org/W4404182570","https://openalex.org/W4404347231","https://openalex.org/W4404470920","https://openalex.org/W4404534210","https://openalex.org/W4404624677","https://openalex.org/W4404625318","https://openalex.org/W4404791049","https://openalex.org/W4405655184","https://openalex.org/W4408736524","https://openalex.org/W4409906005","https://openalex.org/W4411403346","https://openalex.org/W4412488514","https://openalex.org/W4415201218","https://openalex.org/W4415249921","https://openalex.org/W7126395632","https://openalex.org/W7129421753"],"related_works":["https://openalex.org/W7154908291","https://openalex.org/W4399868262","https://openalex.org/W4412458725","https://openalex.org/W7162037817","https://openalex.org/W4410841501","https://openalex.org/W7221322042","https://openalex.org/W4391835514","https://openalex.org/W4409283616","https://openalex.org/W4413423589","https://openalex.org/W7131248021"],"abstract_inverted_index":{"Background:":[0],"Large":[1],"language":[2],"models":[3],"(LLMs)":[4],"are":[5],"increasingly":[6],"used":[7,165],"to":[8,68,91,141,374],"generate":[9],"differential":[10,135,147,186,244,334],"diagnoses":[11,175,212,219,256,264,270,279,291,301,361],"from":[12],"clinical":[13,18,162],"narratives.":[14],"However,":[15],"LLM-based":[16],"diagnostic":[17,69],"decision":[19],"support":[20],"systems":[21],"still":[22],"lack":[23],"a":[24,30,45,51,81,384,410],"quantitative":[25,386],"measure":[26],"of":[27,377,401],"how":[28,43],"strongly":[29],"diagnosis":[31,82,122,148,187,245,335,370,399],"is":[32,48],"supported":[33],"by":[34,71,98,124,151,191,214,248,258,343],"the":[35,73,78,84,119,160,167,171,179,242,318,330,367,397,402],"available":[36],"case":[37,75,172,206,234,237],"description.":[38],"Conditional":[39,182,381],"perplexity":[40,95,130,183,274,296,365,382],"score":[41],"quantifies":[42],"predictable":[44],"target":[46,85],"text":[47],"given":[49],"in":[50,155,371],"preceding":[52],"context,":[53,169],"with":[54,389],"lower":[55,128,272,294,363],"scores":[56,131,184,275,297],"indicating":[57],"greater":[58],"predictability.":[59],"We":[60,204],"hypothesized":[61,117],"that":[62,118],"this":[63,143],"concept":[64],"can":[65],"be":[66],"adapted":[67],"reasoning":[70],"treating":[72],"prediagnostic":[74,161],"description":[76,163],"as":[77,83,166,178,409],"context":[79],"and":[80,102,111,170,194,216,223,236,251,260,328,350],"text.":[86],"Objective:":[87],"This":[88],"study":[89],"aims":[90],"evaluate":[92],"whether":[93],"conditional":[94,129,273,295,364],"scores,":[96],"computed":[97,197],"an":[99,199],"independent":[100,200],"LLM":[101],"conditioned":[103],"on":[104],"case-report":[105],"narratives,":[106],"differ":[107],"between":[108],"physician-verified":[109,390],"correct":[110,120,210,254,360,398],"incorrect":[112,133,217,262,277,299,369,404],"LLM-generated":[113,121,134,211,255,290,300],"diagnoses.":[114,136,240],"Specifically,":[115],"we":[116],"verified":[123,213,257],"physicians":[125,259],"would":[126],"have":[127],"than":[132,276,298,366],"A":[137],"secondary":[138],"outcome":[139],"was":[140,164,313,329],"compare":[142],"scoring":[144],"behavior":[145],"across":[146],"lists":[149,188,246,336],"generated":[150,190,218,247,263,278],"different":[152],"LLMs.":[153],"Methods:":[154],"2022.":[156],"For":[157],"each":[158],"case,":[159],"conditioning":[168],"report-defined":[173,207,238,268],"final":[174,208,239,269],"were":[176,196,265],"treated":[177],"gold":[180],"standard.":[181],"for":[185,332],"previously":[189],"LLaMA2,":[192,249],"Bard,":[193,250],"GPT-4":[195],"using":[198,220],"longer-context":[201],"LLM,":[202],"Qwen2.5-1.5B.":[203],"compared":[205],"diagnoses,":[209],"physicians,":[215],"nonparametric":[221],"comparisons":[222],"receiver":[224,319],"operating":[225,320],"characteristic":[226,321],"analyses.":[227],"Results:":[228],"All":[229],"392":[230],"cases":[231],"had":[232,271,293,362],"complete":[233],"descriptions":[235],"Across":[241],"top-10":[243],"GPT-4,":[252],"823":[253],"10,875":[261],"analyzed.":[266],"Case":[267],"(median":[280,302],"39.9,":[281],"IQR":[282,287,304,309],"17.7-119.9":[283],"vs":[284,306],"median":[285,307],"133.3,":[286,308],"37.5-672.1).":[288],"Correct":[289],"also":[292],"43.3,":[303],"16.6-147.5":[305],"37.6-672.1).":[310],"Candidate-level":[311],"discrimination":[312],"moderate":[314,385],"overall":[315],"(area":[316],"under":[317],"curve":[322],"[AUC]":[323],"0.666,":[324],"95%":[325,339,347,354],"CI":[326,340,348,355],"0.644-0.689)":[327],"highest":[331],"GPT-4-generated":[333],"(AUC":[337,345,352],"0.678,":[338],"0.652-0.705),":[341],"followed":[342],"LLaMA2":[344],"0.662,":[346],"0.625-0.698)":[349],"Bard":[351],"0.648,":[353],"0.617-0.681).":[356],"In":[357],"within-case":[358],"analyses,":[359],"mean":[368],"88.1%":[372],"(237/269)":[373],"91.1%":[375],"(195/214)":[376],"evaluable":[378],"lists.":[379],"Conclusions:":[380],"provided":[383],"signal":[387],"associated":[388],"correctness":[391],"but":[392],"did":[393],"not":[394],"reliably":[395],"rank":[396],"ahead":[400],"strongest":[403],"candidate,":[405],"limiting":[406],"its":[407],"use":[408],"stand-alone":[411],"reranking":[412],"method.":[413]},"counts_by_year":[],"updated_date":"2026-11-10T11:39:43.021295","created_date":"2026-08-20T00:00:00"}
