{"id":"https://openalex.org/W7138390084","doi":"https://doi.org/10.48550/arxiv.2603.14158","title":"Clinician input steers frontier AI models toward both accurate and harmful decisions","display_name":"Clinician input steers frontier AI models toward both accurate and harmful decisions","publication_year":2026,"publication_date":"2026-03-14","ids":{"openalex":"https://openalex.org/W7138390084","doi":"https://doi.org/10.48550/arxiv.2603.14158"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2603.14158","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.14158","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":false,"raw_source_name":null,"raw_type":"article"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2603.14158","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5129669748","display_name":"Ivan Lopez","orcid":null},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Lopez, Ivan","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5059103201","display_name":"Selin Everett","orcid":"https://orcid.org/0009-0008-0196-2531"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Everett, Selin S.","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5040991704","display_name":"Bryan Bunning","orcid":"https://orcid.org/0000-0003-3092-0038"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Bunning, Bryan J.","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5129655701","display_name":"April S. Liang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Liang, April S.","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5129686501","display_name":"Dong Han Yao","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yao, Dong Han","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5129656143","display_name":"Shivam C. Vedak","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Vedak, Shivam C.","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5014095820","display_name":"Kameron Collin Black","orcid":"https://orcid.org/0000-0002-3696-4050"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Black, Kameron C.","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5129642725","display_name":"Sophie Ostmeier","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Ostmeier, Sophie","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5102951515","display_name":"Shihong Ma","orcid":"https://orcid.org/0000-0002-5548-9777"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Ma, Stephen P.","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5129721226","display_name":"Emily Alsentzer","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Alsentzer, Emily","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5129704018","display_name":"Jonathan H. Chen","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Chen, Jonathan H.","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5129752319","display_name":"Akshay S. Chaudhari","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Chaudhari, Akshay S.","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5129686924","display_name":"Eric Horvitz","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Horvitz, Eric","raw_affiliation_strings":[],"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":13,"corresponding_author_ids":["https://openalex.org/A5129669748"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":0,"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/T11636","display_name":"Artificial Intelligence in Healthcare and Education","score":0.6640999913215637,"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":0.6640999913215637,"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/T12026","display_name":"Explainable Artificial Intelligence (XAI)","score":0.13179999589920044,"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":0.0778999999165535,"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/adversarial-system","display_name":"Adversarial system","score":0.7777000069618225},{"id":"https://openalex.org/keywords/context","display_name":"Context (archaeology)","score":0.7401999831199646},{"id":"https://openalex.org/keywords/concordance","display_name":"Concordance","score":0.43700000643730164},{"id":"https://openalex.org/keywords/harm","display_name":"Harm","score":0.36309999227523804},{"id":"https://openalex.org/keywords/vulnerability","display_name":"Vulnerability (computing)","score":0.3476000130176544},{"id":"https://openalex.org/keywords/foundation","display_name":"Foundation (evidence)","score":0.3165999948978424},{"id":"https://openalex.org/keywords/scale","display_name":"Scale (ratio)","score":0.31459999084472656}],"concepts":[{"id":"https://openalex.org/C37736160","wikidata":"https://www.wikidata.org/wiki/Q1801315","display_name":"Adversarial system","level":2,"score":0.7777000069618225},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.7401999831199646},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.45570001006126404},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4472000002861023},{"id":"https://openalex.org/C15744967","wikidata":"https://www.wikidata.org/wiki/Q9418","display_name":"Psychology","level":0,"score":0.44029998779296875},{"id":"https://openalex.org/C160798450","wikidata":"https://www.wikidata.org/wiki/Q4230870","display_name":"Concordance","level":2,"score":0.43700000643730164},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.40619999170303345},{"id":"https://openalex.org/C2777363581","wikidata":"https://www.wikidata.org/wiki/Q15098235","display_name":"Harm","level":2,"score":0.36309999227523804},{"id":"https://openalex.org/C95713431","wikidata":"https://www.wikidata.org/wiki/Q631425","display_name":"Vulnerability (computing)","level":2,"score":0.3476000130176544},{"id":"https://openalex.org/C2780966255","wikidata":"https://www.wikidata.org/wiki/Q5474306","display_name":"Foundation (evidence)","level":2,"score":0.3165999948978424},{"id":"https://openalex.org/C2778755073","wikidata":"https://www.wikidata.org/wiki/Q10858537","display_name":"Scale (ratio)","level":2,"score":0.31459999084472656},{"id":"https://openalex.org/C180747234","wikidata":"https://www.wikidata.org/wiki/Q23373","display_name":"Cognitive psychology","level":1,"score":0.2865999937057495},{"id":"https://openalex.org/C71924100","wikidata":"https://www.wikidata.org/wiki/Q11190","display_name":"Medicine","level":0,"score":0.2827000021934509},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.28220000863075256},{"id":"https://openalex.org/C93226319","wikidata":"https://www.wikidata.org/wiki/Q193137","display_name":"Differential (mechanical device)","level":2,"score":0.28119999170303345},{"id":"https://openalex.org/C97256817","wikidata":"https://www.wikidata.org/wiki/Q1462316","display_name":"Spurious relationship","level":2,"score":0.275299996137619},{"id":"https://openalex.org/C109359841","wikidata":"https://www.wikidata.org/wiki/Q728944","display_name":"Inclusion (mineral)","level":2,"score":0.2685000002384186},{"id":"https://openalex.org/C37335422","wikidata":"https://www.wikidata.org/wiki/Q6888134","display_name":"Model-based reasoning","level":3,"score":0.2605000138282776},{"id":"https://openalex.org/C184356942","wikidata":"https://www.wikidata.org/wiki/Q830382","display_name":"Best practice","level":2,"score":0.2596000134944916},{"id":"https://openalex.org/C2779473830","wikidata":"https://www.wikidata.org/wiki/Q1540899","display_name":"MEDLINE","level":2,"score":0.25589999556541443},{"id":"https://openalex.org/C112930515","wikidata":"https://www.wikidata.org/wiki/Q4389547","display_name":"Risk analysis (engineering)","level":1,"score":0.2524000108242035},{"id":"https://openalex.org/C183322885","wikidata":"https://www.wikidata.org/wiki/Q17007702","display_name":"Context model","level":3,"score":0.25209999084472656},{"id":"https://openalex.org/C2522767166","wikidata":"https://www.wikidata.org/wiki/Q2374463","display_name":"Data science","level":1,"score":0.25040000677108765}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2603.14158","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.14158","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":"doi:10.48550/arxiv.2603.14158","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.14158","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":false,"raw_source_name":null,"raw_type":"article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Large":[0],"language":[1],"models":[2,45,107,129],"(LLMs)":[3],"are":[4],"entering":[5],"clinician":[6,13,61,66,73,186],"workflows,":[7],"yet":[8],"evaluations":[9],"rarely":[10],"measure":[11],"how":[12],"reasoning":[14,39,57,114],"shapes":[15],"model":[16,139],"behavior":[17],"during":[18],"clinical":[19],"interactions.":[20],"We":[21],"combined":[22],"61":[23],"New":[24],"England":[25],"Journal":[26],"of":[27,164],"Medicine":[28],"Case":[29],"Records":[30],"with":[31,75,147,204],"92":[32],"real-world":[33],"clinician-AI":[34,216],"interactions":[35],"to":[36,85,94,145,199],"evaluate":[37],"21":[38,106],"LLM":[40],"variants":[41],"across":[42,104,167],"8":[43],"frontier":[44],"on":[46],"differential":[47,79],"diagnosis":[48,80,102,196],"generation":[49],"and":[50,63,87,116,201,221,227],"next":[51,89],"step":[52,90],"recommendations":[53,91,166],"under":[54],"three":[55],"conditions:":[56],"alone,":[58],"after":[59,64,72,192],"expert":[60],"context,":[62],"adversarial":[65,121,148,193],"context.":[67],"LLM-clinician":[68],"concordance":[69],"increased":[70],"substantially":[71],"exposure,":[74],"simulations":[76],"sharing":[77],">=3":[78,88],"items":[81],"rising":[82],"from":[83,92,142],"65.8%":[84],"93.5%":[86],"20.3%":[93],"53.8%.":[95],"Expert":[96],"context":[97,122,194],"significantly":[98],"improved":[99,189],"correct":[100],"final":[101],"inclusion":[103,197],"all":[105],"(mean":[108,130],"+20.4":[109],"percentage":[110,132],"points),":[111],"reflecting":[112],"both":[113],"improvement":[115],"passive":[117],"content":[118],"echoing,":[119],"while":[120],"caused":[123],"significant":[124],"diagnostic":[125,190],"degradation":[126],"in":[127],"14":[128],"-5.4":[131],"points).":[133],"Multi-turn":[134],"disagreement":[135],"probes":[136],"revealed":[137],"distinct":[138],"phenotypes":[140],"ranging":[141],"highly":[143],"conformist":[144],"dogmatic,":[146],"arguments":[149,206],"remaining":[150],"a":[151,212],"persistent":[152],"vulnerability":[153],"even":[154],"for":[155,214,225],"otherwise":[156],"resilient":[157],"models.":[158],"Inference-time":[159],"scaling":[160],"reduced":[161,202],"harmful":[162],"echoing":[163],"clinician-introduced":[165],"WHO-defined":[168],"harm":[169],"severity":[170],"tiers":[171],"(relative":[172],"reductions:":[173],"62.7%":[174],"mild,":[175],"57.9%":[176],"moderate,":[177],"76.3%":[178],"severe,":[179],"83.5%":[180],"death-tier).":[181],"In":[182],"GPT-4o":[183],"experiments,":[184],"explicit":[185],"uncertainty":[187],"signals":[188],"performance":[191],"(final":[195],"27%":[198],"42%)":[200],"alignment":[203],"incorrect":[205],"by":[207],"21%.":[208],"These":[209],"findings":[210],"establish":[211],"foundation":[213],"evaluating":[215],"collaboration,":[217],"introducing":[218],"interactive":[219],"metrics":[220],"mitigation":[222],"strategies":[223],"essential":[224],"safety":[226],"robustness.":[228]},"counts_by_year":[],"updated_date":"2026-03-18T06:31:55.123368","created_date":"2026-03-18T00:00:00"}