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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)
1
andarapidlyexpandingareaofresearchanddevelopment.Ourvisionisthatwithappropriatelytailoredregulatoryoversight,AI/ML-basedSaMDwilldeliversafeandeffectivesoftwarefunctionalitythatimprovesthequalityofcarethatpatientsreceive.
TheInternationalMedicalDeviceRegulatorsForum(IMDRF)defines‘SoftwareasaMedicalDevice(SaMD)’assoftwareintendedtobeusedforoneormoremedicalpurposesthatperformthesepurposeswithoutbeingpartofahardwaremedicaldevice.1FDA,undertheFederalFood,Drug,andCosmeticAct(FD&CAct)considersmedicalpurposeasthosepurposesthatareintendedtotreat,diagnose,cure,mitigate,orpreventdiseaseorotherconditions.
FDAhasmadesignificantstridesindevelopingpolicies
2
,
3
thatareappropriatelytailoredforSaMDtoensurethatsafeandeffectivetechnologyreachesusers,includingpatientsandhealthcareprofessionals.ManufacturerssubmitamarketingapplicationtoFDApriortoinitialdistributionoftheirmedicaldevice,withthesubmissiontypeanddatarequirementsbasedontheriskoftheSaMD(510(k)notification,DeNovo,orpremarketapprovalapplication(PMA)pathway).Forchangesindesignthatarespecifictosoftwarethathasbeenreviewedandclearedundera510(k)notification,FDA’sCenterforDevicesandRadiologicalHealth(CDRH)haspublishedguidance(
DecidingWhentoSubmita510(k)
foraSoftwareChangetoanExistingDevice,
4
alsoreferredtohereinasthesoftwaremodificationsguidance)thatdescribesarisk-basedapproachtoassistindeterminingwhenapremarketsubmissionisrequired.
5
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
6
;themodificationistothedevice’sintendeduse;orthemodificationintroducesamajorchangetotheSaMDalgorithm.ForaPMA-approvedSaMD,asupplementalapplicationwouldberequiredforchangesthataffectsafetyoreffectiveness,suchasnewindicationsforuse,newclinicaleffects,orsignificanttechnologymodificationsthataffectperformancecharacteristics.
ToaddressthecriticalquestionofwhenacontinuouslylearningAI/MLSaMDmayrequireapremarketsubmissionforanalgorithmchange,wewerepromptedtoreimagineanapproachtopremarketreviewforAI/ML-drivensoftwaremodifications.SuchanapproachwouldneedtomaintainreasonableassuranceofsafetyandeffectivenessofAI/ML-basedSaMD,whileallowingthesoftwaretocontinuetolearnandevolveovertimetoimprovepatientcare.
Todate,FDAhasclearedorapprovedseveralAI/ML-basedSaMD.Typically,thesehaveonlyincludedalgorithmsthatare“locked
7
”priortomarketing,wherealgorithmchangeslikelyrequireFDApremarketreviewforchangesbeyondtheoriginalmarketauthorization.However,notallAI/ML-basedSaMDarelocked;somealgorithmscanadaptovertime.ThepoweroftheseAI/ML-basedSaMDlieswithintheabilitytocontinuouslylearn,wheretheadaptationorchangetothealgorithmisrealizedaftertheSaMDisdistributedforuseandhas“learned”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“locked”algorithmasanalgorithmthatprovidesthesameresulteachtimethesameinputisappliedtoitanddoesnotchangewithuse.Examplesoflockedalgorithmsarestaticlook-uptables,decisiontrees,andcomplexclassifiers.
modificationsguidance
8
,andtheorganization-basedTPLCapproachasenvisionedintheDigitalHealthSoftwarePrecertification(Pre-Cert)Program.
9
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.
10
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.
11
8DecidingWhentoSubmita510(k)foraSoftwareChangetoanExistingDevice:
/downloads/medicaldevices/deviceregulationandguidance/guidancedocuments/ucm514737.pdf.
9DevelopingaSoftwarePrecertificationProgram:AWorkingModel;v1.0–January2019:
/downloads/MedicalDevices/DigitalHealth/DigitalHealthPreCertProgram/UCM629276.pdf.
10
/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
12
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“learns”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.”
13
ThispaperanticipatesthatmanymodificationstoAI/ML-basedSaMDinvolvealgorithmarchitecturemodificationsandre-trainingwithnewdatasets,whichunderthesoftwaremodificationsguidancewouldbesubjecttopremarketreview.Thetypesofmodificationsgenerallyfallintothreebroadcategories:
Performance–clinicalandanalyticalperformance
14
;
InputsusedbythealgorithmandtheirclinicalassociationtotheSaMDoutput;and/or
Intendeduse
15
–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‘anaidindiagnosis’(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,
16
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
17
todemonstratereasonableassuranceofsafetyandeffectivenessandestablishclearexpectationsformanufacturersofAI/ML-basedSaMDtocontinuallymanagepatientrisksthroughoutthelifecycle;
ExpectmanufacturerstomonitortheAI/MLdeviceandincorporateariskmanagementapproachandotherapproachesoutlinedin“DecidingWhentoSubmita510(k)foraSoftwareChangetoanExistingDevice”Guidance
18
indevelopment,validation,andexecutionofthealgorithmchanges(SaMDPre-SpecificationsandAlgorithmChangeProtocol);and
EnableincreasedtransparencytousersandFDAusingpostmarketreal-worldperformancereportingformaintainingcontinuedassuranceofsafetyandeffectiveness.
QualitySystemsandGoodMachineLearningPractices(GMLP):
TheFDAexpectseverymedicaldevicemanufacturertohaveanestablishedqualitysystemthatisgearedtowardsdeveloping,delivering,andmaintaininghigh-qualityproductsthroughoutthelifecyclethatconformstotheappropriatestandardsandregulations.
19
Similarly,forAI/ML-basedSaMD,weexpectthatSaMDdevelopersembracetheexcellenceprinciplesofcultureofqualityandorganizationalexcellence.
20
AsisthecaseforallSaMD,devicesthatrelyonAI/MLareexpectedtodemonstrateanalyticalandclinicalvalidation,asdescribedintheSaMD:ClinicalEvaluationguidance(Figure3).
21
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.
22
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
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