{"id":"https://openalex.org/W7214803076","doi":"https://doi.org/10.1210/clinem/dgag351","title":"An agentic, no-code artificial intelligence workflow for developing a thyroid nodule ultrasound malignancy classifier","display_name":"An agentic, no-code artificial intelligence workflow for developing a thyroid nodule ultrasound malignancy classifier","publication_year":2026,"publication_date":"2026-08-25","ids":{"openalex":"https://openalex.org/W7214803076","doi":"https://doi.org/10.1210/clinem/dgag351","pmid":"https://pubmed.ncbi.nlm.nih.gov/42808848"},"language":"en","primary_location":{"id":"doi:10.1210/clinem/dgag351","doi":"https://doi.org/10.1210/clinem/dgag351","is_oa":false,"landing_page_url":"https://doi.org/10.1210/clinem/dgag351","pdf_url":null,"source":{"id":"https://openalex.org/S205775358","display_name":"The Journal of Clinical Endocrinology & Metabolism","issn_l":"0021-972X","issn":["0021-972X","1945-7197"],"is_oa":false,"is_in_doaj":false,"is_core":true,"listed_in":["anvur-class-a","anvur-scientific","cwts-core","dongbi-b","doyens","jufo-3","ki-jl-3","medline","norway-2","poland-140","russia-white-list-1","vabb-shw"],"host_organization":"https://openalex.org/P4310311648","host_organization_name":"Oxford University Press","host_organization_lineage":["https://openalex.org/P4310311648","https://openalex.org/P4310311647"],"host_organization_lineage_names":["Oxford University Press","University of Oxford"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"The Journal of Clinical Endocrinology & Metabolism","raw_type":"journal-article"},"volume":null,"type":"article","is_traditional":true,"indexed_in":["crossref","pubmed"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://www.medrxiv.org/content/medrxiv/early/2026/06/26/2026.06.23.26356395.full.pdf","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5153490421","display_name":"Johnson Thomas","orcid":"https://orcid.org/0000-0003-3540-4913","observed_orcids":["https://orcid.org/0000-0003-3540-4913"]},"institutions":[{"id":"https://openalex.org/I4210097889","display_name":"Mercy Hospital Springfield","ror":"https://ror.org/00w440229","country_code":"US","type":"healthcare","lineage":["https://openalex.org/I4210097889","https://openalex.org/I4210113060"]},{"id":"https://openalex.org/I4210164729","display_name":"Mercy Medical Center","ror":"https://ror.org/05na1sz60","country_code":"US","type":"healthcare","lineage":["https://openalex.org/I1304269903","https://openalex.org/I4210128764","https://openalex.org/I4210164729"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Johnson Thomas","raw_affiliation_strings":["Department of Endocrinology, Mercy Hospital , Springfield, MO 65807 ,"],"raw_orcid":"https://orcid.org/0000-0003-3540-4913","affiliations":[{"raw_affiliation_string":"Department of Endocrinology, Mercy Hospital , Springfield, MO 65807 ,","institution_ids":["https://openalex.org/I4210097889","https://openalex.org/I4210164729"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5018764559","display_name":"Nikita V. Pozdeyev","orcid":"https://orcid.org/0000-0001-8574-1972","observed_orcids":["https://orcid.org/0000-0001-8574-1972"]},"institutions":[{"id":"https://openalex.org/I4210134151","display_name":"University of Colorado Cancer Center","ror":"https://ror.org/04cqn7d42","country_code":"US","type":"healthcare","lineage":["https://openalex.org/I4210134151"]},{"id":"https://openalex.org/I51713134","display_name":"University of Colorado Anschutz","ror":"https://ror.org/03wmf1y16","country_code":"US","type":"education","lineage":["https://openalex.org/I51713134"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Nikita Pozdeyev","raw_affiliation_strings":["Colorado Center for Personalized Medicine, University of Colorado Anschutz , Aurora, CO 80045 ,","Department of Biomedical Informatics, University of Colorado Anschutz , Aurora, CO 80045 ,","Division of Endocrinology, Diabetes and Metabolism, University of Colorado Anschutz , Aurora, CO 80045 ,","University of Colorado Cancer Center, University of Colorado Anschutz , Aurora, CO 80045 ,"],"raw_orcid":"https://orcid.org/0000-0001-8574-1972","affiliations":[{"raw_affiliation_string":"Colorado Center for Personalized Medicine, University of Colorado Anschutz , Aurora, CO 80045 ,","institution_ids":["https://openalex.org/I51713134"]},{"raw_affiliation_string":"Department of Biomedical Informatics, University of Colorado Anschutz , Aurora, CO 80045 ,","institution_ids":["https://openalex.org/I51713134"]},{"raw_affiliation_string":"Division of Endocrinology, Diabetes and Metabolism, University of Colorado Anschutz , Aurora, CO 80045 ,","institution_ids":["https://openalex.org/I51713134"]},{"raw_affiliation_string":"University of Colorado Cancer Center, University of Colorado Anschutz , Aurora, CO 80045 ,","institution_ids":["https://openalex.org/I4210134151","https://openalex.org/I51713134"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":{"value":3750,"currency":"USD","value_usd":3750},"apc_paid":null,"fwci":0.0,"has_fulltext":false,"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":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10862","display_name":"AI in cancer detection","score":0.5776000022888184,"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"}},"topics":[{"id":"https://openalex.org/T10862","display_name":"AI in cancer detection","score":0.5776000022888184,"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/T10329","display_name":"Thyroid Cancer Diagnosis and Treatment","score":0.4120999872684479,"subfield":{"id":"https://openalex.org/subfields/2712","display_name":"Endocrinology, Diabetes and Metabolism"},"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":0.005100000184029341,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/thyroid-nodules","display_name":"thyroid nodules","score":0.6949999928474426},{"id":"https://openalex.org/keywords/agentic-ai","display_name":"agentic AI","score":0.6589999794960022},{"id":"https://openalex.org/keywords/no-code-machine-learning","display_name":"no-code machine learning","score":0.8119999766349792},{"id":"https://openalex.org/keywords/probability-calibration","display_name":"probability calibration","score":0.6779999732971191},{"id":"https://openalex.org/keywords/external-validation","display_name":"external validation","score":0.7519999742507935},{"id":"https://openalex.org/keywords/resnet-18","display_name":"ResNet-18","score":0.906000018119812},{"id":"https://openalex.org/keywords/follicular-thyroid-carcinoma","display_name":"follicular thyroid carcinoma","score":0.8199999928474426},{"id":"https://openalex.org/keywords/papillary-thyroid-carcinoma","display_name":"papillary thyroid carcinoma","score":0.9290000200271606}],"concepts":[{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7024999856948853},{"id":"https://openalex.org/C177212765","wikidata":"https://www.wikidata.org/wiki/Q627335","display_name":"Workflow","level":2,"score":0.6086000204086304},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5819000005722046},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5291000008583069},{"id":"https://openalex.org/C2779022025","wikidata":"https://www.wikidata.org/wiki/Q53829","display_name":"Thyroid nodules","level":3,"score":0.4975000023841858},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.44679999351501465},{"id":"https://openalex.org/C2779399171","wikidata":"https://www.wikidata.org/wiki/Q1483951","display_name":"Malignancy","level":2,"score":0.4117000102996826},{"id":"https://openalex.org/C2776731575","wikidata":"https://www.wikidata.org/wiki/Q2916245","display_name":"Nodule (geology)","level":2,"score":0.4090999960899353},{"id":"https://openalex.org/C2779761222","wikidata":"https://www.wikidata.org/wiki/Q826522","display_name":"Thyroid cancer","level":3,"score":0.40849998593330383},{"id":"https://openalex.org/C71924100","wikidata":"https://www.wikidata.org/wiki/Q11190","display_name":"Medicine","level":0,"score":0.392300009727478},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.37459999322891235},{"id":"https://openalex.org/C526584372","wikidata":"https://www.wikidata.org/wiki/Q16399","display_name":"Thyroid","level":2,"score":0.37220001220703125},{"id":"https://openalex.org/C126838900","wikidata":"https://www.wikidata.org/wiki/Q77604","display_name":"Radiology","level":1,"score":0.3546999990940094},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.34310001134872437},{"id":"https://openalex.org/C3019719930","wikidata":"https://www.wikidata.org/wiki/Q3910099","display_name":"Predictive value","level":2,"score":0.3292999863624573},{"id":"https://openalex.org/C2777074255","wikidata":"https://www.wikidata.org/wiki/Q1351681","display_name":"Thyroidectomy","level":3,"score":0.32199999690055847},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.30709999799728394},{"id":"https://openalex.org/C3020132585","wikidata":"https://www.wikidata.org/wiki/Q2671652","display_name":"Diagnostic accuracy","level":2,"score":0.28200000524520874},{"id":"https://openalex.org/C143753070","wikidata":"https://www.wikidata.org/wiki/Q162564","display_name":"Ultrasound","level":2,"score":0.27900001406669617},{"id":"https://openalex.org/C2779974597","wikidata":"https://www.wikidata.org/wiki/Q28448986","display_name":"Clinical Practice","level":2,"score":0.2581000030040741}],"mesh":[],"locations_count":3,"locations":[{"id":"doi:10.1210/clinem/dgag351","doi":"https://doi.org/10.1210/clinem/dgag351","is_oa":false,"landing_page_url":"https://doi.org/10.1210/clinem/dgag351","pdf_url":null,"source":{"id":"https://openalex.org/S205775358","display_name":"The Journal of Clinical Endocrinology & Metabolism","issn_l":"0021-972X","issn":["0021-972X","1945-7197"],"is_oa":false,"is_in_doaj":false,"is_core":true,"listed_in":["anvur-class-a","anvur-scientific","cwts-core","dongbi-b","doyens","jufo-3","ki-jl-3","medline","norway-2","poland-140","russia-white-list-1","vabb-shw"],"host_organization":"https://openalex.org/P4310311648","host_organization_name":"Oxford University Press","host_organization_lineage":["https://openalex.org/P4310311648","https://openalex.org/P4310311647"],"host_organization_lineage_names":["Oxford University Press","University of Oxford"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"The Journal of Clinical Endocrinology & Metabolism","raw_type":"journal-article"},{"id":"doi:10.64898/2026.06.23.26356395","doi":"https://doi.org/10.64898/2026.06.23.26356395","is_oa":true,"landing_page_url":"https://doi.org/10.64898/2026.06.23.26356395","pdf_url":"https://www.medrxiv.org/content/medrxiv/early/2026/06/26/2026.06.23.26356395.full.pdf","source":{"id":"https://openalex.org/S3005729997","display_name":"medRxiv","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":true,"listed_in":["cwts-core"],"host_organization":null,"host_organization_name":null,"host_organization_lineage":["https://openalex.org/P4310315909"],"host_organization_lineage_names":["Cold Spring Harbor Laboratory Press"],"type":"repository"},"license":"cc-by-nc","license_id":"https://openalex.org/licenses/cc-by-nc","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"medRxiv","raw_type":"posted-content"},{"id":"pmid:42808848","doi":null,"is_oa":false,"landing_page_url":"https://pubmed.ncbi.nlm.nih.gov/42808848","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":"The Journal of clinical endocrinology and metabolism","raw_type":"Journal Article"}],"best_oa_location":{"id":"doi:10.64898/2026.06.23.26356395","doi":"https://doi.org/10.64898/2026.06.23.26356395","is_oa":true,"landing_page_url":"https://doi.org/10.64898/2026.06.23.26356395","pdf_url":"https://www.medrxiv.org/content/medrxiv/early/2026/06/26/2026.06.23.26356395.full.pdf","source":{"id":"https://openalex.org/S3005729997","display_name":"medRxiv","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":true,"listed_in":["cwts-core"],"host_organization":null,"host_organization_name":null,"host_organization_lineage":["https://openalex.org/P4310315909"],"host_organization_lineage_names":["Cold Spring Harbor Laboratory Press"],"type":"repository"},"license":"cc-by-nc","license_id":"https://openalex.org/licenses/cc-by-nc","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"medRxiv","raw_type":"posted-content"},"sustainable_development_goals":[{"score":0.9391000270843506,"display_name":"Good health and well-being","id":"https://metadata.un.org/sdg/3"}],"study_designs":[],"awards":[],"funders":[],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W7214803076.pdf","grobid_xml":"https://content.openalex.org/works/W7214803076.grobid-xml"},"referenced_works_count":18,"referenced_works":["https://openalex.org/W2145150141","https://openalex.org/W2194775991","https://openalex.org/W2603963723","https://openalex.org/W2802194281","https://openalex.org/W2905949064","https://openalex.org/W2956423933","https://openalex.org/W4281686470","https://openalex.org/W4386533366","https://openalex.org/W4390612340","https://openalex.org/W4394857110","https://openalex.org/W4395959156","https://openalex.org/W4412470760","https://openalex.org/W4413270110","https://openalex.org/W4414499626","https://openalex.org/W6931259419","https://openalex.org/W7113897803","https://openalex.org/W7119198310","https://openalex.org/W7165795830"],"related_works":["https://openalex.org/W7166051246","https://openalex.org/W3202730798","https://openalex.org/W4407205258","https://openalex.org/W4308366733","https://openalex.org/W7163020573","https://openalex.org/W3205678120","https://openalex.org/W7160156420","https://openalex.org/W7203458192","https://openalex.org/W4410572228","https://openalex.org/W4417294003"],"abstract_inverted_index":{"BACKGROUND:":[0],"Convolutional":[1],"neural":[2],"networks":[3],"can":[4,47],"classify":[5],"thyroid":[6,33,51,212],"nodules":[7,108],"on":[8,31,60,106,122],"ultrasound,":[9],"yet":[10],"published":[11,119],"models":[12],"are":[13,28,229],"seldom":[14],"available":[15],"for":[16],"independent":[17],"testing,":[18],"require":[19],"machine":[20],"learning":[21],"expertise":[22],"to":[23],"develop":[24,48],"and":[25,27,55,59,87,91,114,150,175,218,226],"deploy,":[26],"validated":[29,105,211],"mostly":[30],"papillary":[32],"carcinoma.":[34],"OBJECTIVE:":[35],"To":[36],"test":[37,130],"whether":[38],"an":[39,61,136,159],"autonomous":[40],"(\"agentic\"),":[41],"no-code":[42,77,204],"artificial":[43],"intelligence":[44],"(AI)":[45],"agent":[46,78],"a":[49,71,117,208],"calibrated":[50,92],"nodule":[52,213],"malignancy":[53],"classifier":[54,120],"validate":[56],"it":[57],"internally":[58],"external":[62,154],"cohort":[63],"spanning":[64],"multiple":[65],"cancer":[66],"histologies.":[67],"METHODS:":[68],"This":[69],"was":[70,103,167],"retrospective,":[72],"computational":[73],"diagnostic":[74],"study.":[75],"A":[76],"(Hugging":[79],"Face":[80],"ML-Intern)":[81],"autonomously":[82],"audited":[83],"the":[84,89,95,110,123,128,132,139,156,181,184],"data,":[85],"selected":[86],"trained":[88],"model,":[90],"probabilities,":[93],"using":[94],"open-source":[96],"TN5000":[97],"dataset.":[98],"The":[99],"locked":[100],"ResNet-18":[101],"model":[102,134,157,186],"externally":[104,210],"232":[107],"from":[109],"University":[111],"of":[112,143,161,183],"Colorado":[113],"benchmarked":[115],"against":[116],"previously":[118],"evaluated":[121],"same":[124],"source.":[125],"RESULTS:":[126],"On":[127,153],"internal":[129],"set,":[131],"agentic":[133],"achieved":[135,158],"area":[137],"under":[138],"receiver-operating-characteristic":[140],"curve":[141],"(AUROC)":[142],"0.94":[144],"(95%":[145,163],"CI,":[146,164],"0.92-0.95),":[147],"sensitivity":[148,166],"0.90,":[149],"specificity":[151,169],"0.81.":[152],"validation,":[155],"AUROC":[160],"0.90":[162],"0.85-0.93);":[165],"0.92,":[168],"0.68,":[170],"positive":[171],"predictive":[172,177],"value":[173,178],"0.52,":[174],"negative":[176],"0.96,":[179],"exceeding":[180],"performance":[182],"benchmark":[185],"(AUROC":[187],"0.83;":[188],"paired":[189],"DeLong":[190],"P":[191],"=":[192],".03).":[193],"Missed":[194],"cancers":[195],"clustered":[196],"among":[197],"follicular":[198],"pattern":[199],"tumors.":[200],"CONCLUSION:":[201],"An":[202],"agentic,":[203],"AI":[205,222],"workflow":[206],"produced":[207],"calibrated,":[209],"classifier,":[214],"supporting":[215],"accessible,":[216],"reproducible,":[217],"independently":[219],"testable":[220],"medical":[221],"development.":[223],"Prospective":[224],"validation":[225],"local":[227],"recalibration":[228],"required":[230],"before":[231],"clinical":[232],"use.":[233]},"counts_by_year":[],"updated_date":"2026-11-09T13:22:03.289127","created_date":"2026-09-30T00:00:00"}