2026年临床人工智能（AI）发展状况研究报告（英文版）-

Stateof

ClinicalAIReport

2026

ARISE-AI.ORGJanuary,2026

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AboutTheAuthors

PeterBrodeur

Dr.PeterBrodeur

isarisingcardiologyfellowatHarvardMedicalSchool’sBethIsraelDeaconessMedicalCenter.Dr.BrodeurisanaffiliateofARISE,reviewerforNatureMedicine&NEJMAI,andformerlifesciencesstrategyconsultant.HisresearchfocusesonhumancomputerinteractionandLLMclinicalreasoning.

EthanGoh

Dr.EthanGoh

istheExecutiveDirectorofARISE.HisresearchhasbeenfeaturedinTheNewYorkTimes,TheWashingtonPost,andCNN.HedirectstheStanfordHealthcareAILeadershipProgram,andHarvard’sAgenticAIExecutiveCourse.Dr.GohisaFoundingEditorialBoardmemberandAssociateEditoratBMJDigitalHealth&AI.

AdamRodman

Dr.AdamRodman

isanassistantprofessoratHarvardMedicalSchool.HeistheDirectorofAIProgramsfortheCarlJ.ShapiroCenter.Dr.RodmanisanAssociateEditoratNEJMAI.HeisalsothehostoftheAmericanCollegeofPhysicianspodcastBedsideRounds.

JonathanHChen

Dr.Jonathan

HChenisStanford’sinauguralDirectorforMedicalEducationinAIintheDivisionofComputationalMedicine.Hisexpertisecombininghumanwithartificialintelligencetoprovidebetterhealthcarethaneitheraloneisfeaturedinthepopularpresswithover100publicationsandawards.

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MessageFromARISELeadership

“Therearedecadeswherenothinghappens;andthereareweekswhendecades

happen.”Recentdeploymentsbytechnologycompanies,healthsystems,andregulatorshavemadeclinicalAImorevisibleandevermoreconsequential.Atthesametime,ithasbecomehardertokeepupwithemergingresearch.Insomeareastheliteratureis

fragmented;inothers,itsimplydoesn’texistyetforthewaythesetoolsarebeingusedtoday.

Sowhatactuallyholdsupinpractice?

TheStateofClinicalAIReport(2026)wascreatedtolookbeyondmodelperformancealonetoothercriticalfactorsthatdeterminereal-worldimpact:howsystemsare

evaluated,howcliniciansandAIworktogether,andwherepatientrisksstarttoappear.

FrontierAIsystemsarealreadypowerful.What’sneedednowistosafelyandeffectivelytranslatethesetoolsintoreal-worldcare.

EthanGoh,AdamRodman,JonathanHChen

Investigators,ARISENetworkARISE-AI.ORG

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EngagementandEducation

Stanford

Computational

MedicineColloquia

●HealthcareAIseminarswithStanford/industryleaders

●Thursday12pmPT,free

Getweeklyinvites

StanfordHealthcareAILeadership&

StrategyProgram

●Applicationrequired.CMEandaccreditedcertificate

●May2026

Applynow

GenerativeAIandAgenticAIOnlineCourse

●Harvard/Stanfordfaculty,accreditedcertificate

●Summer2026

Getearlyaccess

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TheCurrentLandscape

ClinicalAIIsWidelyDeployedButPoorlyEvaluated

●AIisnowembeddedacrosshealthcare:1,200+FDA-clearedtoolsand350,000+consumerappshavegenerateda$70Bmarket

1

.Onlyaminorityunderwentpeer-reviewedevaluation.

2

●Of691FDA-clearedAI/MLmedicaldevices(1995–2023),>95%wentthroughthe510(k)clearance

pathway,whichispredicatedonequivalencytoexistingdevices—manyofwhichwereapprovedonsuboptimalevidence.2

●~50%ofFDAdevicesummariesomittedstudydesign,53%lackedsamplesize,and<1%reportedpatientoutcomes.2

●95%ofdevicesummariesdidnotreportdemographicdata,and91%lackedbiasassessments,raisingconcernsaboutsafetyandequityinreal-worlduse.2

Bridgingthegapbetweenadoptionandevidencerequiressupportingclinicians,healthsystemleaders,policymakers,andthepublicininterpretingavailableresearch.

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TopTakeaways

1.Modelcapabilityisaccelerating,butevidenceofrealclinicalimpactremains

limited.Manystudiesshowwhatmodelscandoincontrolledsettings;what’s

increasinglyneededareprospectivestudiesthatshowmeasurableeffectsonpatientoutcomesandcaredelivery.

2.FrontierLLMmodelsshowveryunevenperformance.Theyperformextremelywelloncomplexreasoningtasks,yetbreakdownwhenuncertainty,missinginformation,orchangingcontextisintroduced.

3.Cliniciansvalueautomationwhereitreducesadministrativeandworkflowburden,buttheseusecasesremainunderstudied.Taskscliniciansmostwantsupportwithareoftenunderrepresentedincurrentbenchmarksandevaluations.

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TopTakeaways

4.Patient-facingAIhassignificantpotentialtoreshapeengagementandaccess,butraisesdistinctsafetyconcerns.Directinteractionwithpatientsrequiresmuchstrongerguardrailsandscalableoversightsystemsthatdonotcurrentlyexist.

5.MultimodalclinicalAIapplicationsareapproachingpracticalusability.

Improvementsinbasemodelsareenablingapplicationsthatintegrateunstructuredtext,images,andotherclinicaldatatosupportpredictionanddecision-makinginreal-worldsettings.

6.FDAclearanceisincreasing,butnear-termclinicaladoptionwillfavornarrow,task-specificsystems.AItoolsthataretightlyscopedtospecificdomainsand

contextsaremorelikelytodemonstratevalueandbeadoptedinpractice.

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Acknowledgements

Reviewers

SupportedBy

RebeccaHandler

KathleenLacar

JasonHom

KameronBlack

EricHorvitz

LiamMcCoy

LauraZwaan

DavidWu

VishnuRavi

PriyankJain

BrianHan

EmilyTat

KevinSchulman

AdrianHaimovich

Design&Accessibility

EmilyTat

TheorganizationformatofthisreportwasinspiredbyNathanBenaich’sStateofAIReport.

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HowtoCiteThisReport

PeterG.Brodeur,EthanGoh,EmilyTat,LiamMcCoy,DavidWu,PriyankJain,Rebecca

Handler,JasonHom,LauraZwaan,VishnuRavi,BrianHan,KevinSchulman,KathleenLacar,KameronBlack,AdrianHaimovich,EricHorvitz,AdamRodman,JonathanH.Chen“StateofClinicalAI2026,”ARISENetwork,January2026.

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Introduction

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ExecutiveSummary

ModelPerformance

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Frontierreasoningmodels(optimizedformulti-stepinferenceandchainofthought)showedmarkedimprovementonchallengingclinicalreasoningtasksagainsthumanbaselineswhilepredictionmodelscrossednewthresholdsinscalablepredictiontoenableactionableprevention.

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Dominantfailuremodesincludemodelrecognitionofuncertainty,overconfidence,andpatternlearning.

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Benchmarks&Evaluation

Multiplechoicebenchmarksaresaturatedandevaluationsstillunderrepresentrealclinicalwork:administrativetasks,conversationaldialogue,realpatientdata,andbias/fairness.

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Newbenchmarksuites(e.g.,conversational,simulatedEHRenvironments)areforcingmodelsintomorerealistic,dynamicscenarios.

FoundationalMethods

Noveltechniquessuchasconvertingmedicaldatatotokensusedforpredictionbringsaneweraofscreeningandriskstratification.

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ClinicalAIisbeingadvancedbymultiagentsystems,multimodaldiagnosticsupport,andoptimizingreasoningmodels.

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ExecutiveSummary

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AIinClinicalWorkflows

Acrosssettings,AIcanaugmentcliniciansonreasoninganddiagnosticinterpretationtasks.However,collaborationisn’tyetoptimized.HowcliniciansuseAIisasimportantaswhatthemodelcando.

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WorkflowtoolslikeAIscribesfeeltransformative,yetobjectivegainsarestillmodest.Theadditionofdownstreamworkflowtaskswilllikelyyieldmoreproductivityandefficiencyimpact.

PatientFacingAI

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Multi-turnconversationalagentsandAI-basedcoachingshowpromise,particularlyastheyareintegratedwithsmartdevicestosupportmorepersonalizedhealthassistance.

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Inaspacewithcompetingvendorinterests,overtrustandunsuperviseduseraisethebarforguardrailsandforimprovingobjectivepatientoutcomes,notjustengagement.

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AppliedAI&Demos

Themostimmediatetranslatableprogresscanbeseenattheindividualtask-specificlevelwithimagingremainingthedominantusecase.

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WeprovideasneakpeekofthenextwaveoftoolssuchasEHRchatbots,eConsults,andmentalhealthchatbots.

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Methods

OurApproachtoaTargetedReviewofClinicalAI

●Datasources&searchstrategy

。ReviewedPubMed,preprintservers(e.g.,medRxiv,arXiv)usingacombinationoftermssuchas“largelanguagemodelsinmedicine,”“AI,”“diagnosticreasoning,”

“managementreasoning,”“diagnosticerror,”“benchmarks,”and“patient-facingAI.”

。InvitedcliniciansandAIresearchersfromacademicinstitutionsandissuedanopencallforsubmissionsviasocialmedia(e.g.,

LinkedIn

)toidentifyhigh-qualitystudiesacrossthesixthemes.

●Studyselection

。Allstudiesreviewedbyauthorsandreviewersofthispresentation.

。Includedempiricalstudiesthat(1)usedanAImodel/LLMinaclinicalcontext,(2)

reportedquantitativeorqualitativeoutcomes(e.g.,diagnosticaccuracy,bias,

calibration,workflow,userperformance),and(3)determinedtobeofhighimpact.

。Excludedpurelytechnicalmodelpaperswithoutclinician-orpatient-facingevaluation,editorials,andnon-clinicalAI(e.g.,drugdiscovery,biotech).

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TableofContents

ModelPerformance

Howwellmodels(trainedAI

systems)performindependently

acrosspredictionandreasoning

tasks.

FoundationalMethods

Noveltechniquesthatoptimize

clinicalAIperformanceaboveoff

theshelfmodels.

Benchmarks&Evaluations

TheevolvingmetricsthatdefineAIcompetenceinmedicine.

AIinClinicalWorkflows

HowcliniciansandAIsystems

collaborateinrealorsimulated

environments.

AppliedAI&Demos

DemonstratingAI’sdomain

specificapplicationsanduse

cases.

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PatientFacingAI

HowAIengagesdirectlywith

patientstoinform,support,and

personalizetheirhealthcare.

Model

Performance

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ModelPerformance

In2025,frontiermodelsmademajorleapsinautonomousclinicalreasoningandprediction.

●Slides18–20:Reasoningfrontiermodelsshowlargegainsinautonomousclinicalreasoningversushumans,includingonhistoricallydifficultcases.

●Slides21–22:Keyweaknessespersist:poorperformanceinuncertainty-heavyscenarios,overconfidence,andpattern-basedshortcutbehavior.

●Slides23–27:Modelscontinuetoshowpromiseforscalablepredictionacrossawidevarietyofusecasessuchaspatientdeterioration,screeningforinsulinresistance,andaging.

Overall,model-onlyevaluationsrevealthatLLMshaveachievedsuperhumancapabilityincontrolledtasksbutstillrequirestrongermetacognition,calibration,andstresstestingbeforeautonomousdeployment.

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ModelPerformance

Prediction

ComplexReasoning

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Approachingsuperhumanreasoning

●AIvsMD

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LLMvsPrimaryCarePhysician

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LLMasanexpertcase

discussant

●Gaps

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“Noneoftheotheranswers”

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Brittleoverconfidenceand

uncertainty

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Inpatientdeterioration

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Biologicalage

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Insulinresistance

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Wearabletimeseriesdatafor

diagnosisprediction

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Clinicalriskcalculator

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Performance

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Benchmarks

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Methods

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ClinicalWorkflows

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Patient-Facing

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AppliedAI&Demos

O1-preview/o1:ReachingSuperhumanReasoningPerformance

O1-previewando1consistentlyoutperformedoratthelevelofphysiciansacrossseveralreasoningevaluations,solvingchallengingNEJMcasesatstate-of-the-artlevels,documentingsuperiorreasoningquality,excellinginmanagement

tasks,anddiagnosingrealemergencyroomcasesadmittedtothehospital.

●OnNEJMclinicopathologicalconference(CPC)cases,themodelreached78%diagnosticaccuracyandselectedthecorrectnexttest87%ofthetime.

●o1-previewachievedaperfectscore99%ofthetimeforclinical

reasoningqualitygradedbyphysicians.ThissignificantlyoutperformedGPT-4(59%)andattendingphysicians(35%).Managementreasoningforo1-preview(86%)wasalsosuperiorcomparedtoGPT-4(42%)and

physicianswithGPT-4(41%).

●InrealEDcases,themodeloutperformedoratthelevelofboth

attendingphysiciansatthreediagnostictouchpointswith66%

exact/near-exactdiagnosesvs.48–54%forphysiciansatinitialtriage.

Brodeur,Buckley,Manrai,Rodmanetal.,ArXiv,Jul.2025

●ModernLLMsmaynowsurpassphysiciansingeneraldiagnosticandmanagementreasoningincontrolledenvironments,motivatingtheneedforprospectiveclinicaltrialsforreal-worlddeployment.

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Performance

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Benchmarks

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Methods

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ClinicalWorkflows

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Patient-Facing

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AppliedAI&Demos

Google’sAMIEChatbotMatchesPCPsatMulti-VisitDiseaseManagement

Enhancedwithanewmanagement-reasoningagent,theArticulateMedicalIntelligenceExplorer(AMIE)wasnon-inferiorto21primarycarephysiciansacrossguideline-baseddecision-making,treatmentplanning,andlongitudinalcare.AMIEproducedmoreprecise,guideline-basedplans,andoutperformedphysiciansonmedication-reasoningquestions.

●

●

●

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AMIE(gemini-based)wasdesignedasatwopartsystemwithaccesstoanagentstate(currentpatientsummary,differentialetc.):afastDialogue

AgenttocapturerelevantHPIandaslowerManagementReasoningagentusinglongcontextreasoninggroundedinclinicalguidelines.

ComparedAMIEtoPCPsacross100three-visitsimulatedscenarios

spanningcardiology,pulmonology,neurology,OBGYN/urology,andGI,eachgroundedinNICEandBMJBestPracticeguidelines.

Gradedbysubspecialists,AMIE’srecommendationsforinvestigationsandtreatmentswereconsistentlymoreprecise(Yes/No),especiallyfor

investigationsinfollow-upvisits(visit2:99%vs.84%,visit3:100%vs.

88%),andcarriedexplicitcitationstoguidelinesources.Possibilityforagenticagentstoserveasapointofcontinuityinafragmentedsystem.

Onanovelmedicationreasoning(RxQA)benchmark,AMIEoutperformedPCPsonharderquestions(asdeterminedbypharmacists)inbothclosed-andopen-bookconditions,demonstratingstrongtherapeuticreasoning.

Palepu,Schaekermannetal.,ArXiv,Mar.2025

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Performance

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Benchmarks

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Methods

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ClinicalWorkflows

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Patient-Facing

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AppliedAI&Demos

AIOutperformsPhysiciansasanExpertCaseDiscussantonChallengingCases

ResearchersdevelopedDr.CaBot,anAIdiscussantbasedono3thatproduceswrittenandvideoCPC-styledifferentials.Dr.CaBotwasevaluatedonNEJMCPCsandNEJMImageChallenges,spanningtentasksthattestdifferentialdiagnosis,testingstrategies,clinicalreasoning,uncertaintyhandling,andmultimodalinterpretation.Inblindedtesting,physicianscouldnotreliablydistinguishDr.CaBotfromhumanexperts,andconsistentlyrateditsreasoninghigher.

Buckleyetal.,ArXiv,Sept.2025

●Builtfrom7,102NEJMCPCs(1923–2025)and1,021NEJMImage

Challenges,CPC-Benchcovers10reasoningtasks(DDx,testingplans,touchpoints,omission,VQA,literaturesearch,etc.).

●Amongeightfrontiermodels,o3achieved60%top-1and84%top-10accuracyonCPCdifferentialdiagnosis,outperforminga20-physicianbaseline,with98%accuracyselectingthenexttest.

●Dr.CaBot,basedono3,isapubliclyavailable(

/

)systemthatproducesbothwrittenandvideocasepresentationsthatoutperformstheoriginallypresentedexpertcasediscussant.

●ThestudyshowsthatAIisnowcapableofperformingtheentireCPCdiscussantrole,withreasoningqualityratedbetterthanhumanexperts.

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Performance

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Benchmarks

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Methods

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ClinicalWorkflows

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Patient-Facing

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AppliedAI&Demos

“Noneoftheotheranswers”:AnLLMWeakness

ResearcherstestedwhetherLLMscouldtrulyreasonthroughmedicalquestionsbyreplacingthecorrectanswerinmultiplechoicequestionswith“Noneoftheotheranswers”(NOTA).Frontiermodelsshowedsignificantdropsinaccuracy,revealingthatstrongmultiplechoiceperformance,isinpart,duetopatternrecognition.

●Researchersmodified100MedQAquestionssothat

NOTAbecamethecorrectanswer,creatinga68-item

clinician-validatedtestofgenuinereasoning.Thepatternofanswershaschangedbuttheunderlyingclinical

reasoninghasnot.

●DeepSeek-R1,o3-mini,Claude3.5Sonnet,Gemini2.0Flash,GPT-4o,andLlama3.3-70BallperformedworseonNOTA-modifiedquestions.Significantdecreasesinperformancewereexhibited,rangingfrom9%to38%.

Bedi,Shahetal.,JAMANetworkOpen,Aug.2025

●Asystemthatfallsforexamplefrom81%→43%

accuracywhenapatternchangeswouldbeunsafeforautonomousclinicaluse;rigorousbenchmarksmusttestreasoning,notmemorizedanswerdistributions.

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Performance

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Benchmarks

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Methods

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ClinicalWorkflows

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Patient-Facing

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AppliedAI&Demos

ScriptConcordanceTestingRevealsGapsinLLMClinicalReasoning

Astudycompared10frontiermodelsto1,500+clinicianson750ScriptConcordanceTesting(SCT)questions,whichmeasuretheabilitytoreviseclinicaldecisionswhennewinformationbecomesavailable.Modelsmatchedmedicalstudentsbutunderperformedrelativetoseasonedphysicians,revealingconsistentoverconfidenceanddifficulty

updatingdecisionsunderuncertainty.

●SCTmeasurestheabilitytorevisediagnosticormanagementjudgmentswhennewinformationarrives,acoreskillofclinicalreasoningunder

uncertainty.

●Thisstudyestablishedabenchmarkassessing750SCTitemsfrom10datasets,includingpediatrics,neurology,emergencymedicine,internalmedicine,andphysiotherapy,mostneverpreviouslypublished.

●OpenAI’so3(68%)ledperformance,followedbyGPT-4o(64%),matchingmedicalstudentsbutbelowresidentsandattendingphysicians.Many

reasoningmodelsperformedsurprisinglypoorly(e.g.,Gemini2.5:52%).

McCoy,Rodmanetal.,NEJMAI,Sept.2025

●LLMsoverusedextremeratings(+2/-2),rarelyselectedneutrality(0),andshowedmiscalibratedconfidencepatternsunlike

humanexperts,suggestingthatchain-of-thought–optimizedmodelsmayovercommitinuncertainty-richtasks.

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Performance

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Benchmarks

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Methods

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ClinicalWorkflows

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Patient-Facing

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AppliedAI&Demos

PredictingInpatientDeteriorationBeforeItHappens

Researchersdevelopedadeep-learningmodelusingcontinuouswearablevitalsigndatafrom888hospitalizedmed-surgpatientstopredictclinicaldeteriorationupto8-24hoursbeforestandardEHRalerts.Themodelgeneratedmoretimelyalertsthanepisodicvitalchecksandaccuratelypredictedhardoutcomes,includingICUtransfer,cardiacarrest,and

death.

●OutsideoftheICU,inpatientvitalsignsarecheckedevery4-8hours,whichleavestimegapsofmissedopportunityfordetectingcriticalillness.

●Researcherstrainedarecurrentneuralnetworkwitha5hoursequenceofcontinuousvitalsigninputs(e.g.,HR,RR)collectedfromawearablechestdevice,withdemographicsfrom888non-ICUpatientstodetectearlydeterioration.

●Predicted9xmoreclinicalalerts(ModifiedEarlyWarningScore(MEWS)>6for>30mins)8-24hoursbeforeEHR-basedMEWSalerts,with

AUROC0.89(retrospective)andAUROC0.84-0.9(prospective).

Predicted9of11hardoutcomeevents(cardiacarrests,death)upto17hoursbeforeMEWS.

●Enablesfasterrecognitionofphysiologicdeclineandthepotentialtopreventavoidabledeteriorations.

Scheid,Zanosetal.,NatureCommunications,Jul.2025

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Performance

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Benchmarks

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Methods

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ClinicalWorkflows

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Patient-Facing

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AppliedAI&Demos

PredictingBiologicalAgingatPopulationScaleUsingLargeLanguageModels

ThisstudyintroducesanLLMpromptbasedframeworkthatpredictsbiologicalagefromroutinehealthrecords,enablingscalableagingassessmentacrosspopulations.Appliedto>10millionindividualsfromsixcohorts(e.g.,UKBiobank),theLLM-derivedbiologicalageoutperformedtraditionalagingclocksinpredictingmortalityandmultipleage-related

diseases.

●UsingLLMsintheLlamaandQwenfamilies,appliedpromptlearningwithoutsupervisedlearningonagingrelatedknowledge.Afterbeingfedhealth

examinationtextreports,LLMsintegrateindividualizedclinicaldatatoinferbiologicalagewithoutpredefinedbiomarkersorlabels.

●LLM-basedbiologicalageachievedaconcordance-indexof0.76for

all-causemortality.Alsooutperformedepigeneticclocks,telomerelength,frailtyindex,andconventionalMLmodels.Thedifferencebetween

LLM-predictedageandchronologicalage(“age-gap”)wasstronglyassociatedwithall-causemortality(HR1.05).

●LLM-derivedorgan-specificbiologicalagesbetterpredictedcorrespondingorgandiseasesandenabledpotentialdiscoveryof316aging-relatedproteinbiomarkers.

●Potentialforscalableandcost-effectivepersonalizedandpopulationagingassessmentwithinterpretabilityusingchainofthoughtprompts.

Li,Dietal.,NatureMedicine,Jul.2025

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Performance

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Benchmarks

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Methods

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ClinicalWorkflows

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Patient-Facing

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AppliedAI&Demos

PredictingInsulinResistanceUsingWearables+RoutineLabsatScale

Researcherspairedsmartwatch-deriveddata(Fitbit/PixelWatch)withdemographicsandroutinebloodbiomarkersto

predictinsulinresistanceusingdeepneuralnetworkmodels.Thebest-performingpracticalmodel(wearables+

demographics+commonlabs)substantiallyoutperformedsingle-sourcemodelsandmaintainedsimilarperformanceinanindependentvalidationcohort.Performancewasstrongestinhighriskgroups(obesity+sedentary).

●Currentmethodsfordetectingearlyinsulinresistancerelyonsnapshotsintime(e.g.,A1c)whichcanbeinsensitiveinearlystages.

●In1,165participants,usingaHomeostaticModelAssessmentofInsulinResistance(HOMA-IR)>2.9asgroundtruth,usingonlydemographic

variablesandwearabledata,themodelachievedanAUROC0.7.AddingfastingglucoseincreasedperformancetoAUROC0.78.

●Combiningwearables+demographics+fastingglucose+lipid/metabolic

panelsachievedAUROC=0.80,76%sensitivity,84%specificity.Performancewasbestinobese+sedentaryparticipantswith93%sensitivityand95%

adjustedspecificity(minimizesmisclassificationofinsulinsensitiveasresistant).Similarperformanceinavalidationsetof72participants.

●WhentheseinsulinresistancepredictionswereintegratedintoanLLM

coachingagent,endocrinologistsconsistentlyrateditsuperiortoabaseLLMinhead-to-headcomparisonsforpersonalization,comprehensiveness,andtrustworthiness.

Metwally,Prietoetal.,ArXiv,Apr.2025

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Performance

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Benchmarks

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Methods

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ClinicalWorkflows

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Patient-Facing

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AppliedAI&Demos

AFoundationModelforWearableBehavioralDatawithIndividualLevelDiagnosticPrediction

JointEmbeddingforTimeSeries(JETS)isaself-supervisedjoint-embeddingmodeltrainedon~3millionperson-daysofreal-worldwearableandbehavioraldatafrom16,522individuals.Bylearningrobustlatentrepresentationsfromnoisy,

irregulartimeseries,JETSimprovesdownstreampredictionofdiagnosesandbiomarkerscomparedwithmultiplebaselinemodels.

●Manytimeseriesmodelsrelyondense,regularlysampled,fixed

lengthinputsthatoftenisnotcongruentwithrealworlddata.

Joint-embeddingpredictivearchitecture(JEPA-style)withmasking,learnstopredictmissingsegmentsinlatentspaceinsteadof

reconstructingrawsignals.

●Trainedon63dailyorlow-resolutionmetrics(activity,sleep,HR,

VO₂max,respiration,self-reports),covering~3Mperson-daysacross16,522users.

●OutperformedMAE,PrimeNet,andtransformerbaselinesonmanydiagnoses(e.g.,AUROCME/CFS0.81,HTN0.87))andledbiomarkerpredictiondespitesparselabels.

●JETSshowsthatafoundationmodeltrainedonmassivewearabletime-seriescanlearngeneralizablehealthrepresentationsthat

outperformexistingapproachesonrealclinicalpredictiontasks.

Xie,Ballingeretal.,OpenReview,Dec.2025

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Performance

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Benchmarks

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Methods

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ClinicalWorkflows

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Patient-Facing

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AppliedAI&Demos

AgentMD:UsingLLMAgentstoRunClinicalRiskCalculatorsforRiskPredictionatScale

Clinicalcalculatorsareimportantmedicaltoolsbutremainunderutilizedduetopoordissemination,workflowburden,andfragmentedimplementation.AgentMDisanAIagentthatreadsnotes,determineswhichcalculatorsapply,extractsinputs,andutilizesclinicalcalculators,enablingaccurateandinterpretableriskprediction.

●AgentMDautomaticallyconvertedPubMedarticlesinto2,164executableclinicalcalculators,achieving>85%accuracyonexpertqualitychecksand>90%passratesonunittesting.

●Onacontrolledbenchmark(RiskQA-requiresselectingthe

correctcalculator,computing,andinterpretation),Agent