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Theroleofartificialintelligenceinhealthcarehasbeenahugetalkingpointinrecentmonthsandthere’snosignoftheadoptionofthistechnologyslowingdown,well,everreally.
AIinhealthcarehashugeandwidereachingpotentialwitheverythingfrommobilecoachingsolutionstodrugdiscoveryfallingundertheumbrellaofwhatcanbeachievedwithmachinelearning.
However,thefutureofhealthcare&thefutureofmachinelearningandartificialintelligencearedeeplyinterconnected.
Butfirst,adefinition:
ApplicationsofArtificialIntelligenceinhealthcareareendless.Thatmuchweknow.
Wealsoknowthatwe’veonlyscratchedthesurfaceofwhatAIcandoforhealthcare.Whichisbothamazingandfrighteningatthesametime.
Atthehighestlevel,herearesomeofthecurrenttechnologicalapplicationsofAIinhealthcareyoushouldknowabout(somewillbeexploredfurtherinthearticlewhilesomeusecaseshavegottentheirownstandalonearticlesonHealthcareWeeklyalready).
Let’slookateachoftheseamazinguse-casesinmoredetails.
AIinhealthcarecanproveusefulwithinclinicaldecisionsupporttohelpdoctorsmakebetterdecisionsfasterwithpatternrecognitionofhealthcomplicationsthatareregisteredfarmoreaccuratelythanbythehumanbrain.
Thetimesavedandtheconditionsdiagnosedarevitalinanindustrywherethetimetakenanddecisionsmadecanbelife-alteringforpatients.
DoctorscanalsofurthertheirlearningandincreasetheirabilitieswithinthejobthroughAI-driveneducationalmodules,furthershowingtheinformationmanagementcapabilitiesofAIinhealthcare.
Theabilitytoanalyzelargeamountsofpatientdatatoidentifytreatmentoptions.Thetechnologyisabletoidentifytreatmentoptionsthroughcloud-basedsystemsabletoprocessnaturallanguage.
AcquisitionscontinuetofeedtoinnovationneedsofbothlargeandoldbiotechfirmsandwiththedevelopmentofAI,there’splentytoofferupwhenitcomestocompanycontrol.
WithstartupscombiningtheworldofAIandhealthcare,there’smorechoiceforolderandlargercompaniestoacquireinformation,systemsandeventhepeopleresponsibleforleapsandboundsintechnology.
DrugdiscoveryisanothergreatplaceforAItoslipinwithpharmacompaniesabletoincludecutting-edgetechnologyintotheexpensiveandlengthyprocessofdrugdiscovery.
ThebenefitsofAIareinstantlyapparentwiththefocusontime-savingandpatternrecognitionupontestingandidentificationofnewdrugs.
Experiencingteethingproblemswiththeintroductionofanynewtechnologyisnotrare,butmustbeovercomeforlargescaleadoptionofAItooccurinthehealthcaremarket.
Ultimately,theadoptionofAIwillattractstakeholderswhowillinvestinAIandsuccessfulcasestudiesneedtobehighlightedandpresentedforfutureencouragement.Thesecasestudieswillrequiresomeearlyadoptersofhealthcarecompaniestokickstarttheprocess.
Privacywithinhealthcareis,bynature,extremelysensitiveandthusconfidential.
Deeplearning,AIandmachinelearningdonothavetheabilitytoaskthequestion‘why’.Asaresult,thelogicbehinddecisionsisnotjustified,meaningmostlyguessworkisrequiredtohowthedecisionwasmade.
Howandwhythedecisionhasbeenmadeiskeytotheinformationwithinthetreatmentplan.Withalackofreasoningcancomealackofconfidencewithinthedecision,potentiallyrenderingthetechnologyasunreliableoruntrustworthybybothpatientsandprofessionals.
WhenitcomestothestakeholderswithintheadoptionofAIinhealthcare,everyone,includingpatients,insurancecompanies,pharmacompanies,healthcareworkersetc.arekey.
Thatsaid,therecontinuestobesignificantpushbackwhenitcomestoAIadoptionintheclinicaldecisionsupportprocessasscientistsandmedicalpersonnelcontinuetoapproachthetopicofAIwithincrediblecaution.
Withminimaloperatortrainingneededanddesignwithcommonoutputformatsthatdirectlyinterfacewithothermedicalsoftwareandhealthrecordsystems,thesystemisincrediblyeasytouseandsimpletoimplement.
Aclearoutputfromthesystemallows60secondstoidentifywhethertheexamqualitywasofsufficientquality,thepatientisnegativeforreferableDRorthepatienthassignsofreferableDR.FollowingsignsofreferableDR,furtheractionintheformofahumangraderover-reading,teleconsultationand/orreferraltoanophthalmologistmaybesuggested.
WiththelaunchoftheAppleWatchSeries4andthenewelectrodesfoundwithinthegadget,it’snowpossibleforuserstotakeanECGdirectlyfromthewrist.
ThenumberofaccessoriesandaddonsthattechnologycompaniesarereleasingfortheAppleWatchisalsobeginningtocrossoverintothehealthindustry.Companies,suchasAliveCorhavereleasedcustomstrapsthatallowaclinicalgradewearableECGthatreplacestheoriginalAppleWatchband.AlthoughthestrapmayberendereduselesswiththeSeries4,foranyoftheearlierwatches,thestrapmayproveausefulattachmenttoidentifyAF.
Whatalltheseexampleshaveincommonishowwearabletechnologiesareslowlybeingrepurposedoraugmentedtoimprovemedicaloutcomes.Andinalltheseexamples,artificialintelligenceisleveraged,‘underthehood’,tocollect,analyzeandinterpretmassiveamountsofdatawhichcanimprovethequalityoflifeofpatientseverywhere.
Late2018markedtheannouncementfromAidocthatithadbeengrantedit’sU.S.FDAclearanceofitsfirstAI-basedworkflowsolution,thediagnosisofbleedsonthebrain.
Integrationintothehealthindustryissimpleandwon’trequiresignificantITtimeandwithadditionalhardwarenotrequired,it’sasimpleresourcethatcanbesetupandmaintainedremotely.Withasolutiontoassistworkflowoptimizationsandincreasethenumberofcorrectandhigh-qualityscans,thedemandforthisAI-enabledtechnologyisexpectedtobehuge.
1.Usingafunduscamera,atrainedoperatorcapturestwo-color,45°FOVimagespereye
2.TheimagesaretransferredtotheIDx-DRclientonalocalcomputer
3.TheimagesarethensubmittedtotheIDx-DRanalysissystem
4.Inside60seconds,IDx-DRprovidesanimagequalityordiseaseoutputandfollow-upcareinstructions
5.IfnegativeformtmDR,thepatientcanberescreenedatalaterdate.IfpositiveformtmDR,refersthepatienttoeyecare.
iCADannouncedthelaunchofiRevealbackin2015withthegoaltomonitorbreastdensityviamammographytosupportaccuratedecisionsinbreastcancerscreening.
Withanestimated40%ofwomenintheUShavingdensebreasttissuethatcanblockthemammographyfromviewingpotentialcanceroustissue,theissueishugeandasolutionwasimperative.
KenFerry,CEOofiCADstatedthat“WithiReveal,radiologistsmaybebetterabletoidentifywomenwithdensebreastswhoexperiencedecreasedsensitivitytocancerdetectionwithmammography.”
Mr.alsoFerryaddedthat“TheincreasingsupportforthereportingofbreastdensityacrosstheUS,thereisasignificantopportunitytodriveadoptionofiRevealbyexistingusersofthePowerLookAMPplatformandwithnewcustomers,whichrepresentsanincremental$100millionmarketopportunityoverthenextfewyears.Longer-term,weplantointegratetheiRevealtechnologyintoourTomosynthesisCADproduct,whichisthenextlargegrowthopportunityforourCancerDetectionbusiness.”
Ultimately,thesystemremainsattheforefrontofbreastcanceridentificationinwomenintheU.S.andwithsomanylivesexpectedtobesaved,IthinkeveryonecanagreewhatafantasticuseofAIitis.
QuantXisthefirstMRIworkstationtoprovideatruecomputer-aideddiagnosis,deliveringanAI-basedsetoftoolstohelpradiologistsinassessmentandcharacterizationofbreastabnormalities.
UsingMRimagedata,QuantXusesadeepdatabaseofknownoutcomesandcombinesthiswithadvancedmachinelearningandquantitativeimageanalysisforreal-timeanalyticsduringscans.Afastcomprehensivedisplayisseenwithallprocessingon-demandinreal-timewithrapiddisplayandreformattingofMPR,fullMIPs,thinMIPsandsubtractions.
AQIScore,aclinicalmetriccorrelatedtothelikelihoodofmalignancyiscalculatedwiththeimagesandregionsofinterestduringscans.Thisispairedwithasimilarcasecompare,atoolwhichallowsupto45similarcasesfromareferencelibrarytobedisplayedforeachanalyzedlesion.
Thisinformationispassedontoradiologiststomakeaccurateclinicaldecisions,decreasingthenumberofincorrectdiagnosesinhigh-riskenvironments.
Coronarycalciumscoringisabiomarkerofcoronaryarterydiseaseandquantificationofthiscoronarycalcificationisaverystrongpredictorforcardiovascularevents,includingheartattacksorstrokes.
Aconventionalcoronarycalciumscoringrequiresdedicatedcardiac,ECGgatedCTperformedwithandwithoutcontrast.
However,inrecenttimes,areliablederivationofcoronarycalciumscorehasbeenfoundalgorithmicallywiththeuseofAIfromlowdosechestCTdata.ZebraMedical’sscoringalgorithmusesthesestandard,non-contrastChestCTsandautomaticallycalculatestheCoronaryCalciumScores.
Thetoolisvitalfortheearlydetectionofpeopleathighriskofseverecardiovasculareventsthatotherwisewouldnotbeawareoftheriskwithoutextensivetesting.
Ultimately,EchoMDandAutoEFwillstrivetomaximiseworkflowefficiencywhilereducingtheerrorinclinicaldecisionmakingbyhelpingphysiciansmakecorrectchoices.
NeuralAnalytics,amedicaldevicecompanytacklingbrainhealth,announcedadeviceforparamedicstrokediagnosisbackin2017,revolutionisingthewaythatparamedicsdiagnosestrokevictims.
NeuralAnalytic’LucidM1TranscranialDopplerUltrasoundSystemtacklestheissuesofexpensiveandtime-consumingstrokediagnosisforpatientsthatsufferbloodflowdisorders.
Thisultrasoundsystemisdesignedformeasuringcerebralbloodflowvelocities.Thisisnojoke.Issuccessful,thistechnologywillchangehowearlydoctorscandetectstrokeandcoulddrasticallyimprovepatientoutcomes.
TheuseofTranscranialDoppler(TCD),atypeofultrasound,allowsforAItoassessthebrain’sbloodvesselsfromoutsidethebody,preventingtheneedformoreinvasivetests.TheAIsoftwarehelpsphysiciansdetectstrokeandotherbraindisorderscausedbybloodflowissues,increasingthecapabilityofcorrectclinicaldecisions.
IcometrixisacompanywiththemissiontotransformpatientcarethroughimagingAI.WithMRIbraininterpretationusedtodecreaseerrorinclinicaldiagnosis,thecompanyiswellonthewaytochangingthewaythatabnormalitiesarediscoveredwithinthebrain.
Withquantificationofclinicallyrelevantbrainstructuresforindividualpatientsandarangeofidentifiableneurologicaldisorders,there’splentythatAIhadtoofferinthespace.
TheOsteoDetectsoftwareisanAIbaseddetection/diagnosticsoftwarethatutilisesintelligentalgorithmsthatanalyzetwo-dimensionalX-rays.
Thesoftwaresearchesfordamageinthebone,specificallyacommonwristfracturecalledthedistalradiusfracture.Thesoftwareutilisesthemachinelearningtechniquestoidentifytheseproblemareasandmarkthelocationofthefractureontheimage,assistingthephysicianwithidentificationofabreak.
OsteoDetectisdesignedforuseinavarietyofdifferentsituationsincludingprimarycare,emergencymedicine,urgentcareandspecialtycare,suchasorthopedics.Althoughthesoftwareisnotintendedtoreplaceaclinician’sreviewandindependentthoughtsandjudgementofaradiograph,itisdesignedtoassist.
PoweredbyAI,Viz.aiallowsforsynchronizedstrokecaretoimproveaccesstolife-savingtherapies.
Stroketeamscanthenconsultinrealtimeviaamobileinterface,allowingafocusonfasttreatmentdecisionsthatnotonlysavebrains,butlivestoo.
MedicalimagingcompanyArteryshasbeendemonstratingitswiderangeofAIpoweredimagingservicesandsolutionsforanumberofyearswithliverandlungcancerMRIandCTdiagnosisaswellasMRIheartinterpretation,coveringhugeareasofpotentialhealthscares.
ArterysLungAIsimilarlyprovidesimprovedtrackingoflungnodulesandclinicalmanagementoflungcancerinpatients.Thesolutionyieldsmoreaccuratemeasurementsthanotheravailablesolutionswhiletheautomatedlongitudinaltrackingassistsclinicianswithbettermanagementofthenodules.AutomaticreportingcapabilitiesarealsousedtodeliversummariesandfindingsfromtheAI,demonstratingrelevantquantitativeinformation,imagesandcomments.
ArterysCardioAIMRnotonlyusesAIanddeeplearning,butallowscloudcomputingtostepintothemixtoautomatetheanalysisofcardiacmagneticresonance(MR)images.Thesolutionallowsfortediousmanualtaskstobeavoided,effectivelymanagingworkflowandquicklyandeasilyidentifyinganddeterminingtreatmentfortrackheartproblems.
Ultimately,Arterys’workinthefieldisvitaltoimprovingworkflowmanagementanddevelopingsystemsthatbetterclinicaldecisionsinbothspeedandaccuracy.
Withthecompanycontinuouslyupdatingthealgorithm,wecanexpecttoseeanevenhigherpercentageofcorrectclinicaldecisionsintheverynearfuture.It’sanexcitingtimeforallinvolvedwithintheindustry.
TheBritishcompanyhasleveragedmachinelearningwithdatafromover15,000patients,allowingthealgorithmtospoteyediseasefromopticalcoherencetomography(OCT).Thesolutionremovesalargechunkoftimethatoftenrequiresexpertanalysistointerpret,butwithAI,weseeasaccuratediagnosisautomatically.
Althoughthetechnologyisintheearlystages,itisexpectedthatitwillevolveuntildoctorscanfullyprioritizethoseindesperateneedoftimesensitivetreatment,savingthesightofpatients.
Knowledgemanagementfordoctors:
JohnsonandJohnsonareoneofthepioneersofaVRmoduletotraindoctorswithVRbasedheadsetsusedtoallowhands-onpracticeformedicalprofessionals,reducingreal-lifemistakesandsurgerycomplications.
ByfocusingonthreeuniqueVRtrainingmodulesfororthopaedicsurgery,totalhip,totalkneereplacementandhipfracture,theVRexperiencehasledtopraisethroughoutthemedicalcommunity.
Thesoftwareprovidesasafeandconvenientlearningexperiencewheredoctorsareabletoreceiveinstantfeedbackandmakebetterprogresswiththeirpractice,butthepossibilitiesareendless.
Knowledgemanagementforpatients:
Onthepatientside,thebiggestusecasesarehealthbotsandself-assistappswithdigitaldoctorstakingoverthespaceonceruledbyhumaninteraction.
With24-houravailabilityandcoveragefordoctorsinruralareas,notonlyistheappgreatformaximisingpatientcomfort,butforminimisingthetimetakentodiagnose.Withfewerpeopleseeingdoctorsinperson,thisreducesthephysicalstrainonalreadystrugglinghospitals.Thatmeanslesswaittimesthroughout,lesscongestionandbetterdiagnosisfromdoctorswithmoretimetofocusonseriousissues.
There’splentytochoosefrom,withthetop12healthchatbotsratedinnoparticularorderas:
Selfdetectionofskincancer
Withover1.1musersandover3.5mpicturestaken,theappcouldbeshowingthestartofsomethinghugeintheformofselfdiagnosisofseriousmedicalconditions.
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