【世界卫生组织WHO】2024卫生领域人工智能伦理与治理多模态大模型指南

Ethicsandgovernanceof

artificialintelligenceforhealth

Guidanceonlargemulti-modalmodels

worldHealthorganization

Ethicsandgovernanceof

artificialintelligenceforhealth

Guidanceonlargemulti-modalmodels

Ethicsandgovernanceofartificialintelligenceforhealth.Guidanceonlargemulti-modalmodels

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Contentsiii

Contents

Acknowledgements v

Abbreviations vii

Executivesummary viii

1Introduction 1

1.1SignificanceofLMMs 3

1.2WHOguidanceonethicsandgovernanceofAIforhealth 4

I.Applications,challengesandrisksofLMMs 7

2ApplicationsandchallengesofuseofLMMsinhealth 8

2.1Diagnosisandclinicalcare 8

2.2Patient-centredapplications 12

2.3Clericalfunctionsandadministrativetasks 16

2.4Medicalandnursingeducation 17

2.5Scientificandmedicalresearchanddrugdevelopment 17

3Riskstohealthsystemsandsocietyandethicalconcernsaboutuse

ofLMMs 20

3.1Healthsystems 20

3.2Compliancewithregulatoryandlegalrequirements 23

3.3Societalconcernsandrisks 24

II.EthicsandgovernanceofLMMsinhealthcareandmedicine 31

4Designanddevelopmentofgeneral-purposefoundationmodels

(LMMs) 34

4.1Riskstobeaddressedduringthedevelopmentofgeneral-

purposefoundationmodels(LMMs) 34

4.2Measuresdeveloperscantaketoaddressriskswithgeneral-

purposefoundationmodels(LMMs) 35

4.3Governmentlaws,policiesandpublicsectorinvestments 39

4.4Open-sourceLMMs 41

ivEthicsandgovernanceofartificialintelligenceforhealth.Guidanceonlargemulti-modalmodels

5Provisionwithgeneral-purposefoundationmodels(LMMs) 45

5.1Riskstobeaddressedwhenprovidingahealth-careserviceor

applicationwithageneral-purposefoundationmodel(LMM) 45

5.2Measuresthatgovernmentscanintroducetoaddresssuchrisks

andethicalprinciplesthatshouldbeupheld 46

6Deploymentwithgeneral-purposefoundationmodels(LMMs) 53

6.1Riskstobeaddressedwhendeployingahealth-careserviceor

applicationwithageneral-purposefoundationmodel(LMM) 53

6.2On-goingresponsibilitiesofdevelopersandprovidersduring

deployment 54

6.3Responsibilitiesofdeployers 54

6.4Governmentprogrammesandpractices 55

7LiabilityforLMMs 58

8InternationalgovernanceofLMMs 60

References 62

Annex.Methods 77

Acknowledgementsv

Acknowledgements

DevelopmentofthisWorldHealthOrganization(WHO)guidancewasledbyAndreasReis

(co-leadoftheHealthEthicsandGovernanceunitintheDepartmentofResearchforHealth)andSameerPujari(DepartmentofDigitalHealthandInnovation),undertheoverallguidanceofJohnReeder(Director,ResearchforHealth),AlainLabrique(Director,DigitalHealthand

Innovation)andJeremyFarrar(ChiefScientist).

RohitMalpani(consultant,France)wastheleadwriter.Theco-chairsoftheWHOexpertgrouponethicsandgovernanceofAIforhealth,EffyVayena(ETHZurich,Switzerland)andParthaMajumder(IndianStatisticalInstituteandNationalInstituteofBiomedicalGenomics,India),providedoverallguidanceondraftingofthereportandleadershipoftheexpertgroup.

WHOisgratefultothefollowingindividualswhocontributedtodevelopmentofthisguidance.

WHOexpertgrouponethicsandgovernanceofAIforhealth

NajeebAlShorbaji,eHealthDevelopmentAssociation,Amman,Jordan;MariaPazCanales,

GlobalPartnersDigital,SantiagodeChile,Chile;ArisaEma,UniversityofTokyo,Tokyo,Japan;AmelGhouila,Bill&MelindaGatesFoundation,Seattle(WA),USA;JenniferGibson,WHO

CollaboratingCentreforBioethics,UniversityofToronto,Toronto,Canada;KennethGoodman,InstituteofBioethicsandHealthPolicy,UniversityofMiamiMillerSchoolofMedicines,Miami(FL),USA;MalavikaJayaram,DigitalAsiaHub,Singapore;DaudiJjingo,MakerereUniversity,

Kampala,Uganda;TzeYunLeong,NationalUniversityofSingapore,Singapore;AlexJohn

London,CarnegieMellonUniversity,Pittsburgh(PA),USA;ParthaMajumder,IndianStatisticalInstituteandNationalInstituteofBiomedicalGenomics,Kolkata,India;ThilidziMarwala,

UniversityofJohannesburg,Johannesburg,SouthAfrica;RoliMathur,IndianCouncilof

MedicalResearch,Bangalore,India;TimoMinssen,CentreforAdvancedStudiesinBiomedicalInnovationLaw,FacultyofLaw,UniversityofCopenhagen,Copenhagen,Denmark;Andrew

Morris,HealthDataResearchUK,London,UnitedKingdom;DanielaPaolotti,ISIFoundation,Turin,Italy;JeromeSingh,UniversityofKwa-ZuluNatal,Durban,SouthAfrica;Jeroenvan

denHoven,UniversityofDelft,Delft,Netherlands(Kingdomofthe);EffyVayena,ETHZurich,Zurich,Switzerland;RobynWhittaker,UniversityofAuckland,Auckland,NewZealand;andYiZeng,ChineseAcademyofSciences,Beijing,China.

Observers

DavidGruson,Luminess,Paris,France;LeeHibbard,CouncilofEurope,Strasbourg,France

viEthicsandgovernanceofartificialintelligenceforhealth.Guidanceonlargemulti-modalmodels

Externalreviewers

OrenAsman,TelAvivUniversity,TelAviv,Israel;I.GlennCohen,HarvardLawSchool,Boston(MA),USA;AlexandrinePirlotdeCorbion,PrivacyInternational,London,UnitedKingdom;

RodrigoLins,FederalUniversityofPernambuco,Recife,Brazil;DougMcNair,DeputyDirector,IntegratedDevelopment,Bill&MelindaGatesFoundation,Seattle(WA),USA;Keymanthri

Moodley,StellenboschUniversity,CapeTown,SouthAfrica;AmirTal,TelAvivUniversity,TelAviv,Israel;TomWest,PrivacyInternational,London,UnitedKingdom.

Externalcontributors

Box2(EthicalconsiderationsfortheuseofLMMsbychildren)oftheguidancewasdraftedbyVijaythaMuralidharan,AlyssaBurgart,RoxanaDaneshjouandSherriRose,Stanford

University,Stanford(CA),USA.Box3(EthicalconsiderationsassociatedwithLMMsandtheirimpactonindividualswithdisabilities)oftheguidancewasdraftedbyYonahWelker,independentconsultant,Geneva,Switzerland.

Allexternalreviewers,expertsandcontributorsdeclaredtheirinterestsinlinewithWHOpolicies.Noneoftheinterestsdeclaredwereassessedtobesignificant.

WHO

ShadaAl-Salamah,TechnicalOfficer,DepartmentofDigitalHealthandInnovation,Geneva;

MariamOtmaniDelBarrio,Scientist,SpecialProgrammeonTropicalDiseasesResearch,

Geneva;MarceloD'Agostino,UnitChief,InformationSystemsandDigitalHealth,WHORegionalOfficefortheAmericas,Washington(DC);JeremyFarrar,ChiefScientist,Geneva;Clayton

Hamilton,TechnicalOfficer,WHORegionalOfficeforEurope,Copenhagen,Denmark;KanikaKalra,Consultant,DepartmentofDigitalHealthandInnovation,Geneva;AhmedMohamedAminMandil,Coordinator,ResearchandInnovation,WHORegionalOfficefortheEastern

Mediterranean,Cairo;IssaT.Matta,LegalAffairs,Geneva;JoseEduardoDiazMendoza,

Consultant,DepartmentofDigitalHealthandInnovation,Geneva;MohammedHassanNour,TechnicalOfficer,DepartmentofDigitalHealthandInnovation,WHORegionalOfficefor

theEasternMediterranean,Cairo;DeniseSchalet,TechnicalOfficer,DepartmentofDigitalHealthandInnovation,Geneva;YuZhao,TechnicalOfficer,DepartmentofDigitalHealthandInnovation,Geneva.

Acknowledgementsvii

Abbreviations

AIartificialintelligence

LMMlargemulti-modalmodelUSAUnitedStatesofAmerica

viiiEthicsandgovernanceofartificialintelligenceforhealth.Guidanceonlargemulti-modalmodels

Executivesummary

ArtificialIntelligence(AI)referstothecapabilityofalgorithmsintegratedintosystems

andtoolstolearnfromdatasothattheycanperformautomatedtaskswithoutexplicit

programmingofeverystepbyahuman.GenerativeAIisacategoryofAItechniquesin

whichalgorithmsaretrainedondatasetsthatcanbeusedtogeneratenewcontent,suchastext,imagesorvideo.ThisguidanceaddressesonetypeofgenerativeAI,largemulti-modal

models(LMMs),whichcanacceptoneormoretypeofdatainputandgeneratediverseoutputsthatarenotlimitedtothetypeofdatafedintothealgorithm.IthasbeenpredictedthatLMMswillhavewideuseandapplicationinhealthcare,scientificresearch,publichealthanddrug

development.LMMsarealsoknownas“general-purposefoundationmodels”,althoughitisnotyetprovenwhetherLMMscanaccomplishawiderangeoftasksandpurposes.

LMMshavebeenadoptedfasterthananyconsumerapplicationinhistory.Theyarecompellingbecausetheyfacilitatehuman–computerinteractiontomimichumancommunicationandtogenerateresponsestoqueriesordatainputsthatmayappearhuman-likeandauthoritative.Withrapidconsumeradoptionanduptakeandinviewofitspotentialtodisruptcoresocial

servicesandeconomicsectors,manylargetechnologycompanies,start-upsandgovernmentsareinvestinginandcompetingtoguidethedevelopmentofgenerativeAI.

In2021,WHOpublishedcomprehensiveguidance

(1)

ontheethicsandgovernanceofAIfor

health.WHOconsulted20leadingexpertsinAI,whoidentifiedbothpotentialbenefitsand

potentialrisksofuseofAIinhealthcareandissuedsixprinciplesarrivedatbyconsensusforconsiderationinthepoliciesandpracticesofgovernments,developers,andprovidersthat

areusingAI.TheprinciplesshouldguidethedevelopmentanddeploymentofAIinhealthcarebyawiderangeofstakeholders,includinggovernments,publicsectoragencies,researchers,companiesandimplementers.Theprinciplesare:(1)protectautonomy;(2)promotehumanwell-being,humansafetyandthepublicinterest;(3)ensuretransparency,“explainability”andintelligibility;(4)fosterresponsibilityandaccountability;(5)ensureinclusivenessandequity;and(6)promoteAIthatisresponsiveandsustainable(Figure1).

WHOisissuingthisguidancetoassistMemberStatesinmappingthebenefitsandchallenges

associatedwithuseofLMMsforhealthandindevelopingpoliciesandpracticesfor

appropriatedevelopment,provisionanduse.Theguidanceincludesrecommendationsforgovernance,withincompanies,bygovernmentsandthroughinternationalcollaboration,alignedwiththeguidingprinciples.Theprinciplesandrecommendations,whichaccountfortheuniquewaysinwhichhumanscanusegenerativeAIforhealth,arethebasisof

thisguidance.

Executivesummaryix

Figure1:WHOconsensusethicalprinciplesforuseofAIforhealth

Protectautonomy

Ensuretransparency,

explainabilityand

intelligibility

Promotehuman

well-being,humansafetyandthepublicinterest

Fosterresponsibilityandaccountability

PromoteAIthatis

responsiveand

sustainable

Ensureinclusivenessandequity

Applications,challengesandrisksoflargemulti-modalmodels

ThepotentialapplicationsofLMMsinhealthcarearesimilartothoseofotherformsofAI,yethowLMMsareaccessedandusedisnew,withbothnovelbenefitsandrisksthatsocieties,healthsystemsandend-usersmaynotyetbepreparedtoaddressfully.Table1summarizesthemainapplicationsofLMMsandtheirpotentialbenefitsandrisks.

ThesystemicrisksassociatedwithuseofLMMsincluderisksthatcouldaffecthealthsystems(Table2).

BroaderregulatoryandsystemicriskscouldemergewithuseofLMMs.Oneconcern(being

examinedbyseveraldataprotectionauthorities)iswhetherLMMscomplywithexistinglegalorregulatoryregimes,includinginternationalhumanrightsobligations,andwithnational

dataprotectionregulations.AlgorithmsmightnotcomplywithsuchlawsbecauseofthewayinwhichdataarecollectedtotrainLMMs,themanagementandprocessingofdatathathavebeencollected(orputintoLMMsbyendusers),thetransparencyandaccountabilityofentitiesthatdevelopLMMs,andthepossibilitythatLMMs“hallucinate”.LMMscouldalsobenon-

compliantwithconsumerprotectionlaws.

BroadersocietalrisksassociatedwiththegrowinguseofLMMs(includingandbeyondtheuseofsuchalgorithmsinhealthcare)includethefactthatLMMsareoftendevelopedand

deployedbylargetechnologycompanies,duepartlytothesignificantcomputing,data,

humanandfinancialresourcerequiredfordevelopmentofLMMs.Thismayreinforcethe

dominanceofthesecompaniesvis-a-vissmallerenterprisesandgovernmentswithrespecttothedevelopmentanduseofAI,includingthefocusofAIresearchinthepublicandprivatesectors.Additionalconcernsaboutthepotentialdominanceoflargetechnologycompaniesincludeinsufficientcorporatecommitmenttoethicsandtransparency.Newvoluntary

xEthicsandgovernanceofartificialintelligenceforhealth.Guidanceonlargemulti-modalmodels

Table1.PotentialbenefitsandrisksinvarioususesofLMMsinhealthcare

Use

Potentialorproposedbenefits

Potentialrisks

Diagnosisand

clinicalcare

Assistinmanagingcomplexcasesandreviewofroutinediagnoses

Reducethecommunicationworkloadofhealth-careproviders(“keyboardliberation”)

Providenovelinsightsandreportsfromvariousunstructuredformsofhealth

data

Inaccurate,incompleteorfalseresponses

Poorqualitytrainingdata

Bias(oftrainingdataandresponses)Automationbias

Degradationofskills(ofhealth-careprofessionals)

Informedconsent(ofpatients)

Patient-guideduse

Generateinformationtoimprove

understandingofamedicalcondition(asapatientorasacaregiver)

Virtualhealthassistant

Clinicaltrialenrolment

Inaccurate,incompleteorfalse

statements

ManipulationPrivacy

Lessinteractionbetweencliniciansandpatients

Epistemicinjustice

Riskofdeliveryofcareoutsidethehealthsystem

Clericaland

administrativetasks

Assistwithpaperworkand

documentationrequiredforclinicalcareAssistinlanguagetranslation

CompletionofelectronichealthrecordsDraftclinicalnotesafterapatientvisit

Inaccuraciesanderrors

Inconsistentresponsesdependingonprompts

Medical

andnursing

education

Dynamictextssuitedtoeachstudent’sneeds

Simulatedconversationtoimprovecommunicationandtopractiseindiversesituationsandwithdiversepatients

Responsestoquestionsaccompaniedbychain-of-thoughtreasoning

Contributetoautomationbias

Errorsorfalseinformationunderminethequalityofmedicaleducation

Newburdenoflearningdigitalskills

Scientific

research

anddrug

development

Generateinsightsfromscientificdataandresearch

Generatetextforuseinscientificarticles,manuscriptsubmissionorpeer-review

AnalyseandsummarizedataforresearchProofreading

Denovodrugdesign

Cannotholdalgorithmsaccountableforcontent

Algorithmsencodebiastowardsthe

perspectivesofhigh-incomecountriesGenerateinformationand/orreferencesthatdonotexist

Underminekeytenetsofscientificresearch,suchaspeerreview

Exacerbatedifferentialaccesstoscientificknowledge

Executivesummaryxi

Table2.RiskstohealthsystemsassociatedwithuseofLMMsinhealthcare

Typeofrisk

Description

OverestimationofthebenefitsofLMMs

Theremaybeatendencyto‘technologicalsolutionism’,orover-estimationofthebenefitsofLMMswhileignoringordownplayingchallengesinitsuse,includingitssafety,efficacyandutility.

Accessibilityandaffordability

EquitableaccesstoLMMsmaybelackingforseveralreasons,includingthe“digitaldivide”andsubscriptionfeestoaccessLMMs.

System-widebiases

Useofever-largerdatasetscouldincreasebiasesencodedinLMMs,whichcouldbeautomatedthroughoutahealth-caresystem.

Impactsonlabour

UseofLMMscouldleadtojoblossesinsomecountriesandrequirehealthworkerstoretrainandadjusttouseofLMMs.Dataannotationandfilteringcanleadtolowwagesandtountreatedpsychologicaldistress.

Dependenceofhealthsystemsonill-suitedLMMs

DependenceonLMMscouldmakehealthsystemsvulnerableifLMMsarenotmaintainedor(inlow-andmiddle-incomecountries)areupdatedonlyforuseinhigh-incomecountries.Furthermore,lackofpreservationandprotectionofprivacyandconfidentialitycouldunderminetrustinhealth-caresystemsbypeoplewhoarenotconfidentthattheirprivacywillbe

protected.

Cybersecurityrisks

MaliciousattacksorhackingcouldunderminesafetyandtrustintheuseofLMMsinhealthcare.

commitmentsbysuchcompanies,withoneanotherandwithgovernments,couldmitigate

severalrisksintheshort-termbutarenotanalternativetogovernmentaloversightthatmighteventuallybeenacted.

AnothersocietalriskisthecarbonandwaterfootprintsofLMMs,whichlikeotherformsofAI,requirebothsignificantenergyandcontributetoAI’sgrowingwaterfootprint.WhileLMMsandotherformsofAIcanprovideimportantsocietalbenefits,thegrowingcarbonfootprintmaybecomeamajorcontributortoclimatechange,andincreasingwaterconsumptioncanhaveafurthernegativeimpactinwater-stressedcommunities.Anothersocietalriskassociated

withtheemergenceofLMMsisthat,byprovidingplausibleresponsesthatareincreasinglyconsideredasourceofknowledge,LMMsmayeventuallyunderminehumanepistemic

authority,includinginthedomainsofhealthcare,scienceandmedicine.

EthicsandgovernanceofLMMsinhealthcareandmedicine

LMMscanbeconsideredproductsofaseries(orchain)ofdecisionsonprogrammingand

productdevelopmentbyoneormoreactors(Figure2).DecisionsmadeateachstageofanAIvaluechainmayhavebothdirectandindirectconsequencesonthosewhoparticipateinthedevelopment,deploymentanduseofLMMsdownstream.Thedecisionscanbeinfluencedandregulatedbygovernmentsbyenactingandenforcinglawsandpoliciesnationally,regionallyandglobally.

xiiEthicsandgovernanceofartificialintelligenceforhealth.Guidanceonlargemulti-modalmodels

Figure2:Valuechainofthedevelopment,provisionanddeploymentofLMMs

Provision

Providers

•Companies

•Not-for-profitentities

•Academicinstitutions

•Healthproviders

•Developers(verticallyintegrated)

Adapt/integrateLMMs

•Finetuning

•Integrateintoso什waresystems

•Organizeintoaregulatedorverifiedformat(e.g.

withplug-ins)

CF

Deployment

Deployers

•MinistriesofHealth

•Pharmaceuticalcompanies

•Healthsystems

•Hospitals

•Individualproviders

•Developers(verticallyintegrated)

•Providers(verticallyintegrated)

UseLMMs

•Diagnosisandclinicalcare

•Patientcenteredcare

•Clericaland

administrativetasks

•Medicalandnursingeducation

•Scientificresearch

•Drugdevelopment

Development

Developers

•Largetechnologycompanies

•Smalltechnologycompanies

•Universities

•Scientificcollaborations

•Public-privateconsortiums

•Nationalhealthsystems

DevelopLMMs

•General-purpose

foundationmodels(largemulti-modalmodels)

•Healthormedical

domainspecific

foundationalmodels

•Healthsystemspecificfoundationalmodels

TheAIvaluechainoftenbeginsinalargetechnologycompany,referredtoasa“developer”

inthisguidance.Thedevelopercouldalsobeauniversity,asmallertechnologycompany,

nationalhealthsystems,public-privateconsortiumsorotherentitiesthathavetheresourcesandcapacitytouseseveralinputs,whichcomprisethe“AIinfrastructure”,suchasdata,

computingpowerandAIexpertise,todevelopgeneral-purposefoundationmodels(aterm

usedbygovernmentstodescribeLMMsinlegislationandregulation).Thesemodelscanbe

useddirectlytoperformvarious,oftenunanticipatedtasks,includingthoserelatedtohealthcare.Severalgeneral-purposefoundationmodelsaretrainedspecificallyforuseinhealthcareandmedicine.

Ageneral-purposefoundationmodelcanbeusedbyathirdparty(a“provider”)throughanactiveprogramminginterfaceforaspecificpurposeoruse.Thisinvolves(i)fine-tuninganewLMM,whichmayrequireadditionaltrainingofthefoundationmodel;(ii)integratingtheLMMintoapplicationsoralargersoftwaresystemtoprovideaservicetousers;or(iii)integrating

Executivesummaryxiii

componentsknownas“plug-ins”tochannel,filterandorganizetheLMMsintoformalorregulatedformatstogenerate“digestible”results.1

Thereafter,theprovidercanmarketaproductorservicebasedontheLMMtoacustomer(or“deployer”),suchasaministryofhealth,ahealth-caresystem,ahospital,apharmaceuticalcompanyorevenanindividual,suchasahealth-careprovider.Thecustomerwhoacquiresorlicensestheproductorapplicationmaythenuseitdirectlyforpatients,health-careproviders,otherentitiesinthehealthsystem,laypeopleorinitsownbusiness.Thevaluechaincanbe“verticallyintegrated”,sothatacompany(orotherentity,suchasanationalhealthsystem)

thatcollectsdataandtrainsageneral-purposefoundationmodelcanmodifytheLMMforaparticularuseandprovidetheapplicationdirectlytousers.

Governanceisameansforenshriningethicalprinciplesandhumanrightsobligationsthroughexistinglawsandpoliciesandthroughneworrevisedlaws,norms,internalcodesofpracticeandproceduresfordevelopersandinternationalagreementsandframeworks.

OnewayofframingthegovernanceofLMMsisinthethreestagesoftheAIvaluechain:(i)

thedesignanddevelopmentofgeneral-purposefoundationmodelsorLMMs;(ii)provisionofaservice,applicationorproductbasedonageneral-purposefoundationmodel;and(iii)deploymentofahealth-careserviceorapplication.Inthisguidance,eachphaseisexaminedwithrespecttothreeareasofenquiry:

•Whatrisks(describedabove)shouldbeaddressedateachstageofthevaluechain,andwhichactorsarebestplacedtoaddressthoserisks?

•Whatcanarelevantactordotoaddresstherisks,andwhichethicalprinciplesmustbeupheld?

•Whatistheroleofgovernment,includingrelevantlaws,policiesandregulations?

CertainriskscanbeaddressedateachphaseoftheAIvaluechain,andcertainactorsarelikelytoplaymoreimportantrolesinmitigatingeachriskandupholdingethicalvalues.Whilethereislikelytobedisagreementandtensionaboutwhereresponsibilityrestsbetweendevelopers,providersanddeployers,thereareclearareasinwhicheachactorisbestplacedoristheonlyentitywiththecapacitytoaddressapotentialoractualrisk.

Designanddevelopmentofgeneral-purposefoundationmodels(LMMs)

Duringthedesignanddevelopmentofgeneral-purposefoundationmodels,theresponsibilityrestswiththedevelopers.Governmentsbeartheresponsibilitytosetlawsandstandardstorequireorforbidcertainpractices.Section4ofthisguidanceprovidesrecommendationstohelpaddressrisksandmaximizebenefitsduringthedevelopmentofLMMs.

1CommunicationfromLeongTze-Yun,WHOexpertontheethicsandgovernanceofAIforhealth.

xivEthicsandgovernanceofartificialintelligenceforhealth.Guidanceonlargemulti-modalmodels

Provisionwithgeneral-purposefoundationmodels(LMMs)

Duringprovisionofaservic