杰富瑞-中国医疗：AI2.0：医疗健康的下一张处方-AI 2.0：Healthcare's Next Prescription-20260708

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China(PRC)|Healthcare

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AI2.0:Healthcare'sNextPrescription

WhyareMNCswritingcheckstoalmosteveryChineseAIDDcompany?It'snotaboutcertainty,it'saboutexposure-MNCsareinvestingbecausetheycan'taffordnotto.HCAIismovingfromexperimentationtocommercialization,whereweseefourthemesemerging:AIDD,BCI,intelligentmedtech,andHCAIagents;ofwhichwebelieveAI-nativebiotechplatformsofferthemostcompellingrisk-reward.BuyInsilico,METiS,Mindray,UIH,MedBot,JDH.

Inour

AIHealthcare1.0report

,wehighlightedAI'spotentialtoimproveaccessibility,accuracy,andefficiencyacrosshealthcare.Theindustryhassincemovedbeyondexperimentation,withmilestonessuchasInsilico'sRentosertibadvancingintoPhase3andChina'sfirstapprovedBCIproductforparalysis.AsAIentersclinicalvalidationandcommercialization,weseefourinvestablethemesemerging:AIDD,BCI,intelligentmedtech,andhealthcareAIagents.

AIDrugDiscovery=Data+Algorithms+Workflow,DrivingFaster,Smarter,andMorePredictableDrugR&D-HealthcareAIismorethanascienceproject-it'sbecomingacommercialreality.Inourview,thebiggestriskinhealthcareAImaynolongerbeinvesting,butnotinvesting.WithInsilicoadvancingintoPh3andChina'sfirstBCIapproved,AIismovingfromconcepttocommercialization.

AIDDisevolvingfromsoftware-drivenefficiencytoanAI+biotechassetcreationstory.Byintegratingdata,algorithms,andautomatedworkflowsintoaclosed-loopR&Dengine,AIcouldreducedrugdiscoverytimelinesby70-90%andsave>US$50bnofvalueacrossthedrugdevelopmentchain.Our

survey

indicatedAIadoptionisbroadeningacrossthepharmavaluechain,withthehighestpenetrationindiscovery(67-70%)andmanufacturing(82%),followedbyclinicaldatamgmt(56-67%)andregulatoryintelligence(44%).

Webelievethenextgenerationofwinnerswillbedefinednotbymodelperformancealone,butbytheirabilitytotranslateAI-generatedinsightsintodifferentiateddrugassetsviaproprietarydata,closed-loopdry-wetlabs,andin-housedevelopmentcapabilities.Drugdiscovery'siterativedesign-test-learncycleisanaturalfitforAI,andChinaoffersacompellingenvironmentforthismodel,supportedbydeepscientifictalent,richdatageneration,andcost-efficientexperimentation.WeprefertheAI+biotechmodel,asitcapturesvaluefrombothplatformmonetizationandproprietarypipelines.Relatedcos:

Insilico

,

METiS

,

Xtalpi

.

Brain-ComputerInterfaces:DecodingThoughtsintoTherapies-BCIsareprogressingfromrehabilitationdevicestotherapeuticplatforms.Whilestillearly,advancesinAIandneuraldecodingarebringingclinicaladoptioncloser,withinvasiveandultrasound-basedinterfacesrepresentingthemostpromisinginnovationfrontiers.Relatedcos:BrainCo,Neuracle,StairMed.

Medtech:TurningHardwareintoIntelligentPlatforms-AIisreshapingmedtechacrossimaging,robotics,anddiagnostics,movingtheindustryfromhardware-centricproductstointelligentplatforms.WebelieveleaderssuchasUIH,MindrayandMedBotarebestpositionedtobenefitfromthistransitionthroughintegrated"AI+equipment"solutionsthatimproveclinicaloutcomesandoperatingefficiency.Relatedcos:

Mindray

,

UnitedImaging

,

MedBot

.

AIAgents:FromChatbottoHealthcareOperatingSystem-HealthcareAIagentsaremovingupthevaluechain-fromconsumerchatbotstophysiciancopilotsandultimatelyhospitalandpayerinfrastructure.Eachstepunlockslargerbudgets,deeperworkflowintegration,andstrongermonetizationopportunities,shiftingAIfromatraffic-generationtooltoacoreoperatinglayerofthehealthcaresystem.Relatedcos:

JDHealth

,

AliHealth

,

PAGD

.

Source:Jefferies

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TableofContents

PartI:AIdrugR&D–DisruptionorBreakthrough?3 1.1AIDD=Data+Algorithms+Workflow3 1.2AIAimstoImproveEfficiency,Accuracy,PredictabilityindrugR&D4 1.3DivergingPathsforSmallandLargeMoleculesinAIDD7 1.4TheAIDrugR&DRace:WhoWillPrevail?11PartII:Brain-ComputerInterface(BCI)14 2.1TechnologyRoutesofBrain-ComputerInterface14 2.2BCIIndustryRoadmap:FromEarlyNeurosciencetoScalablePlatforms16 2.3TechnicalPrinciplesBehindBCI18PartIII:MedicalEquipmentandHospitalManagement20 3.1MedicalImaging20 3.2Surgicalrobots22 3.3In-vitroDiagnostics(IVD)23PartIV:China’sBigTechRacetoBuildHealthcareAIAgents24 4.1SpecializedLLMsExtendingUpstream25 4.2FollowtheMoney:WhoPaysforHealthcareAIAgents?25 4.3StructuralConstraints-RealbutNarrowerthanOneYearAgo26 4.4MajorHealthcareAILLMsinChina:PositioningtheBigFive27

InsilicoMedicine-FromCodetoCure-AIIntegratedDrugR&DEngine;InitiateatBUY

30

METiSTechBio-DeliveringtheFuture-WhereAIMeetsNanomedicine;InitiateatBUY

30

XtalPi-TransitioningintoAScalableAI-drivenR&DPlatform31

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PartI:AIdrugR&D–DisruptionorBreakthrough?

1.1AIDD=Data+Algorithms+Workflow

ThecoreofAIDDisthetransformationoftraditional,fragmenteddrugdevelopmentintoadata-driven,closed-loopsystemthatreplaces"randomdiscovery"with"rationaldesign."Byintegratingthreemostimportantelements,bigdata,algorithms,andworkflow,AIDDseekstosolvethefundamentalchallengesofhighcosts,longtimelines,andhighfailureratesinthepharmaceuticalR&D.

•Data:MiningBiologicalInsights-Bigdataactsasthe"fuel"forAIDD,isusednotonlytotrainLLMsbuttoenableresearcherstoidentifytherightbiologicaltargetswithhigherconfidencethanthetraditional"shotgun"approach.

•Algorithms:FromScreeningtoGenerativeDesign-Algorithmsserveasthe"brain"ofAIDD(e.g.Insilico'sPharma.AIsuite),evolvingfrompassivetoolsthatfilterexistinglibrariestoactivesystemsthatcreatenewmolecules.

Chart1-InSilico'sPharma.AIPlatform

Source:Companydata

•Workflow:WorkflowdrivesacontinuouslearningcycleforAIDD:bigdatatotrain,algorithmstocreate,andwetlab(automation)tovalidatethatacceleratesAImodelinnovationandthuscontinuallyimprovesdrugdiscoveryplatform.Besides,automationprovidesthe"physicalframework"(e.g.Xtalpi'sXmartChemaswet-labrobot;METiS'NanoForgeintegratinghigh-throughputwet-labscreening)tovalidatethemoleculethatallowsdataandalgorithmstointeractinacontinuousfeedbackloop,oftenreferredtoas"dry-lab"(computational)and"wet-lab"(experimental)integration.

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Chart3-XtalPiXmartChemLCMSWorkstation(automated)

Chart2-TraditionalGloveBoxWorkstation

Source:XtalPi

Source:XtalPi

Chart4-End-to-end,integrateddry-wetAIDDworkflow

Source:Jefferiesresearch

1.2AIAimstoImproveEfficiency,Accuracy,PredictabilityindrugR&D

AIDDisfundamentallyreshapingpharmaceuticalR&Dbyreplacingsequential,labor-intensiveexperimentationwithparallel,predictive,anddesign-drivenworkflows.Byleveraginglarge-scalebiologicaldatasets,advancedalgorithms,andautomatedlaboratoryplatforms,AIenablescompaniestocompressdiscoverytimelinesfromyearstomonthswhilesignificantlyreducingthecapitalrequiredtoachievekeydevelopmentmilestones.Althoughchallengesremainintranslatingpreclinicalsuccessintolate-stageclinicaloutcomes,AIDDhasalreadydemonstratedhigherhitratesfornovelmoleculediscoveryandislayingthegroundworkformorepersonalizedtherapiesthroughenhancedpatientstratificationandbiomarker-drivendevelopment.

Efficiency:AcceleratingTimelinesandReducingCosts

•AIsubstantiallyimprovesR&Dproductivitybyshorteningdevelopmenttimelinesandloweringcostsacrossthevaluechain.AccordingtoFrost&Sullivan,AI-drivenplatformscanreduce

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drugdiscoverytimelinesby70-90%,preclinicaldevelopmenttimelinesby50-80%,andclinicaldevelopmenttimelinesby50-60%comparedwithtraditionalfirst-in-class(FIC)drugdevelopment.Beyondspeed,AIalsodeliverssignificanteconomicbenefits.InFICprograms,AIisestimatedtogenerateuptoUS$26bninsavingsduringdrugdiscoveryandanadditionalUS$28bnduringclinicaldevelopment,resultingintotalpotentialsavingsofuptoUS$54bnacrossthepharmaceuticalR&Dvaluechain.

Chart5-CostandTimeSavingofAIDDvsTraditionalDrugR&D

Source:LiteratureReview,Frost&SullivanAnalysis

Accuracy:EnhancingMolecularDesignandPatientSelection

•AIimprovesdevelopmentaccuracybyenablingthedesignofbiologicallyrelevantmoleculesandtheidentificationofpatientsmostlikelytobenefitfromtreatment,therebyreducingtheinefficienciesassociatedwithtraditionaltrial-and-errorapproaches.Forexample,Recursion’sAI-enabledchemistryplatformrequiresanaverageofonly330synthesizedcompoundsandapproximately17monthstoadvanceadrugcandidate,comparedwiththeindustryaverageofmorethan2,500compoundsand42months.Thismoretargetedanddata-drivenapproachincreasesthelikelihoodofgeneratinghigh-qualitycandidateswhilereducingresource-intensiveexperimentation.

Predictability:De-riskingDevelopmentThroughEarlyInsights

•PerhapsthemosttransformativeaspectofAIisitsabilitytopredictoutcomesearlyinthedevelopmentcycle,allowingcompaniestoidentifypotentialfailuresbeforesubstantialresourcesarecommitted.Thisimprovesdecision-making,reducesdownstreamattrition,andincreasestheprobabilityofsuccessforcandidatesenteringclinicaltrials.Forinstance,Insilico’sChemistry42platformcanpredictkeyabsorption,distribution,metabolism,excretion,andtoxicity(ADMET)characteristicsofsmallmoleculesbeforelaboratorysynthesis,enablingresearcherstooptimizecompoundsandeliminateweakercandidatesatamuchearlierstageofdevelopment.

1.2.1AIRolesandDeploymentAcrossthePharmaceuticalValueChain

AIisbeingappliedacrossallstagesofthedrugR&Dlifecycle,albeitboththenatureofusecasesandthedepthofdeploymentvarysignificantlybyfunctionandmaturityofactivity.Insightsondeploymentpatternsareinformedbyourprevious

surveyof50biopharmacompanies.

•Discovery&Preclinical:AIadoptionisadvancedinearly-stageR&D,andiswidelyusedforextractingbiologicalinsightandacceleratingearlyinnovation.Bylearningpatternsfromgenomics,transcriptomics,proteomics,andphenotypicimaging,AIsupportstargetidentification,pathwayanalysis,andhypothesisgeneration.Consistentwithourpriorsurvey,moleculardesignandscreening/discovery(both70%ofresponses)andtargetdiscoveryandvalidation(67%)arethemostcommondeploymentareas,followedbydataintegration(52%).

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•Safety&Testing:PredictiveToxicology(ADMET).Solutionsherearedesignedtopredictthesafetyandpharmacokineticprofileofadruginsilico,reducingtheneedforextensiveanimaltesting.

•ClinicalDevelopment:CausalAIandTrialOptimization.AIsupportssmartertrialdesign(dose,endpoints,adaptiveprotocols),patientstratificationusingbiomarkersandreal-worlddata,andfaster,moretargetedrecruitment.However,oursurveyindicatesthatcurrentadoptionisheavilyskewedtowardadministrativeanddataworkflows,suchasdocumentgenerationandmanagement(67%)andclinicaldatamanagement(56%).Deploymentremainslimitedinpatient-facingandoperationalactivities,

incl.site

andvendormanagement(22%)andclinicaloperationsandmonitoring(22%).

•Regulatory:LLM-BasedDocumentationandSafety.AIassistswithregulatorydocumentation,safetyreporting,andregulatoryintelligencebylearningfromhistoricalapprovalsandguidelines.Surveyresultsindicatemoderateadoption(44%)inregulatoryintelligenceandstrategy.

•Manufacturing,Commercialization,andDistribution:AIdeploymentismatureinmanufacturingandsupplychain-relatedfunctions.Surveydatashowshighestadoptionindemandplanningandinventorymanagement(82%),followedbyproductionoptimization(64%).Predictivemaintenance(55%)andlogisticsanddistribution(55%)arealsowell-establishedusecases,whileprocurementandsourcingremainsacomparativelylesspenetratedarea(27%).

Chart6-AIusecasesacrossdrugR&Dcycle

Source:Jefferiesresearch

Chart8-AIuseinclinicalstudies

Source:Jefferiesresearch

Chart7-AIuseindrugdiscovery

Source:Jefferiesresearch

Chart9-AIuseindrugcommercializationanddistribution

Source:Jefferiesresearch

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1.2.2KeyLimitationsofAIinDrugR&D

Despiteitstransformativepotential,AIreplacementisconstrainedbyseveralfactorsthatrequireongoinghumaninvolvement:

1.ComplexityofBiology-Itdoesnotreplacebiology,chemistry,orclinicaljudgmentthatdrugR&Drequireforadeepunderstandingofdiseasebiologyandpathology;itrequiresexpertguidancetointerpretbiologicalcontext.

2.ClinicalUncertaintyandRisk-Itdoesnoteliminateclinicalrisk,althoughithasbeensuccessfulintargetidentificationandmoleculedesign,butitlackstheabilitytopredictcomplexclinicaltrialoutcomes.

3.DataQualityandModelReliability-ItcannotfixdataqualityissuesintrainingandhallucinationsinoutcomesasAImodelsare"onlyasgoodasthedatatheylearnfrom".Heterogeneityanderrorsinpublicdatasetsremainamajorbottleneck,oftenrequiringhumaninterventiontorefinemodelsorgenerate"groundtruth"simulateddata.

4.Noguaranteedsuccess-Itdoesnotguaranteerealbreakthroughsandsuccessasitcannotyetbypassthefundamentalbiological,technical,andclinicalcomplexitiesofdrugdevelopment.Sofar,nodrugdiscoveredentirelybyAIhasreceivedfullregulatoryapproval.

5.Hallucination-Itdoesnotguaranteefidelityingeneratedresultsandmayproduceincorrectoutputssuchasfabricatedcitations,spuriousbiologicalmechanisms,orinaccurateclinicalinferences,introducingsafetyandcompliancerisksandnegativelyaffectingthedrugdevelopmentprocess.

1.3DivergingPathsforSmallandLargeMoleculesinAIDD

1.3.1AIDD:SmallMoleculesvs.LargeMolecules

AIDDisevolvingalongtwodistinctparadigms:therelativelymaturesmall-moleculeecosystemandtherapidlyadvancingfrontierofbiologics.Insmall-moleculedrugdiscovery,AIleveragesvasthistoricaldatasetstooptimizecompounddesign,predicttargetinteractions,andaccelerateleadidentification.Incontrast,large-moleculedrugdiscoveryisbeingtransformedbynext-generationall-atomgenerativemodels,whichenablethedenovoandincreasinglyprogrammabledesignofproteins,peptides,andantibodies.Ratherthanoptimizingexistingscaffolds,thesemodelscanengineerbiologicsfromfirstprinciples,openingnewpossibilitiesforpreviouslyintractabletargets.

Chart10-ComparisonofAIDDinsmallmoleculevs.largemolecule

Source:Jefferiesresearch

1.3.2CoreObjectivesandKeyChallengesinSmall-andLarge-MoleculeAIDD

Acrossbothmodalities,thefundamentalgoalofAIistoacceleratemoleculardiscovery-fromleadoptimizationanditerativedesigntothegenerationofdenovotherapeuticcandidates.However,thescientificbottlenecksdiffersubstantiallybetweensmallandlargemolecules.

Largemolecules:Challengesinunderstandingsequence-structure-functionrelationships

Forbiologics,theprimarychallengeliesintheincompleteunderstandingoftherelationshipbetweenaminoacidsequence,three-dimensionalstructure,andbiologicalfunction.Evenasingleaminoacidsubstitutioncandramaticallyalterproteinfolding,stability,bindingaffinity,orfunctionality.Thesemutationaleffectsarehighlynon-linearanddifficulttopredict,makingit

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challengingforAl-generatedproteinstoconsistentlyexhibitbothstructuralstabilityanddesiredbiologicalactivity.

Smallmolecules:Challengesindatalimitationsandchemical-spaceexploration

Small-moleculeAlDDhasbenefitedfromdecadesofaccumulateddrug-targetdata丿particularlyforwell-studiedtargetclassessuchaskinases.Consequently丿Almodelshavedemonstratedstrongcapabilitiesingeneratingandoptimizingmoleculesfortheseestablishedtargets.However丿performanceremainsconstrainedfornoveltargetsandpreviouslyunexploredbindingpocketswhereexperimentaldataaresparse.Thechallengestemsfromthevastnessofchemicalspaceandthehighlydata-dependentnatureofcurrentAlmodels.Existingdatasetscoveronlyasmallfractionofchemicaldiversity丿limitingmodelextrapolationtonewchemotypesandtargetclasses.lnaddition丿manyAl-generatedcompoundsfailtomeetpracticalrequirementsforsyntheticaccessibilityandmanufacturability丿creatingagapbetweencomputationaldesignandexperimentalrealization.

1.3.3EmergingSolutionsandTechnologicalBreakthroughs

Smallmolecules:Expandingdataandimprovingchemical-spaceunderstanding

Forsmallmolecules丿theprincipalbottleneckremainsdataavailabilityandquality.Asaresult丿thegenerationofproprietaryexperimentaldatasetsfornoveltargetshasbecomeacriticalcompetitiveadvantage.Atthesametime丿advancesingenerativeAlandfoundationmodelsareimprovingtheindustry丨sabilitytonavigatediscretechemicalspaceanduncovernovelmoleculararchitecturesbeyondexistingdatasets.Together丿richerdatasetsandmoresophisticatedmodelsareexpectedtoacceleratetheapplicationofAlacrossthesmall-moleculediscoveryprocess.

CaseStudy:InsilicoMedicine'sPharma.AI(smallmolecule)

IntegratedPharma.AIPlatformwithClearFirstMoverAdvantages-lnsilicobuiltoneoftheindustry丨searliestandmostintegratedend-to-endAlDDplatforms丿spanningtargetidentificationthroughPCCnomination丿withthepotentialtofurtherextendintoandsupportclinicaldevelopment.Todate丿theplatformhasdelivered~30PCCs.

Scale,Data,andValidationDriveaHard-to-ReplicateMoat-Wethinklnsilicohasaccumulatedagrowingbaseofproprietarydata丿increasinglysophisticatedAlmodels丿andoperationalworkflowthattogethercreateameaningfulcompetitivemoat.Meanwhile丿Rentosertib(inChinaPh3)丿aTNlKinhibitordiscoveredthroughlnsilico丿sin-housePharma.Alplatform丿isthemostadvancedAl-enableddrugcandidategloballywithencouragingsignalsinaPh2trialvs.existingdrugsinlPF.

FromMoleculeOriginationtoAssetCreation,withLillyEndorsement-lnsilicohasevolvedbeyondatechnologyproviderintoanend-to-endassetcreationplatform丿generatingproprietarypipelineassetsandadvancingthemtowardclinicalvalidation.ThedeepeningcollaborationwithpartnerssuchasLLY-progressingfromsoftwaretoolstodiscoveryplatformsandultimatelymoleculelevelprograms-providesstrongexternalvalidationofthecapabilitiesofitsintegratedAl-drivenplatform.AsAl-drivenproductivitycontinuestoscalePCCoutput丿lnsilicolookspositionedtoexpanditspoolofproprietaryassets丿enhancingthelikelihoodoffutureout-licensingopportunitiesandstrengtheningitsLTmonetizationpotential.

AI+biotechone-stopassetengine,backedbymedicalscienceandclinicalexecutionexpertise-lnsilicointegratesAldiscovery丿experimental丿clinicaldevelopmentandAltrainedbyderiveddataintoaclosed-loopsystem丿supportedbyin-housebiotechcapabilities.Atthediscoverystage丿PandaOmics(Biology42)identifiesnoveltargetsanddiseasemechanisms丿whileChemistry42generatesandoptimizescandidatemolecules.Theseoutputsarevalidatedthroughwet-labexperiments丿ensuringthatAl-generatedhypothesesaregroundedinexperimentaldata.lnsilicoextendsitsdiscoveryengineintoclinicaldevelopmentthroughacombinationofAl-enabledtoolsandin-houseclinicaldevelopmentcapabilities-anareawherewebelieveitstandsoutversusmanyAlDDpeers.

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Chart11-InsilicogenerativeAIfordrugdiscoveryprocess

Source:Companydata

LNPs:UnlockingundruggabletargetsthroughAI-empowereddelivery

Drugdeliveryisoneofpharma'sbiggestbottlenecks—manypromisingmoleculesfailnotonefficacy,butbecausetheycan'treachtherightorganorcell.LNPshaveemergedasthebreakthroughvehicle,firstenablingmRNAandsiRNAmodalities,andnowpushingtowardprecisiondeliveryintopreviouslyinaccessibletissues(lung,muscle,brain,immuneorgans)and"undruggable"targets.Butdesigninganorgan-targetedLNPmeansoptimizingacrossavastspaceoflipidchemistry,particleproperties,biodistribution,andendosomalescape—farbeyondtrial-and-error.AIappearstobethestep-changeenabler:generatingandscreeningcandidatelipidsinsilico,predictingorgantropism,andclosingtheloopwithhigh-throughputwet-labdata—compressingyearsofformulationworkintomonthsandmakingscalable,multi-organprecisiondeliverynowmoreachievable.

CaseStudy:METiS'AiRNA+AiLNP

AI+Nanomaterials:FromMolecularDesigntoInitialClinicalValidation

METiShasbuiltadifferentiatedAI-drugdevelopmentenginespanningsmallmolecules,RNAs+LNPs,andproteins,poweredbyitsproprietaryNanoForgedry-/wet-labecosystem.Unlikepuresoftwareplatforms,METiScontinuouslyintegratesexperimentalbiologyintoAImodels,creatingaself-reinforcingadvantageindrugdesignanddevelopment.

Itsleadasset,MTS-004,servesasthefirstreal-worldproofofconcept.DevelopedusingtheAiTEMplatform,MTS-004compressedformulationtimelinesfrom1-2yrsto<3moandadvancedfromINDtoPh3completioninjust2yrs,makingittheworld'smostadvancedAI-formulateddrug.WeviewthisasthefirstclinicalvalidationofMETiS'abilitytorepeatedlygeneratedevelopabledrugcandidatesthroughAI.

CrackingOrgan-TargetedDeliverywiththeWorld'sLargestLi