Download ACR Comments to White House OSTP on AI

July22,2016
Attn:TerahLyons
OfficeofScienceandTechnologyPolicy
EisenhowerExecutiveOfficeBuilding
1650PennsylvaniaAve.NW,
Washington,DC20504
Subject:(2016-15082;81FR41610)RequestforInformationonArtificial
Intelligence;CommentsoftheAmericanCollegeofRadiology
TheAmericanCollegeofRadiology(ACR)—aprofessionalorganizationrepresenting
morethan35,000radiologists,radiationoncologists,interventionalradiologists,
nuclearmedicinephysicians,andmedicalphysicists—appreciatestheopportunity
torespondtotheWhiteHouseOfficeofScienceandTechnologyPolicy’s(OSTP)
RequestforInformation(RFI)on“ArtificialIntelligence”(AI)publishedinthe
FederalRegisteronJune27,2016(documentnumber2016-15082;81FR41610).
TheACRsupportsthefederalgovernment’seffortstoleverageAIandmachine
learningtoimprovegovernmentservicesingeneral,andweurgeadditionalfederal
supportfor,andcollaborationwith,professionalassociationsandother
stakeholderswithinspecificfieldsofinteresttoensureasafeandefficacioususeof
thistechnology.
ThefollowingcommentsonthequestionsenumeratedintheRFIwerecompiledby
membersoftheACRClinicalDataScienceCommittee,ACRCommissionon
Informatics,andACRResearch.Individualcontributingmembersarelistedatthe
endofthissubmission.
ACRResponsestoRFITopics
1. ThelegalandgovernanceimplicationsofAI:
Healthcareinstitutions,radiologygroups,andvendorsplanningtodevelop
algorithmsusingsourcedatasuchaselectronichealthrecordtechnologyand/or
patientdiagnosticimagingdataneedguidancefromagenciesonissuesofpatient
1
consentandappropriatemethods/bestpractices.Moreover,AIincorporationinto
clinicalradiologypracticecanintroducenewmedico-legalrisksanduncertainties.
RelatedconcernscouldpotentiallydiscourageacceptanceandproliferationofAIby
providers.
2.TheuseofAIforpublicgood:
AIcouldoffervariousbenefitstomedicalimaginginthefuture,including
augmentingthecapabilitiesofradiologiststoenhancetheirefficiencyandaccuracy,
aswellasreducingcostsbyimprovingtheappropriatenessandcost-effectivenessof
medicalimagingutilization.
TheuseofAIandmachinelearninginhealthcareingeneralcouldbebestappliedto
theareasofprecisionmedicine,predictiveanalytics,andoutcomesassessments.AI
canstreamlinehealthcareworkflowandimprovetriageofpatients(especiallyin
acutecaresettings),reduceclinicianfatigue,andincreasetheefficiencyandefficacy
oftraining.Moreover,shortagesofmedicalexpertstomeettheneedsofvulnerable
andunderservedpopulationsindomesticandinternationalsettingscould
potentiallyberelieved,inpart,byAI.
3.ThesafetyandcontrolissuesforAI:
Safetystandardsshouldbeidentifiedtofacilitatetheproperdevelopmentand
monitoringofAI-driventechnologiesinmedicalimaging.Thiscouldbeaddressed
throughacombinationofregulatoryoversightandprofessionalassociation
validationorcertificationofalgorithms.Federalagenciescouldalsopartnerwith
professionalandtradeassociationstodevelopstandardizeddatasetsforalgorithm
trainingandtesting.
Inadditiontooversightoverthetechnology,safetyissuesneedtobeaddressedvia
trainingandbestpracticesforpractitionersonappropriateincorporationofAIinto
clinicalradiology.
5.Themostpressing,fundamentalquestionsinAIresearch,commontomostor
allscientificfields:
ThemostuniversalAIresearchquestionishowtomeasuretheeffectivenessofthe
technology;however,thespecificdefinitions/measuresofeffectivenessandtesting
methodologieswouldlikelyvaryfromfieldtofield.Inmedicine,researchinto
effectivenessshouldfocusonareassuchasdiagnosticerrorreduction,improved
accuracy,workflowenhancement,andefficiencygains.Moreover,researchshould
explorehowAItoolscanbeseamlesslyintegratedintoclinicalworkflowandto
whatdegreethereisimpact,bothpositiveandnegative,onclinicaldecisionmaking
andpatientcareoutcomes.
2
6.ThemostimportantresearchgapsinAIthatmustbeaddressedtoadvance
thisfieldandbenefitthepublic:
IntermsoftheapplicationofAItomedicalimaging,thereisaneedtodefine
standardsbywhichimagesandcorrespondingdatashouldbestructuredto
facilitateAIresearch.Asmentionedabove,researchneedstoalsoexploreimpact
measurementofAItoolsonimage/datainterpretation,diagnosticaccuracy,and
workflowefficiency.
8.Thespecificstepsthatcouldbetakenbythefederalgovernment,research
institutes,universities,andphilanthropiestoencouragemulti-disciplinaryAI
research:
TheDepartmentsofHealthandHumanServices,VeteransAffairs,andDefense
shouldincreasegrantopportunitiestostudyanddevelopAItechnologiesinmedical
imaging.Federalagenciespartneredwithprofessionalassociations,academic
institutions,patientadvocates,andotherorganizationscoulddevelopand/or
disseminatepolicy,ethical,scientific,andindustrystandards,includingthose
relatedtointeroperabilityandgeneralizabilityofAI-driventechnologies.Standards
aroundsecurity,privacy,data-sharing,andtheuseofcommondatasetsfor
researcherswouldfacilitatetheimprovedgeneralizabilityofalgorithms.
Importantly,addedexpertiseindomainsoutsideoftraditionalcomputerscience
andhealthinformationtechnology(e.g.,imageperception,humanfactors,and
safety)shouldbeconsulted.
9.ThespecifictrainingdatasetsthatcanacceleratethedevelopmentofAIand
itsapplication:
TheclassofAItechnologiesthatutilizemachinelearningtechniques(neural
networks,deeplearning,etc.)requirelargedatasetstolearnrelationshipsbetween
inputsandoutputsofinformationprocessingchains,ortodiscoverandcategorize
patterns.Thefeasibilityofacquiringandutilizingsuchlargedatasetsvaries
tremendouslyacrossapplicationdomains.Thereareseveralsignificant
impedimentstoacquiringsuchdataforhealthcareapplicationsofAI,includingthe
needtoprotectpatientprivacy,collectdataacrossdistributedsitesandacross
multiplemodalities(genomics,radiomics,pathology,etc.).
However,theseproblemshavelongbeensolvedforclinicalresearchinitiatives,e.g.,
clinicaltrialsandregistries.Theinformaticsplatformsandprocessesdevelopedto
collectandcreatesuchrepositoriescouldbereadilyadaptedforthehealthcareAI
domain.Inaddition,datafromclosedinitiativescanberepurposedforAIresearch.
TheACRhasalreadybeguntosupporttheAIresearchofitsmembersandpartners
inacademiaandindustry,utilizingourTRIADandDARTplatformsusedforclinical
imagingresearch.
3
De-identifiedtrainingsetsofvarioushealthcaredatatypes—includingmedical
imagingdata(MRI,CT,X-Ray,Ultrasound,PET)—coveringthewholespectrumof
pathologiesneedtobeaccessibleinthepublicdomainandvalidatedtoensurethat
thesesetsmeetgovernment,academic,andindustrystandards.Thecreationand
curationoflabeleddatasetsisatimeconsumingyetcriticalprocessinthe
developmentofAItechnologiesinhealthcareandmedicalimaging.
11)AnyadditionalinformationrelatedtoAIresearchorpolicymaking,not
requestedabove,thatyoubelieveOSTPshouldconsider:
TheACRbelievesAIhasthepotentialtoalleviateadministrativeburdenand
inappropriateutilization,anditcouldsomedayincreasetheprecisionandefficiency
ofcertainmedicalservices,includingdiagnosticimaging.Thistechnologyhasthe
potential,withappropriatetesting/validationandsafeguards,toimprovethevalue,
safety,andappropriateutilizationofmedicalimaging.AIalsohasthepotentialto
shiftmoremundanetasksfromradiologistsandotherphysicianstomachines,
freeingradiologiststofocusonpatientcare,includinginterpretingimagesand
providingclinicalconsultationstootherspecialists.
TheAmericanCollegeofRadiologyappreciatesthisopportunitytoprovideinputto
OSTPstaffandmembersoftheNationalScienceandTechnologyCouncil
SubcommitteeonMachineLearningandArtificialIntelligence.Wewelcomefurther
communicationsonthisandrelatedtopics.PleasecontactGloriaRomanelli,JD,
SeniorDirector,LegislativeandRegulatoryRelations([email protected]),or
MichaelPeters,DirectorofLegislativeandRegulatoryAffairs([email protected]),if
interestedinreachingouttotheACR.
Sincerely,
JamesA.Brink,MD,FACR
Chair,BoardofChancellors
AmericanCollegeofRadiology
KeithDreyer,DO,PhD,FACR
Chair,CommissiononInformatics
AmericanCollegeofRadiology
4
GarryChoy,MD,MBA
Chair,ClinicalDataScienceCommittee
AmericanCollegeofRadiology
Contributors:
ACRCommissiononInformatics-ClinicalDataScienceCommittee
GarryChoy,MD,MBA,Chair
SawfanHalabi,MD
KathyAndriole,PhD
KeithDreyer,DO,PhD
ChristophWald,MD,MBA
WoojinKim,MD
MikeMcNitt-Gray,PhD
BobNishikawa,PhD
JamesStone,MD,PhD
RaymGeis,MD,FACR
TonyScuderi,MD
LauraCoombs,PhD
MikeTilkin,MSandACRCIO
ACRResearch
JohnPearson,PhD
5