Handbook of Artificial Intelligence in Healthcare: Vol 2: Practicalities and Prospects (Intelligent Systems Reference Library, 212)

429 Pages·2021·9.883 MB·English

by  Chee-Peng Lim (editor), Yen-Wei Chen (editor), Ashlesha Vaidya (editor), Charu Mahorkar (editor), Lakhmi C. Jain (editor)

Intelligent Systems Reference Library 212
Chee-Peng Lim · Yen-Wei Chen ·
Ashlesha Vaidya · Charu Mahorkar ·
Lakhmi C. Jain   Editors
Handbook
of Artificial
Intelligence
in Healthcare
Vol 2: Practicalities and Prospects
Intelligent Systems Reference Library
Volume 212
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· · ·
Chee-Peng Lim Yen-Wei Chen Ashlesha Vaidya
·
Charu Mahorkar Lakhmi C. Jain
Editors
Handbook of Artificial
Intelligence in Healthcare
Vol 2: Practicalities and Prospects
Editors
Chee-PengLim Yen-WeiChen
InstituteforIntelligentSystemsResearch CollegeofInformationScience
andInnovation andEngineering
DeakinUniversity RitsumeikanUniversity
WaurnPonds,VIC,Australia Shiga,Japan
AshleshaVaidya CharuMahorkar
RoyalAdelaideHospital AvantiInstituteofCardiology
Adelaide,SA,Australia Nagpur,India
LakhmiC.Jain
KESInternational
Shoreham-by-Sea,UK
ISSN1868-4394 ISSN1868-4408 (electronic)
IntelligentSystemsReferenceLibrary
ISBN978-3-030-83619-1 ISBN978-3-030-83620-7 (eBook)
https://doi.org/10.1007/978-3-030-83620-7
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Preface
ThisvolumeisasequelofHandbookofArtificialIntelligenceinHealthcare.Thefirst
volumefocusesonadvancesandapplicationsofartificialintelligence(AI)method-
ologiesinseveralspecificareas,i.e.signal,imageandvideoprocessingaswellas
informationanddataanalytics.Inthisvolume,severalgeneralpracticalitychallenges
and future prospects of AI methodologies pertaining to the healthcare and related
domainsarepresentedinPartIandPartII,respectively.Atotalof17chaptersare
includedinthisvolume.Adescriptionofeachcontributionisasfollows.
Decision-makingandcontrolinhealthcareenvironmentsareessentialactivities.
AI-based tools are useful for informed decision-making by both physicians and
patients.Albuetal.presentseveralintelligentparadigms,particularlyartificialneural
networksandfuzzylogic,formodelling,prediction,diagnosisandcontrolinhealth-
care applications. These intelligent tools are able to assist in decision-making and
controlprocessesforprevention,earlydetectionandpersonalizedhealthcare.
Tribertietal.aimtotacklethe“human”challengepertainingtoAIinhealthcare
practice,focusingonthepotentialriskinthedoctor–patientrelationship.Notingthat
thereisstilllimitedknowledgeontheusageofAIinhealthandmedicine,theystudy
the guidelines for identifying people who work with AI in the healthcare context.
Theyarguethatitisimportanttoformaninterdisciplinaryteamwithmemberswho
areabletovaluebothrigorouspracticeandhealthandwell-beingofpatients.
Belciug acknowledges the cross-fertilization of statistical analysis and AI for
devisingnewandimpactfulmethodstoassistinmedicalpracticeanddiscovery.It
isnecessarytoexploitstatisticalanalysisforvalidatingAI-basedmethodologiesin
healthcare,inordertoimprovereliabilityandcredibilityofthefindings.Inaddition,
usefulplan,designandimplementationofstatisticalanalysiswithrespecttoAIin
healthcareresearcharediscussed.
Pedelletal.examinethebenefitsofintroducinghumanoidrobotsintodifferent
activeageingandagedcaresettings.Itisfoundthatimplementationandinteraction
with robots require a well-designed plan, in order to develop trust and interest for
creating a shift in feelings of control pertaining to older adults as well as staff. In
a group setting, older adults can engage and enjoy the interaction with both the
robot and the wider group with positive effects. Successful interactions between
v
vi Preface
older adults and humanoid robots also need to be supported by motivational goal
modellingandtechnologyprobetechniques.
To combat cancer, which is a leading cause of mortality worldwide, physical
activity (PA) plays a significant role in reducing the risk of developing cancer.
Dadhania and Williams investigate the use of digital wearable tools in offering
advantagesincludingscale,costanddatacapture.Specifically,currentmethodsof
evaluatingPAincancerpatientsandhowwearableaccelerometersareusedincancer
clinical trials are studied. The successes and challenges associated with collecting
PAdatawithwearableaccelerometersindigitalhealthcaretrialsarediscussed.
Stankova et al. develop an online application of a home-administered parent-
mediated program for children with Autism spectrum disorder for enhancement
of their communication skills. The program is organized in modules, each with
differenttextandvisualcards,targetingimpressive/expressivelanguage,discourse
abilitiesandotherfunctions.Theinstructionalcomponentforparentsinvolvesactiv-
itieswithintheModdlee-educationalplatform.Theadministrationfortheprogram
followsastrictschedule,whichisalsoavailableinMoodle.
Toovercomethe“black-box”issue,Gerlingsetal.focustheirresearchonexplain-
able AI models. Different explanation needs with respect to stakeholders in the
caseofclassifyingCOVID-19patientsarestudied.Theneedforaconstellationof
stakeholders involved in human-AI collaborative decision-making is highlighted.
ThestudyprovidesinsightsintohowAI-basedsystemscanbeadjustedtosupport
differentneedsfromstakeholders,inordertofacilitatebetterimplementationinthe
healthcarecontext.
Restausesaneuralnetworkmodel,i.e.theself-organizingmap(SOM),toiden-
tify the emergence of COVID-19 clusters among different regions in Italy, in an
attempttoexplaindifferentcharacteristicsofthepandemicwithinthesamecountry.
Demographic,healthcareandpoliticaldataattheregionallevelareconsidered,and
theinteractionsamongthemareexamined.ByleveragingcapabilitiesoftheSOM
model,therelationsamongvariablescanbevisualized,andanearlywarningsystem
canbedevelopedtoaddressfurtherinterventioninthebattleagainsttheCOVID-19
pandemic.
CasacubertaandVallverdúindicatethatuniversalemotionleadstoaconceptual
biasintheuseofAIinmedicalscenarios.Indeed,emotionalresponsesinmedical
practicesaremediatedculturally.Asaresult,amulticulturalapproachisrequiredin
themedicalcontext,takingspecialconsiderationofemotionalvariationswithrespect
todifferentculturalbackgroundofpatients.Fromthecomputationalperspective,the
mostcommonbiasesthatcanoriginatefromdatatreatmentutilizingmachinelearning
algorithmsarediscussed.
TheRussianHocGrouponApplicationofAITechnologiesinHealthInformatics
(AHG2TC215ISO)highlightstheimportanceofdesigninganddeployingAI-based
systemsinaccordancewithestablishedguidelinesandlegislationformedicalappli-
cations.Inthisrespect,theformationofunifiedapproaches,definitionsandrequire-
ments for AI in medicine can significantly increase efficiency of the associated
developmentandapplication.Aconsistentapproachthroughglobalstandardization
can reduce the burden of stakeholders when establishing regulatory frameworks.
Preface vii
InitiativestodefinegoalsanddirectionsforstandardizationpertainingtoAIinthe
healthcareareasarediscussed.
Gusevetal.discussAIresearchanddevelopmentinRussia,wheregovernment
andexpertcommunityareworkingtogether todevelop legalandtechnicalregula-
tions.AI-basedsoftwareproductsfordiagnosticandtreatmentprocesses,including
clinicaltrials,areregulatedcomprehensively.Abalancebetweenacceleratingtime
to market of AI products and ensuring their safety and efficacy is required along
withappropriateconsiderationonthepotentialrisksandproblems.Thefirstseries
of Russian national technical standards to accelerate AI product development and
instiltrustinmedicalpractitionersarebeingestablished.
Kolpashchikov et al. address issues and challenges on the use of robotic tech-
nologiesinhealthcare.Inadditiontosurgicalandrehabilitationrobots,non-medical
robots that are useful for healthcare organizations to reduce costs, prevent disease
transmissionandmitigatethelackofworkforcearereviewed.Onecriticalissuethat
preventsfuturedevelopmentofroboticinhealthcareislackofautonomy,whichis
most challenging in minimally invasive surgery where flexible robots are used in
confined spaces. Innovative solutions for producing flexible robots as well as new
roboticdesignswithappropriateactuatorsandsensorsarerequired.
Belandi et al. conduct a review on the development of Internet of things (IoT)
andmachinelearningforsmarthealthcaresystems.Utilizingsmarthealthcaretech-
nologiesencompassingIoTandmachinelearningdevicesformonitoringhomeenvi-
ronmentsisbecomingpopular,particularlyforelderlypatientswithlong-termnon-
acute diseases who do not require hospitalization. The survey focus is placed on
twoaspects,namelyarchitecturesandalgorithms,oftheavailable technologies.A
taxonomyforclassificationofthereviewedmodelsandsystemsisprovided.
Hoppeetal.highlightthelackofstudiesonthepotentialofdigitalbusinessmodels
inthehealthcaresector.Keyperformanceindicators(KPIs),individualization,effi-
ciencyandcommunicationchannelsareidentifiedasthemainfactors.Anevaluation
withastructuralequationmodellingprocessindicatesthatKPIsandcommunication
channelshaveasignificantinfluenceonthepotentialofdigitalbusinessmodelsand
theirprocessesinhealthcare.Anoutlookonthebenefitsandchallengespertaining
totherapiddevelopmentofAIinthehealthcaresectorispresented.
Manresa-Yee et al. explore the transparency and interpretability issues of AI,
particularlydeepneuralnetworkmodels.ThroughexplainableAI,usersareableto
understand the predictions and decisions from AI-based systems, increasing trust-
fulnessandreliabilityofthesystems.Anoverviewonexplanationinterfacesinthe
healthcarecontextisdiscussed.Asurveyonhealthcarerelatedtostudiesonexpla-
nationsintheformofnaturaltext,parameterinfluence,visualizationofdatagraphs
orsaliencymapsispresented.
Giarelisetal.introduceagraph-basedtextrepresentationmethodfordiscoveryof
futureresearchcollaborationinthemedicalfield.Themethodcombinesgraph-based
feature selection and text categorization for formulation of a novel representation
of multiple scientific documents. The proposed method is able to provide useful
predictionsonfutureresearchcollaborations,asdemonstratedthroughtheuseofthe
COVID-19OpenResearchDataSet.
viii Preface
Shopon et al. investigate information security by combining privacy concepts
and biometric technologies. An analysis on the protection of physiological and
socialbehaviouralbiometricdatathroughavarietyofauthenticationapplicationsis
given.Currentandemergingresearchstudiesinthemulti-modalbiometricdomain,
including the use of deep learning-based methods, are explained. Open questions
and future directions in this research field are discussed, offering new methods in
biometricsecurityandprivacyinvestigationandprovidinginsightsintotheemerging
topicsofbigdataanalyticsandsocialnetworkresearch.
The editors are grateful to all authors and reviewers for their contributions. We
wouldalsoliketothanktheeditorialteamofSpringerfortheirsupportthroughoutthe
compilationofbothvolumesofthishandbook.Wesincerelyhopethattheresearch
and practical studies covered in both volumes can help instil new ideas and plans
for researchers and practitioners to work together, as well as to further advance
researchandapplicationofAIandrelatedmethodologiesforthebenefitsofhealth
andwell-beingofhumans.
WaurnPonds,Australia Chee-PengLim
Shiga,Japan Yen-WeiChen
Adelaide,Australia AshleshaVaidya
Nagpur,India CharuMahorkar
Shoreham-by-Sea,UK LakhmiC.Jain
May2021
Contents
PartI PracticalitiesofAIMethodologiesinHealthcare
1 IntelligentParadigmsforDiagnosis,PredictionandControl
inHealthcareApplications ..................................... 3
AdrianaAlbu,Radu-EmilPrecup,andTeodor-AdrianTeban
1.1 Introduction ............................................. 4
1.2 RelevantReferences ...................................... 9
1.3 Medical Decision-Making Based on Artificial Neural
Networks ............................................... 12
1.3.1 SkinDiseasesDiagnosis ........................... 12
1.3.2 HepatitisCPredictions ............................ 14
1.3.3 CoronaryHeartDiseasePrediction .................. 16
1.4 MedicalImageAnalysisUsingArtificialNeuralNetworks ..... 18
1.5 Artificial Neural Networks Versus Naïve Bayesian
Classifier ................................................ 21
1.5.1 HepatitisBPredictions ............................ 22
1.5.2 StrokeRiskPrediction ............................. 25
1.6 ProstheticHandMyoelectric-BasedModelingandControl
UsingEvolvingFuzzyModelsandFuzzyControl ............. 27
1.6.1 EvolvingFuzzyModelingResults ................... 28
1.6.2 FuzzyControlResults ............................. 32
1.7 Conclusions ............................................. 35
References .................................................... 35
2 ArtificialIntelligenceinHealthcarePractice:HowtoTackle
the“Human”Challenge ....................................... 43
StefanoTriberti,IlariaDurosini,DavideLaTorre,ValeriaSebri,
LucreziaSavioni,andGabriellaPravettoni
2.1 Introduction ............................................. 44
2.2 AIinHealthcare ......................................... 46
2.3 A“thirdWheel”Effect .................................... 48
2.3.1 “ConfusionoftheTongues” ........................ 50
2.3.2 DecisionParalysisandRiskofDelay ................ 51
ix