ArtificialIntelligenceinHealthcare:Advantages&Challenges

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Thesystemcanhandlemosttaskspreviouslyhandledbyhumanswhiledoingitfasterandcheaper.Thissignificantbenefithaseasedstakeholders’healthsectoractivities,especiallyhospitaladministrators,doctors,andpatients.

Artificialintelligencehascontinuedtoreinvigorateandreinventitself.Therearenowmodernmachinelearningsolutionscapableofacting,learning,understanding,andpredicting.Thisisastepfurtherthanthesurgery-assistingrobotsandlinkinggeneticcodespreviouslydrivenbyAI.

Artificialintelligencedevelopmentinhealthcarecomeswithsomerisksandchallenges.Forinstance,AIsystemerrorsputpatientsatriskofinjuries.Likewise,thepatient’sdataforAIreferenceputsthepatientattheriskofprivacyinvasion.

ThispostdiscussesthemajoropportunitiesthatcomewithAIwhiletouchingonthechallengesandrisksthatcomewithsuchopportunities.Wewillstartwiththeadvantages.

TableofContents

WithAIinnovations,thesedisadvantagedareascanenjoyanefficienthealthcareecosystem.AI-backeddigitalsystemscanfacilitatepatientdiagnosisandtreatment.Therearededicatedapplicationsdevelopedtohelpinternationalandnationalhealthcareorganizationscometogetherandrendernecessaryassistancetopeoplewhoneedthem.

ExplorehowAdareactedtotheglobalpandemicsbyenablingdiseaseself-assessmentsacrossthecountries.

2.SharingInformationIsSimple

EasyinformationsharingisanadditionaladvantageofArtificialIntelligenceinhealthcarethatmeritsmention.TofullyrealizethepotentialofAIandprecisionmedicine,algorithmsmustbeabletoanalyzelargeamountsofdataquickly.

Artificialintelligenceinhealthcarecanlocateparticularpatientdatamoreeffectivelythanconventionalcare,givingdoctorsmoretimetoconcentrateonmedicationsandtreatments.

Theconditionrequiresimmediatetreatmentandmanagement,andArtificialIntelligencecanprovideproviderswithdata-drivenassistance.Forinstance,aglucosebroadcastingsystemenablespeoplewithdiabetestomonitortheirglucoselevelsinreal-timeandacquirereportstodiscussandmanagetheiradvancementwithphysiciansorsupportgroups.

Wearabledevicedatacanindicatethelikelihoodofcontractingaparticulardisease.ArevolutionaryhealthcaredatatreasuretrovecouldemergeasthehealthcareindustryusesAItostore,collect,andanalyzedata.

3.Earlydiagnosis.

AI-driventoolsnowrelyonpeople’sdatatoassessthepreviousandpresenthealthissuesofpatients.Bycomparingthediseasedetails,healthcareprofessionalsarepositionedtodiagnosemoreaccurately.Thedatabaseinseveralhealthcaremobileappshascomputedmillionsofsymptomsanddiagnoses.Moreimportantly,itcanpredictthepotentialhealthissuesanindividualcanencounterinthefuture.

ThankstoAIalgorithms,healthcareprocessesarenowfasterandatafractionoftheoriginalcosts.Frompatientexaminationtodiagnosis,theAIhasreallychangedthegameintermsofspeedandcosts.Forinstance,theAIcanidentifythebiomarkersthatsuggestdiseaseinourbodies.AI-algorithmshaveminimizedthemanualworkinvolvedinspecifyingthesebiomarkers.Themassiveautomationmeanswecannowsavemorelivesbyactingfaster.

Patientshaveapoorexperienceduetocrowdedandchaotichealthcarefacilities.UtilizingAIcanhelppatientsquicklynavigatethroughdata,obtainreports,andbedirectedtowheretogoandwhotosee,therebyavoidingtheusualconfusioninhealthcaresettings.Arecentstudyrevealsthat,accordingto83%ofpatients,poorcommunicationisthemostunpleasantaspectoftheirexperience.

6.Efficientanduniqueassistanceinsurgery

ArtificialIntelligencedevelopmenthastakenahugeleapinroboticapplications.Thesameisthecaseformachinelearningimplementationinsurgery.TherearededicatedAISurgicalSystemsthatcanexecutethetiniestmovementswith100%accuracy.Thismeanswecandocomplexoperationsefficientlywithreducedrisksofsideeffects,bloodloss,orpain.Likewise,post-surgeryrecoveryisfasterandeasier.

Forinstance,patientswaitingforoperationsaresubjectedtoAntibacterialNanorobotstoeliminateallinfectionsintheirbloodbeforegoingundertheknives.Perhaps,thebestpartistheAI-backedinformationonthepatient’spresentsituation,availabletosurgeonsinreal-time.Thishashelpedtoquelldoubtsinpatients,especiallyregardingsurgeryundergeneralanesthesia.

7.Enhancedhumanabilitiesandmentalhealthsupport

Robotscannowassistpatientsalongsidethemedicalstaff.Forinstance,exoskeletonrobotscanhelpparalyzedpeopleregaintheirmobilitywithlittleornohelpfromcaretakers.Likewise,smartAI-backedprosthesesarefittedwithsensorsthatserveasmorereactivelimbsthantraditionalmodels.

Servicerobotsfrommachinelearningimplementationcanhandledailytasksandkeepthecompanyofpatients.Therearededicatedcompanionandconversationalrobotsthatcarryoutnecessarytestsandchecks–sugarlevels,bloodpressure,controllingtemperature,andeventakingpills.Therearerobotsdevelopedtohelpdepressedpatients,thankstotheirin-builtanalyticcapabilities.Withthesecapabilities,theycananalyzethemodofthepatientsandhelpthemfeelmorepositive.

That’sallaboutthebenefits.Now,let’smovetothechallenges.

JustlikethereisaYinandYangineverything,thereareadvantagesanddisadvantagesofartificialintelligenceinhealthcareindustryaswell.DespitethemassiveprogressthatcomeswithArtificialIntelligencedevelopment,fewnotablechallengesareyettobesorted,mainlywhenitinvolvesdata.Togetthebestoutofartificialintelligencedevelopmentandmachinelearningimplementation,weneedtosolvechallengeslike:

1.Datadigitizationandconsolidation.

Theunderlyingprincipleinmost,ifnotall,AIprojectsisthegarbage-in-garbage-outprinciple.WithoutthemassivechunkofdatafedintotheAIsystems,itispracticallyimpossibletogetresults.Thisiswhyitisimportanttosourcehigh-qualityhealthcaredata–amovethathasbecomeincreasinglydifficultovertheyears.Thedifficultyisattributedtothefragmentedandunorganizedhealthdataspreadacrossvariousdatasystemsandorganizations.Patientschangeinsuranceprovidersandhealthcareproviderstoofrequently,makingdataacquisitionachallenge.

Withupdatedanddigitalrecordsystems,improvedaccuracyandefficiencyisguaranteedinthehealthcaresector.Thisiswhyhealthcarestakeholdersmustperfectdigitizationandconsolidationofmedicaldata.ItistheonlywaytofeedtheAIwiththeinformationneededtodeliveraccurateanalysisanddriveefficientprocesses.

ThesuccessfulimplementationofAIwithinyourownbusinessisanotherobstacle.Inaddition,properexpertswithknowledgeofthehealthcareindustryandexperiencecreatingAI-integratedsolutionsarehighlyrequired.Nevertheless,Datascienceisasmallareaofexpertise.

3.Updatingregulations

Therearetonsofstrictconfidentialityandprivacylawsguardingmedicalrecordsworldwide.Withsuchprotection,wecaninterpretdatasharingamongAIsystemsintolegalviolations.Incaseswhereitislegal,patientsmustprovidetheirconsenttoobtainmedicaldataandusethemforsuchpurposes.

Thelogisticalchallengethatcomeswiththisisimmense.Thereisaneedforflexiblerulesonmedicaldataacquisitionwithidentityprotection.Medicalinstitutionsmustalsoensurestrictcompliancewithsuchrulesandbeaccountableforpatientdataacquisitionanduse.Thisisthemostviablewaytocurateaccurate,high-qualitymedicaldataforAItechnologies.a

4.Humaninterventions.

AgoodnumberofpatientsandmedicalprofessionalshavedoubtsaboutAI.Radiologistsdonotwantrobotstotakeovertheirjobs.Patientsdonotwanttosurrenderthemselvestomachinestodealwiththeirhealthconcernsproperly.

Withoutcorrectingtheseimpressions,itwon’tbeeasytobuildanAI-drivenhealthcaresystem.AIshouldbeseenasahelperdesignedtoassisthealthcarepractitionerstoexecutetheirdiagnosticroles.TherightmindsetiskeytoembracingAI-assistedmedicalpractices.

Let’sdiscussthepotentialrisksassociatedwithAIinthehealthcaresector.

1.Errorsandinjuries.

AIsystemsarepronetoerrors,whicheventuallyleadstopatientinjuryorothersignificantproblems.Forinstance,apatientmaytakeadrugwronglyrecommendedbytheAIsystem,leadingtomorequestions.Likewise,theAI-drivenradiologicalscanmaymissatumor.ThewrongallocationofahospitalbedbasedonAIpredictionscanleadtoinjuriesandrelapse.

ThebiggerproblemwithAI-relatederrorsisthepotentialtobefar-reaching.Tonsofpatientscouldsufferfromjustoneerror.Likewise,nofamilyorfriendwilltakeitlightly,hearingthattheirlovedonesufferedasetbackbecauseofacomputererror.TheonlinepatientreviewsystemmeansitismorecomfortabletoputoutabadwordaboutAIcapabilitiesbuttoughtorecallit.

2.Privacyissues.

Privacyisaseriousconcernregardingpatientdataacquisition.Whileresearchershavemeasuresinplacetoprotectpatientdata,therearemalicioushackersouttheretryingtogetaccessatallcosts.IfagiantlikeGooglecanhaveprivacyandpatientdataissues,thenitmeansnooneinAIshouldoverlookprivacyissues.

AnotherwayprivacyisatriskwithAIisthesystem’scapabilitytopredictinformationaboutpatients,evenwhenthealgorithmwasnotfedwithsuchdata.

3.Inequalityanddiscrimination.

4.Professionalreshuffling.

Inthisarticle,wehavealreadydiscussedtherequirementforhigh-qualityinformation.However,thereisasubstantialriskofincorrectdiagnosisifArtificialIntelligencesystemsarenotmanagedwithsufficientinformationfromvariousbackgrounds.DoctorsonlyhavesufficientAIexperiencetopointoutamistakeifAIcanbeexplained.Accountabilityisquestionedintheeventofawrongdiagnosis.

Forinstance,isthedoctorinchargewhousedAItomakethedecisionresponsibleforthemishaporerrorHealthcaremayfaceavarietyofethicalquandariesasaresultofAIdeployment.However,itisessentialtocomprehendthatmedicalandAIdiagnoseshaveanedgeoferror.Aworldwideresearchonprimarycaremistakesfoundthat5percentoutofallthepatientsreceiveanincorrectdiagnosisandAtertiaryofallseriousillnessesmisdiagnosesthatresultinharm.

Finally

Whiletherearerisksandchallenges,itisclearthatroboticsandAIbringhugebenefitstotheglobalhealthcareecosystem.TheInternetofMedicalThingsandAI-driventoolshaverecordedsignificantsuccess,especiallyinhumanlives.WithAI,wearenowbetteralignedwithconscioushealthmanagementandanoverallhealthylifestyle.

UpendraPatelisDirector&FounderatTriStateTechnology.Heisanenthusiastictechno-savvyperson,andcloselyfollowsalltechnologicalplatformsacrosssoftwaredevelopmentindustry.Hehasanexperienceof20+yearswiththeSoftwareindustryandheisactivelyinvolvedinupcomingsoftwaretechnologyimplementationtrends.HehasanextensiveexperienceindeliveringsoftwaresolutionsfordifferentindustrieslikeHealthcare,Finance,Insurance,Logistics,Education&Ecommerce&Retailetc.HisdiversifiedexperienceincludesProjectManagement,DeliveringManagement,TechnologyArchitecture&StrategicRoad-Mapping.Underhisleadership,TeamTriStatehasshownasignificantgrowth.Withhisstrategicallythoughts,hedrivesteamexceptionallywell.

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ArtificialIntelligenceinHealthcare:Advantages&Challenges

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