美國 FDA 人工智能和機器學(xué)習(xí)討論論文

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ProposedRegulatoryFrameworkforModificationstoArtificialIntelligence/MachineLearning(AI/ML)-BasedSoftwareasaMedicalDevice(SaMD)-DiscussionPaperandRequestforFeedback

Introduction

Artificialintelligence(AI)-andmachinelearning(ML)-basedtechnologieshavethepotentialtotransformhealthcarebyderivingnewandimportantinsightsfromthevastamountofdatageneratedduringthedeliveryofhealthcareeveryday.Examplehigh-valueapplicationsincludeearlierdiseasedetection,moreaccuratediagnosis,identificationofnewobservationsorpatternsonhumanphysiology,anddevelopmentofpersonalizeddiagnosticsandtherapeutics.OneofthegreatestbenefitsofAI/MLinsoftwareresidesinitsabilitytolearnfromreal-worlduseandexperience,anditscapabilitytoimproveitsperformance.TheabilityforAI/MLsoftwaretolearnfromreal-worldfeedback(training)andimproveitsperformance(adaptation)makesthesetechnologiesuniquelysituatedamongsoftwareasamedicaldevice(SaMD)

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andarapidlyexpandingareaofresearchanddevelopment.Ourvisionisthatwithappropriatelytailoredregulatoryoversight,AI/ML-basedSaMDwilldeliversafeandeffectivesoftwarefunctionalitythatimprovesthequalityofcarethatpatientsreceive.

TheInternationalMedicalDeviceRegulatorsForum(IMDRF)defines‘SoftwareasaMedicalDevice(SaMD)’assoftwareintendedtobeusedforoneormoremedicalpurposesthatperformthesepurposeswithoutbeingpartofahardwaremedicaldevice.1FDA,undertheFederalFood,Drug,andCosmeticAct(FD&CAct)considersmedicalpurposeasthosepurposesthatareintendedtotreat,diagnose,cure,mitigate,orpreventdiseaseorotherconditions.

FDAhasmadesignificantstridesindevelopingpolicies

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,

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thatareappropriatelytailoredforSaMDtoensurethatsafeandeffectivetechnologyreachesusers,includingpatientsandhealthcareprofessionals.ManufacturerssubmitamarketingapplicationtoFDApriortoinitialdistributionoftheirmedicaldevice,withthesubmissiontypeanddatarequirementsbasedontheriskoftheSaMD(510(k)notification,DeNovo,orpremarketapprovalapplication(PMA)pathway).Forchangesindesignthatarespecifictosoftwarethathasbeenreviewedandclearedundera510(k)notification,FDA’sCenterforDevicesandRadiologicalHealth(CDRH)haspublishedguidance(

DecidingWhentoSubmita510(k)

foraSoftwareChangetoanExistingDevice,

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alsoreferredtohereinasthesoftwaremodificationsguidance)thatdescribesarisk-basedapproachtoassistindeterminingwhenapremarketsubmissionisrequired.

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1SoftwareasaMedicalDevice(SaMD):KeyDefinitions:

/docs/imdrf/final/technical/imdrf-tech-131209-samd-key-

definitions-140901.pdf.

2Pre-CertProgramVersion1.0WorkingModel:

/downloads/MedicalDevices/DigitalHealth/DigitalHealthPreCertProgram/UCM629276.pdf.

3SoftwareasaMedicalDevice(SaMD):ClinicalEvaluation:

/downloads/medicaldevices/deviceregulationandguidance/guidancedocuments/ucm524904.pdf.

4DecidingWhentoSubmita510(k)foraSoftwareChangetoanExistingDevice:

/downloads/medicaldevices/deviceregulationandguidance/guidancedocuments/ucm514737.pdf.

521CFR807.81(a)(3).ModificationstoadeviceapprovedthroughaPMAaregovernedbythecriteriain21CFR814.39(a).ModificationstoDevicesSubjecttoPremarketApproval(PMA)-ThePMASupplementDecision-MakingProcess:

/downloads/MedicalDevices/DeviceRegulationandGuidance/GuidanceDocuments/UCM089360.pdf.

The510(k)softwaremodificationsguidancefocusesontherisktousers/patientsresultingfromthesoftwarechange.Categoriesofsoftwaremodificationsthatmayrequireapremarketsubmissioninclude:

Achangethatintroducesanewriskormodifiesanexistingriskthatcouldresultinsignificantharm;

Achangetoriskcontrolstopreventsignificantharm;and

Achangethatsignificantlyaffectsclinicalfunctionalityorperformancespecificationsofthedevice.

WhenappliedtoAI/ML-basedSaMD,theaboveapproachwouldrequireapremarketsubmissiontotheFDAwhentheAI/MLsoftwaremodificationsignificantlyaffectsdeviceperformance,orsafetyandeffectiveness

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;themodificationistothedevice’sintendeduse;orthemodificationintroducesamajorchangetotheSaMDalgorithm.ForaPMA-approvedSaMD,asupplementalapplicationwouldberequiredforchangesthataffectsafetyoreffectiveness,suchasnewindicationsforuse,newclinicaleffects,orsignificanttechnologymodificationsthataffectperformancecharacteristics.

ToaddressthecriticalquestionofwhenacontinuouslylearningAI/MLSaMDmayrequireapremarketsubmissionforanalgorithmchange,wewerepromptedtoreimagineanapproachtopremarketreviewforAI/ML-drivensoftwaremodifications.SuchanapproachwouldneedtomaintainreasonableassuranceofsafetyandeffectivenessofAI/ML-basedSaMD,whileallowingthesoftwaretocontinuetolearnandevolveovertimetoimprovepatientcare.

Todate,FDAhasclearedorapprovedseveralAI/ML-basedSaMD.Typically,thesehaveonlyincludedalgorithmsthatare“l(fā)ocked

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”priortomarketing,wherealgorithmchangeslikelyrequireFDApremarketreviewforchangesbeyondtheoriginalmarketauthorization.However,notallAI/ML-basedSaMDarelocked;somealgorithmscanadaptovertime.ThepoweroftheseAI/ML-basedSaMDlieswithintheabilitytocontinuouslylearn,wheretheadaptationorchangetothealgorithmisrealizedaftertheSaMDisdistributedforuseandhas“l(fā)earned”fromreal-worldexperience.Followingdistribution,thesetypesofcontinuouslylearningandadaptiveAI/MLalgorithmsmayprovideadifferentoutputincomparisontotheoutputinitiallyclearedforagivensetofinputs.

ThetraditionalparadigmofmedicaldeviceregulationwasnotdesignedforadaptiveAI/MLtechnologies,whichhavethepotentialtoadaptandoptimizedeviceperformanceinreal-timetocontinuouslyimprovehealthcareforpatients.Thehighlyiterative,autonomous,andadaptivenatureofthesetoolsrequiresanew,totalproductlifecycle(TPLC)regulatoryapproachthatfacilitatesarapidcycleofproductimprovementandallowsthesedevicestocontinuallyimprovewhileprovidingeffectivesafeguards.

ThisdiscussionpaperproposesaframeworkformodificationstoAI/ML-basedSaMDthatisbasedontheinternationallyharmonizedInternationalMedicalDeviceRegulatorsForum(IMDRF)riskcategorizationprinciples,FDA’sbenefit-riskframework,riskmanagementprinciplesinthesoftware

621CFR807.81(a)(3).

7Wedefinea“l(fā)ocked”algorithmasanalgorithmthatprovidesthesameresulteachtimethesameinputisappliedtoitanddoesnotchangewithuse.Examplesoflockedalgorithmsarestaticlook-uptables,decisiontrees,andcomplexclassifiers.

modificationsguidance

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,andtheorganization-basedTPLCapproachasenvisionedintheDigitalHealthSoftwarePrecertification(Pre-Cert)Program.

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Italsoleveragespracticesfromourcurrentpremarketprograms,includingthe510(k),DeNovo,andPMApathways.

Thisdiscussionpaperdescribesaninnovativeapproachthatmayrequireadditionalstatutoryauthoritytoimplementfully.Theproposedframeworkisbeingissuedfordiscussionpurposesonlyandisnotadraftguidance.ThisdocumentisnotintendedtocommunicateFDA'sproposed(orfinal)regulatoryexpectationsbutisinsteadmeanttoseekearlyinputfromgroupsandindividualsoutsidetheAgencypriortodevelopmentofadraftguidance.

ThisproposedTPLCapproachallowsFDA’sregulatoryoversighttoembracetheiterativeimprovementpowerofAI/MLSaMDwhileassuringthatpatientsafetyismaintained.Italsoassuresthatongoingalgorithmchangesareimplementedaccordingtopre-specifiedperformanceobjectives,followdefinedalgorithmchangeprotocols,utilizeavalidationprocessthatiscommittedtoimprovingtheperformance,safety,andeffectivenessofAI/MLsoftware,andincludereal-worldmonitoringofperformance.ThisproposedTPLCregulatoryframeworkaimstopromoteamechanismformanufacturerstobecontinuallyvigilantinmaintainingthesafetyandeffectivenessoftheirSaMD,thatultimately,supportsbothFDAandmanufacturersinprovidingincreasedbenefitstopatientsandproviders.

Background:AI/ML-BasedSoftwareasaMedicalDevice

Non-devicesoftwarefunctionsarenotsubjecttoFDAdeviceregulationandarenotwithinthescopeofthispaper.Inaddition,asdetailedinsection502(o)oftheFD&CAct,softwarefunctionsintended(1)foradministrativesupportofahealthcarefacility,(2)formaintainingorencouragingahealthylifestyle,(3)toserveaselectronicpatientrecords,(4)fortransferring,storing,convertingformats,ordisplayingdata,or(5)toprovidecertain,limitedclinicaldecisionsupportarenotmedicaldevicesandarenotsubjecttoFDAregulation.

Inthispaper,weuseJohnMcCarthy’sdefinitionofAIasthescienceandengineeringofmakingintelligentmachines,especiallyintelligentcomputerprograms.

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AIcanusedifferenttechniques,suchasML,toproduceintelligentbehavior,includingmodelsbasedonstatisticalanalysisofdata,andexpertsystemsthatprimarilyrelyonif-thenstatements.Inthispaper,werefertoanMLsystemasasystemthathasthecapacitytolearnbasedontraining

onaspecifictaskbytrackingperformancemeasure(s).AI,andspecificallyML,aretechniquesusedtodesignandtrainsoftwarealgorithmstolearnfromandactondata.TheseAI/ML-basedsoftware,whenintendedtotreat,diagnose,cure,mitigate,orpreventdiseaseorotherconditions,aremedicaldevicesundertheFD&CAct,andcalled“SoftwareasaMedicalDevice”(SaMD)byFDAandIMDRF.TheintendeduseofAI/ML-basedSaMD,similartootherSaMDs,mayexistonaspectrumofimpacttopatientsascategorizedbyIMDRFSaMDriskcategorizationframework.

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8DecidingWhentoSubmita510(k)foraSoftwareChangetoanExistingDevice:

/downloads/medicaldevices/deviceregulationandguidance/guidancedocuments/ucm514737.pdf.

9DevelopingaSoftwarePrecertificationProgram:AWorkingModel;v1.0–January2019:

/downloads/MedicalDevices/DigitalHealth/DigitalHealthPreCertProgram/UCM629276.pdf.

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/articles/whatisai/whatisai.pdf.

11SoftwareasaMedicalDevice(SaMD):PossibleFrameworkforRiskCategorizationandCorrespondingConsiderations:

/docs/imdrf/final/technical/imdrf-tech-140918-samd-framework-risk-categorization-141013.pdf.

TheIMDRFSaMDriskcategorizationframeworktakesarisk-basedapproachtocategorizeSaMDbasedonintendeduse,similartotraditionalrisk-basedapproachesusedbytheFDA.TheIMDRFriskframeworkidentifiesthefollowingtwomajorfactorsasprovidingadescriptionoftheintendeduse

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oftheSaMD:

SignificanceofinformationprovidedbytheSaMDtothehealthcaredecision,whichidentifiestheintendeduseoftheinformationprovidedbytheSaMD–i.e.,totreatordiagnose;todriveclinicalmanagement;ortoinformclinicalmanagement;and

Stateofhealthcaresituationorcondition,whichidentifiestheintendeduser,diseaseorcondition,andthepopulationfortheSaMD–i.e.,critical;serious;ornon-serioushealthcaresituationsorconditions.

Takentogether,thesefactorsdescribingtheintendedusecanbeusedtoplacetheAI/ML-basedSaMDintooneoffourcategories,fromlowest(I)tohighestrisk(IV)toreflecttheriskassociatedwiththeclinicalsituationanddeviceuse.

Stateofhealthcaresituationor

condition

SignificanceofinformationprovidedbySaMDtohealthcare

decision

Treatordiagnose

Driveclinical

management

Informclinical

management

Critical

IV

III

II

Serious

III

II

I

Non-serious

II

I

I

Figure1:SaMDIMDRFriskcategorization

WhileAI/ML-basedSaMDexistonaspectrumcategorizedbyrisktopatients,theyalsoexistonaspectrumfromlockedtocontinuouslylearning.“Locked”algorithmsarethosethatprovidethesameresulteachtimethesameinputisprovided.Assuch,alockedalgorithmappliesafixedfunction(e.g.,astaticlook-uptable,decisiontree,orcomplexclassifier)toagivensetofinputs.Thesealgorithmsmayusemanualprocessesforupdatesandvalidation.Incontrasttoalockedalgorithm,anadaptivealgorithm(e.g.,acontinuouslearningalgorithm)changesitsbehaviorusingadefinedlearningprocess.Thealgorithmadaptationorchangesareimplementedsuchthatforagivensetofinputs,theoutputmaybedifferentbeforeandafterthechangesareimplemented.Thesealgorithmchangesaretypicallyimplementedandvalidatedthroughawell-definedandpossiblyfullyautomatedprocessthataimsatimprovingperformancebasedonanalysisofneworadditionaldata.

Theadaptationprocesscanbeintendedtoaddressseveraldifferentclinicalaspects,suchasoptimizingperformancewithinaspecificenvironment(e.g.,basedonthelocalpatientpopulation),optimizingperformancebasedonhowthedeviceisbeingused(e.g.,basedonpreferencesofaspecificphysician),improvingperformanceasmoredataarecollected,and/orchangingtheintendeduseofthedevice.Theadaptationprocessfollowstwostages:learningandupdating.Thealgorithm“l(fā)earns”howtochangeitsbehavior,forexample,fromtheadditionofnewinputtypesoraddingnewcasestoanalreadyexistingtrainingdatabase.The“update”thenoccurswhenthenewversionofthealgorithmisdeployed.Asa

12InformationthatmaybeusedtodescribeintendeduseforFDApurposesissetforthin21CFR807.92(a)(5),814.20(b)(3),and860.7(b),andcouldbewrittenusingterminologyasdescribedintheIMDRFriskcategorizationframework.

result,giventhesamesetofinputsattimeA(beforeupdate)andtimeB(afterupdate),theoutputofthealgorithmmaydiffer.

AlthoughAI/ML-basedSaMDexistsonaspectrumfromlockedtocontinuouslyadaptivealgorithms,acommonsetofconsiderationsfordatamanagement,re-training,andperformanceevaluationcanbeappliedtotheentirespectrumofSaMD.Forexample,therigorofperformanceevaluationforbothlockedandcontinuouslyadaptivealgorithmsdependonthetestmethods,qualityandapplicabilityofdatasetusedfortesting,andthealgorithm'strainingmethods.Robustalgorithmstypicallyrequiretheavailabilityoflarge,high-quality,andwell-labeledtrainingdatasets.Likewise,acommonsetofprinciplescanbeappliedtoconsiderationsabouthowtoprovideconfidenceinfunctionandperformancetousersthroughappropriatevalidation,transparency,andclaimsafterthemodification.

TypesofAI/ML-basedSaMDModifications

TherearemanypossiblemodificationstoanAI/ML-basedSaMD.Somemodificationsmaynotrequireareviewbasedonguidanceprovidedin“DecidingWhentoSubmita510(k)foraSoftwareChangetoanExistingDevice.”

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ThispaperanticipatesthatmanymodificationstoAI/ML-basedSaMDinvolvealgorithmarchitecturemodificationsandre-trainingwithnewdatasets,whichunderthesoftwaremodificationsguidancewouldbesubjecttopremarketreview.Thetypesofmodificationsgenerallyfallintothreebroadcategories:

Performance–clinicalandanalyticalperformance

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;

InputsusedbythealgorithmandtheirclinicalassociationtotheSaMDoutput;and/or

Intendeduse

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–TheintendeduseoftheSaMD,asoutlinedaboveandintheIMDRFriskcategorizationframework,describedthroughthesignificanceofinformationprovidedbytheSaMDforthestateofthehealthcaresituationorcondition.

Thechangesdescribedmaynotbemutuallyexclusive–onesoftwaremodificationmayimpact,forexample,bothachangeininputandchangeinperformance;or,aperformancechangemayincreaseadevice’sclinicalperformancethatinturnimpactstheintendeduse.ThesesoftwarechangesinAI/ML-basedSaMD,groupedbythetypesofchangesasdescribedabove,havedifferentimpactonusers,whichmayincludeeitherpatients,healthcareprofessionals,orothers:

Modificationsrelatedtoperformance,withnochangetotheintendeduseornewinputtype:Thistypeofmodificationincludesimprovementstoanalyticalandclinicalperformancethatcanresultfromanumberofchanges.Thismayincludere-trainingwithnewdatasetswithintheintendedusepopulationfromthesametypeofinputsignal,achangeintheAI/MLarchitecture,orothermeans.Forthistypeofmodification,themanufacturercommonlyaimstoupdateusersontheperformance,withoutchanginganyoftheexplicituseclaimsabouttheirproduct(e.g.,increasedsensitivityoftheSaMDatdetectingbreastlesionssuspiciousforcancerindigitalmammograms).

13DecidingWhentoSubmita510(k)foraSoftwareChangetoanExistingDevice:

/downloads/medicaldevices/deviceregulationandguidance/guidancedocuments/ucm514737.pdf.

14SoftwareasaMedicalDevice(SaMD):ClinicalEvaluation:

/downloads/medicaldevices/deviceregulationandguidance/guidancedocuments/ucm524904.pdf.

15Inthisdocument,“modificationsrelatedtointendeduse”referstochangeswithintheparametersofthecleared/approvedintendeduseasdefinedintheclassificationregulationorFDAapprovalorauthorization.

Modificationsrelatedtoinputs,withnochangetotheintendeduse:ThesetypesofmodificationsarethosethatchangetheinputsusedbytheAI/MLalgorithm.Thesemodificationsmayinvolvechangestothealgorithmforusewithnewtypesofinputsignals,butdonotchangetheproductuseclaims.Examplesofthesechangescouldbe:

ExpandingtheSaMD’scompatibilitywithothersource(s)ofthesameinputdatatype(e.g.,SaMDmodificationtosupportcompatibilitywithCTscannersfromadditionalmanufacturers);or

Addingdifferentinputdatatype(s)(e.g.,expandingtheinputsforaSaMDthatdiagnosesatrialfibrillationtoincludeoximetrydata,forexample,inadditiontoheartratedata).

ModificationsrelatedtotheSaMD’sintendeduse:ThesetypesofmodificationsincludethosethatresultinachangeinthesignificanceofinformationprovidedbytheSaMD(e.g.,fromaconfidencescorethatis‘a(chǎn)naidindiagnosis’(driveclinicalmanagement)toa‘definitivediagnosis’(diagnose)).Thesetypesofmodificationsalsoincludethose

thatresultinachangeinthestateofthehealthcaresituationorconditionandareexplicitlyclaimedbythemanufacturer,suchasanexpandedintendedpatientpopulation(e.g.,inclusionofpediatricpopulationwheretheSaMDwasinitiallyintendedforadultsages18yearsorolder);ortheintendeddiseaseorcondition(e.g.,expansiontouseaSaMDalgorithmforlesiondetectionfromonetypeofcancertoanother).ChangesrelatedtoeitherthesignificanceoftheinformationprovidedbytheSaMDorthehealthcaresituationorconditionmaybelimitedinscopebythepre-specifiedperformanceobjectivesandalgorithmchangeprotocols.

Questions/FeedbackonthetypesofAI/ML-SaMDmodifications:

DothesecategoriesofAI/ML-SaMDmodificationsalignwiththemodificationsthatwouldtypicallybeencounteredinsoftwaredevelopmentthatcouldrequirepremarketsubmission?

Whatadditionalcategories,ifany,ofAI/ML-SaMDmodificationsshouldbeconsideredinthisproposedapproach?

WouldtheproposedframeworkforaddressingmodificationsandmodificationtypesassistthedevelopmentAI/MLsoftware?

ATotalProductLifecycleRegulatoryApproachforAI/ML-BasedSaMD

AsenvisionedintheSoftwarePre-CertProgram,

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applyingaTPLCapproachtotheregulationofsoftwareproductsisparticularlyimportantforAI/ML-basedSaMDduetoitsabilitytoadaptandimprovefromreal-worlduse.InthePre-CertTPLCapproach,FDAwillassessthecultureofqualityandorganizationalexcellenceofaparticularcompanyandhavereasonableassuranceofthehighqualityoftheirsoftwaredevelopment,testing,andperformancemonitoringoftheirproducts.Thisapproach

16DevelopingaSoftwarePrecertificationProgram:AWorkingModel;v1.0–January2019:

/downloads/MedicalDevices/DigitalHealth/DigitalHealthPreCertProgram/UCM629276.pdf.

ThisproposedregulatoryapproachwouldapplytoonlythoseAI/MLbased-SaMDthatrequirepremarketsubmissionandnotthosethatareexemptfromrequiringpremarketreview(i.e.,ClassIexemptandClassIIexempt).

wouldprovidereasonableassuranceofsafetyandeffectivenessthroughoutthelifecycleoftheorganizationandproductssothatpatients,caregivers,healthcareprofessionals,andotherusershaveassuranceofthesafetyandqualityofthoseproducts.ThisTPLCapproachenablestheevaluationandmonitoringofasoftwareproductfromitspremarketdevelopmenttopostmarketperformance,alongwithcontinueddemonstrationoftheorganization’sexcellence(Figure2).

Figure2:OverlayofFDA'sTPLCapproachonAI/MLworkflow

TofullyrealizethepowerofAI/MLlearningalgorithmswhileenablingcontinuousimprovementoftheirperformanceandlimitingdegradations,theFDA’sproposedTPLCapproachisbasedonthefollowinggeneralprinciplesthatbalancethebenefitsandrisks,andprovideaccesstosafeandeffectiveAI/ML-basedSaMD:

EstablishclearexpectationsonqualitysystemsandgoodMLpractices(GMLP);

ConductpremarketreviewforthoseSaMDthatrequirepremarketsubmission

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todemonstratereasonableassuranceofsafetyandeffectivenessandestablishclearexpectationsformanufacturersofAI/ML-basedSaMDtocontinuallymanagepatientrisksthroughoutthelifecycle;

ExpectmanufacturerstomonitortheAI/MLdeviceandincorporateariskmanagementapproachandotherapproachesoutlinedin“DecidingWhentoSubmita510(k)foraSoftwareChangetoanExistingDevice”Guidance

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indevelopment,validation,andexecutionofthealgorithmchanges(SaMDPre-SpecificationsandAlgorithmChangeProtocol);and

EnableincreasedtransparencytousersandFDAusingpostmarketreal-worldperformancereportingformaintainingcontinuedassuranceofsafetyandeffectiveness.

QualitySystemsandGoodMachineLearningPractices(GMLP):

TheFDAexpectseverymedicaldevicemanufacturertohaveanestablishedqualitysystemthatisgearedtowardsdeveloping,delivering,andmaintaininghigh-qualityproductsthroughoutthelifecyclethatconformstotheappropriatestandardsandregulations.

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Similarly,forAI/ML-basedSaMD,weexpectthatSaMDdevelopersembracetheexcellenceprinciplesofcultureofqualityandorganizationalexcellence.

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AsisthecaseforallSaMD,devicesthatrelyonAI/MLareexpectedtodemonstrateanalyticalandclinicalvalidation,asdescribedintheSaMD:ClinicalEvaluationguidance(Figure3).

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Thespecifictypesofdatanecessarytoassuresafetyandeffectivenessduringthepremarketreview,includingstudydesign,willdependonthefunctionoftheAI/ML,theriskitposestousers,anditsintendeduse.

Figure3:IMDRFdescriptionofClinicalEvaluationcomponents

AI/MLalgorithmdevelopmentinvolveslearningfromdataandhencepromptsuniqueconsiderationsthatembodyGMLP.Inthispaper,GMLParethoseAI/MLbestpractices(e.g.,datamanagement,featureextraction,training,andevaluation)thatareakintogoodsoftwareengineeringpracticesorqualitysystempractices.ExamplesofGMLPconsiderationsasappliedforSaMDinclude:

1721CFRPart807SubpartEor21CFRPart814SubpartB.

18DecidingWhentoSubmita510(k)foraSoftwareChangetoanExistingDevice:

/downloads/medicaldevices/deviceregulationandguidance/guidancedocuments/ucm514737.pdf.

1921CFRPart820.

20SeethediscussioninDevelopingaSoftwarePrecertificationProgram:AWorkingModel;v1.0–January2019:

/downloads/MedicalDevices/DigitalHealth/DigitalHealthPreCertProgram/UCM629276.pdf.

21SoftwareasaMedicalDevice(SaMD):ClinicalEvaluation:

/downloads/medicaldevices/deviceregulationandguidance/guidancedocuments/ucm524904.pdf.

Relevanceofavailabledatatotheclinicalproblemandcurrentclinicalpractice;

Dataacquiredinaconsistent,clinicallyrelevantandgeneralizablemannerthatalignswiththeSaMD’sintendeduseandmodificationplans;

Appropriateseparationbetweentraining,tuning,andtestdatasets;and

Appropriateleveloftransparency(clarity)oftheoutputandthealgorithmaimedatusers.

Questions/FeedbackonGMLP:

WhatadditionalconsiderationsexistforGMLP?

HowcanFDAsupportdevelopmentofGMLP?

HowdomanufacturersandsoftwaredevelopersincorporateGMLPintheirorganization?

InitialPremarketAssuranceofSafetyandEffectiveness:

ThisframeworkgivesmanufacturerstheoptiontosubmitaplanformodificationsduringtheinitialpremarketreviewofanAI/ML-basedSaMD.FDA’spremarketreviewanddeterminationregardingtheacceptabilityofsuchplanswouldprovidereasonableassuranceofsafetyandeffectivenessandwouldincludereviewoftheSaMD’sperformance,themanufacturer’splanformodifications,andtheabilityofthemanufacturertomanageandcontrolresultantrisksofthemodifications.FDAhassuccessfullyexploredthisvoluntaryapproachtoreviewdevicemodificationplansincertainrecentDeNovoclassificationsregardingseveralin-vitrodiagnosticnextgenerationsequencingproducts.

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ThispaperproposesaframeworkformodificationstoAI/ML-basedSaMDthatreliesontheprincipleofa“predeterminedchangecontrolplan.”Usingthisproposedregulatoryapproach,webelievethatouroversightwillenableresponsibleperformanceenhancementsinAI/ML-basedtechnologies.

Thepredeterminedchangecontrolplanwouldincludethetypesofanticipatedmodifications–SaMDPre-Specifications–basedontheretrainingandmodelupdatestrategy,andtheassociatedmethodology–AlgorithmChangeProtocol–beingusedtoimplementthosechangesinacontrolledmannerthatmanagesriskstopatients.

SaMDPre-Specifications(SPS):ASaMDmanufacturer’santicipatedmodificationsto“performance”or“inputs,”orchangesrelatedtothe“intendeduse”ofAI/ML-basedSaMD.ThesearethetypesofchangesthemanufacturerplanstoachievewhentheSaMDisinuse.TheSPSdrawsa“regionofpotentialchanges”aroundtheinitialspecificationsandlabelingoftheoriginaldevice.Thisis"what"themanufacturerintendsthealgorithmtobecomeasitlearns.

AlgorithmChangeProtocol(ACP):SpecificmethodsthatamanufacturerhasinplacetoachieveandappropriatelycontroltherisksoftheanticipatedtypesofmodificationsdelineatedintheSPS.TheACPisa