Raman, Raghu; Pa naik, Debidu a; Hughes, Da id Lau ie; Nedungadi, P ema
A icle
Un eiling he dynamics o AI applica ions: A e iew o
e iews using scien ome ics and BERTopic modeling
Jou nal o Inno a ion & Knowledge (JIK)
P o ided in Coope a ion wi h:
Else ie
Sugges ed Ci a ion: Raman, Raghu; Pa naik, Debidu a; Hughes, Da id Lau ie; Nedungadi, P ema
(2024) : Un eiling he dynamics o AI applica ions: A e iew o e iews using scien ome ics and
BERTopic modeling, Jou nal o Inno a ion & Knowledge (JIK), ISSN 2444-569X, Else ie , Ams e dam,
Vol. 9, Iss. 3, pp. 1-18,
h ps://doi.o g/10.1016/j.jik.2024.100517
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Un eiling he dynamics o AI applica ions: A e iew o e iews using
scien ome ics and BERTopic modeling
Raghu Raman
a,
*, Debidu a Pa naik
b
, Lau ie Hughes
c
, P ema Nedungadi
d
a
Am i a School o Business, Am i a Vishwa Vidyapee ham, Am i apu i, Ke ala 690525, India
b
In e na ional Managemen Ins i u e, Bhubaneswa 751 003, India
c
School o Business and Law, Edi h Cowan Uni e si y, JO 2.331, 270 Joondalup D i e, Joondalup WA 6027, Aus alia
d
Am i a School o Compu ing, Am i a Vishwa Vidyapee ham, Am i apu i, Ke ala 690525, India
ARTICLE INFO
A icle His o y:
Recei ed 13 Ma ch 2024
Accep ed 15 July 2024
A ailable online 20 July 2024
ABSTRACT
In a wo ld ha has apidly ans o med h ough he ad en o a ificial in elligence (AI), ou sys ema ic
e iew, guided by he PRISMA p o ocol, in es iga es a decade o AI esea ch, e ealing insigh s in o i s e olu-
ion and impac . Ou s udy, examining 3,767 a icles, has d awn conside able a en ion, as e idenced by an
imp essi e 63,577 ci a ions, unde sco ing he schola ly communi y’s p o ound engagemen . Ou s udy
e eals a collabo a i e landscape wi h 18,189 con ibu ing au ho s, eflec ing a obus ne wo k o esea ch-
e s ad ancing AI and machine lea ning applica ions. Re iew ca ego ies ocus on sys ema ic e iews and bib-
liome ic analyses, indica ing an inc easing emphasis on comp ehensi e li e a u e syn hesis and
quan i a i e analysis. The findings also sugges an oppo uni y o explo e eme ging me hodologies such as
opic modeling and me a-analysis. We dissec he s a e o he a p esen ed in hese e iews, finding hemes
h oughou he b oad schola ly discou se h ough hema ic clus e ing and BERTopic modeling. Ca ego iza-
ion o s udy a icles ac oss fields o esea ch indica es dominance in In o ma ion and Compu ing Sciences, ol-
lowed by Biomedical and Clinical Sciences. Subjec ca ego ies e eal in e connec ed clus e s ac oss a ious
sec o s, no ably in heal hca e, enginee ing, business in elligence, and compu a ional echnologies. Seman ic
analysis ia BERTopic e ealed nine een clus e s mapped o hemes such as AI in heal h inno a ions, AI o sus-
ainable de elopmen , AI and deep lea ning, AI in educa ion, and e hical conside a ions. Fu u e esea ch di ec-
ions a e sugges ed, emphasizing he need o in e sec ional bias mi iga ion, holis ic heal h app oaches, AI’s
ole in en i onmen al sus ainabili y, and he e hical deploymen o gene a i e AI.
© 2024 The Au ho (s). Published by Else ie España, S.L.U. on behal o Jou nal o Inno a ion & Knowledge.
This is an open access a icle unde he CC BY-NC-ND license
(h p://c ea i ecommons.o g/licenses/by-nc-nd/4.0/)
Keywo ds:
Thema ic analysis
Coci a ion analysis
Topic modeling
BERTopic
A ificial in elligence
Sus ainable de elopmen goal
Cybe secu i y
Inno a ion
E hics
Blockchain
JEL classifica ion:
I21
L86
Z00
In oduc ion
The adop ion o a ificial in elligence (AI) wi hin indus y, heal h,
educa ion, and go e nmen has p o ound implica ions o humans a
he socie al le el (Dwi edi e al., 2021). Resea che s ha e de eloped
a subs an ial body o li e a u e co e ing a ple ho a o AI- ela ed
hemes ac oss nume ous applica ions, p o iding aluable insigh s
in o he impac o AI sys ems and applica ions. This apidly e ol ing
field has led o a significan numbe o e iew a icles ha dis ill
(Goodell e al., 2021), analyze (Pa naik e al., 2023), and syn hesize
(Pa naik e al., 2024) he as quan um o knowledge p oduced.
These e iews a e c ucial o unde s anding he dynamics o AI appli-
ca ions and hei ans o ma i e po en ial. The widesp ead impac o
AI in heal hca e, educa ion, ag icul u e, cybe secu i y, and go e n-
men has gene a ed a conside able olume o academic publica ions,
encompassing di e se gen es o AI adop ion ac oss mul iple sec o s
(Guo e al., 2020;Hinojo-Lucena e al., 2019). Resea che s ha e
endea o ed o dis ill key findings h ough hese e iew a icles,
esul ing in hema ic analyses ha highligh essen ial aspec s o AI
esea ch. These hema ic analyses p o ide a de ailed unde s anding
o how AI applica ions influence a ious domains; e eal ends, chal-
lenges, and oppo uni ies; and shape u u e esea ch di ec ions. As
he field con inues o g ow, he syn hesis o hese di e se insigh s
h ough a me a- e iew app oach becomes inc easingly impo an . By
employing scien ome ics and ad anced opic modeling echniques
such as BERTopic, we can un eil he comp ehensi e dynamics o AI
applica ions, o e ing a holis ic pe spec i e ha guides bo h cu en
unde s anding and u u e schola ly e o s (R. Raman e al., 2024).
Fo ins ance, ex ensi e esea ch ad ancemen s in na u al lan-
guage p ocessing (NLP) sys ems ha e been demons a ed by K ei-
meye e al. (2017) and Bannach-B own e al. (2019), demons a ing
he dep h o AI applica ions in language echnology. These s udies
* Co esponding au ho .
E-mail add ess: aghu@am i a.edu (R. Raman).
h ps://doi.o g/10.1016/j.jik.2024.100517
2444-569X/© 2024 The Au ho (s). Published by Else ie España, S.L.U. on behal o Jou nal o Inno a ion & Knowledge. This is an open access a icle unde he CC BY-NC-ND license
(h p://c ea i ecommons.o g/licenses/by-nc-nd/4.0/)
Jou nal o Inno a ion & Knowledge 9 (2024) 100517
Jou nal o Inno a ion
&Knowledge
h ps://www.jou nals.else ie .com/jou nal-o -inno a ion-and-knowledge
highligh how NLP sys ems ha e e ol ed o unde s and and gene a e
human language wi h inc easing accu acy, enabling applica ions in
a eas such as au oma ed ansla ion, sen imen analysis, and con e -
sa ional agen s. G oundb eaking insigh s in o AI applica ions in med-
ical fields a e syn hesized by Roy e al. (2019) and
Eb ahimighahna ieh e al. (2020), who highligh he po en ial o AI
in neu ological esea ch and medical ad ancemen s. These e iews
e eal how AI echnologies can aid in diagnosing neu ological diso -
de s, pe sonalizing ea men plans, and imp o ing pa ien ou -
comes. Simila ly, Liu e al. (2019) and B inke e al. (2018) analyzed
he con e gence o AI and heal hca e, e ealing significan in e disci-
plina y con ibu ions ha span om p edic i e analy ics in pa ien
ca e o he op imiza ion o hospi al ope a ions.
In addi ion o heal hca e, he ans o ma i e po en ial o AI in
educa ion was explo ed by Tahi u (2021) and Xu and Ouyang (2022),
who illus a ed how AI eshapes lea ning en i onmen s. Thei s udies
discuss he in eg a ion o AI in pe sonalized lea ning sys ems, in elli-
gen u o ing, and au oma ed g ading, which enhance educa ional
expe iences and ou comes. The applica ion o AI has also ex ended o
sus ainable de elopmen , wi h in ensi e use o enewable ene gy
and ag icul u e, as discussed by Mosa i e al. (2018) and Ca alho
e al. (2019). These wo ks demons a e how AI-d i en solu ions, such
as p ecision a ming and ene gy managemen sys ems, con ibu e o
sus ainabili y by op imizing esou ce use and educing en i onmen-
al impac . Con e sely, he ole o AI in so wa e quali y assu ance is
de ailed by Li e al. (2020) and Meiliana e al. (2017), who del e in o
so wa e de ec p edic ion and he complexi ies o so wa e de elop-
men . Thei e iews highligh how AI echniques imp o e so wa e
eliabili y and e ficiency by p edic ing and mi iga ing po en ial
de ec s du ing he de elopmen li ecycle.
Fu he mo e, con ibu ions o in elligen anspo sys ems a e
unde sco ed by Si ohi e al. (2020) and Noaeen e al. (2022), who
emphasize AI’s po en ial o enhance a fic sa e y. These s udies
explo e how AI echnologies, such as au onomous ehicles and sma
a fic managemen sys ems, educe acciden s and imp o e a fic
flow. Mo eo e , he mode n applicabili y o AI in u ban adminis a-
ion and planning is illus a ed by Di Vaio e al. (2020) and Da ko
e al. (2020), who elucida e i s mul i ace ed impac s on sma ci ies.
Thei analyses e ealed how AI can op imize u ban se ices, enhance
public sa e y, and imp o e he quali y o li e o ci y inhabi an s
h ough in elligen in as uc u e and da a-d i en decision-making.
Mo eo e , he in e sec ion o AI wi h blockchain echnology was
desc ibed by Kuma e al. (2023) and Ek ami a d e al. (2020), e eal-
ing he syne gies be ween hese e olu iona y echnologies. Thei
s udies discuss how AI enhances blockchain capabili ies in secu e
da a ansac ions and decen alized applica ions, while blockchain
p o ides obus amewo ks o AI da a in eg i y and p o enance. AI-
powe ed emo ional in elligence was explo ed in “deep lea ning o
emo ion analysis”by Bouwmans e al. (2018) and Canedo and Ne es
(2019). These e iews del e in o he nuances o emo ion iden ifica-
ion and i s applica ions in a eas such as men al heal h moni o ing,
cus ome se ice, and human-compu e in e ac ion. Addi ionally,
deep lea ning applica ions in ecommenda ions and molecula sci-
ence a e discussed by Po ugal e al. (2018),Mu ad e al. (2018),Ma -
inelli (2022), and Wu e al. (2022). Thei findings highligh how
deep lea ning algo i hms enhance ecommenda ion sys ems by pe -
sonalizing con en deli e y and accele a ing disco e ies in molecula
science h ough p edic i e modeling o molecula in e ac ions.
The ole o AI in edge compu ing and cybe secu i y was analyzed
by Ma ins e al. (2020) and Manzoo e al. (2019). These s udies
show how AI imp o es he e ficiency and secu i y o dis ibu ed com-
pu ing sys ems by enabling eal- ime da a p ocessing and h ea
de ec ion a he edge o he ne wo k. The e hical dimensions o AI in
heal hca e a e highligh ed by Milne-I es e al. (2020) and Loh e al.
(2022), who p o ide in aluable insigh s in o he mo al issues su -
ounding AI-d i en heal hca e applica ions. They discuss conce ns
such as pa ien p i acy, algo i hmic bias, and he need o anspa en
and accoun able AI sys ems. Fu he mo e, in elligen diagnos ics
we e examined by Ch is odoulou e al. (2019) and Fleu en e al.
(2020), who explo ed he syn hesis o machine lea ning models o
diagnos ic accu acy. Finally, he challenge o mi iga ing bias in AI
decision-making was explo ed by Sun e al. (2019) and Pagano e al.
(2023), who add essed c i ical issues o ai ness and accoun abili y in
AI sys ems by p oposing s a egies o iden i ying and educing
biases in AI algo i hms.
Despi e he ex ensi e co e age o AI applica ions h ough indi id-
ual e iew a icles, he e emains a significan oppo uni y o syn he-
size hese di e se insigh s in o a comp ehensi e o e iew. The
concep o a “ e iew o e iews”o me a- e iew has been e ec i ely
employed in o he disciplines o consolida e findings, iden i y
esea ch gaps, and p opose new di ec ions. Fo example, Sch yen and
Spe ling (2023) conduc ed a me a- e iew o ope a ions esea ch,
highligh ing p edominan ends and unde explo ed a eas in 709
e iews published be ween 2011 and 2020. Thei analysis e ealed a
ocus on scoping and selec i e e iews, emphasizing he impo ance
o sys ema ically o ganizing and syn hesizing exis ing knowledge.
Simila ly, Mo o e al. (2023) p o ided an umb ella e iew o p oduc -
se ice sys ems, o e ing a pano amic o e iew ha iden ified well-
esea ched a eas and hose s ill equi ing u he explo a ion. In
ano he ins ance, Sadeghi-Nia aki (2023) analyzed con empo a y IoT
e iews, pinpoin ing c i ical challenges and oppo uni ies wi hin he
field. Fu he mo e, Risso e al. (2023) employed a sys ema ic li e a-
u e e iew o ex end discussions in 103 e iew pape s on blockchain
echnology in supply chain managemen , p o iding mul i ace ed
insigh s and iden i ying u u e esea ch di ec ions.
The high engagemen and success o me a- e iews in a ious
fields unde sco e he alue o his esea ch app oach. Howe e , a
simila comp ehensi e s udy o AI emains la gely un apped, p e-
sen ing an oppo uni y o syn hesize and gene a e no el findings
om he ex ensi e body o AI esea ch. The significan co pus o AI
publica ions and e iews explo ing i s a ious applica ions sugges s
ha syn hesizing seman ic clus e s om collec i e pe spec i es on AI
could e ec i ely di ec u u e schola ly e o s (Pa naik e al., 2024).
This esea ch aims o fill his exis ing gap by o e ing no el insigh s
in o AI h ough hema ic clus e ing o subjec ca ego ies and employ-
ing he BERTopic modeling app oach (G oo endo s , 2022). By
le e aging hese ad anced echniques, we can un eil he comp ehen-
si e dynamics o AI applica ions, p o iding a holis ic pe spec i e ha
enhances cu en unde s anding and guides u u e esea ch and
de elopmen in his apidly e ol ing field. We p opose he ollowing
esea ch ques ions:
RQ1: Wha a e he key domains and applica ions cu en ly being
ans o med by AI, and how is his ans o ma ion cha ac e ized?
RQ2: Can he p oposed modeling echnique de elop no el insigh
in o he global ans o ma i e impac o AI ac oss di e se domains?
RQ3: Wha a e he po en ial u u e di ec ions and inno a ions?
By add essing hese esea ch ques ions, his s udy un eils he
dynamics o AI applica ions h ough a comp ehensi e e iew o
e iews employing scien ome ics and ad anced bibliome ic model-
ing. Ou in es iga ion e eals he ans o ma i e impac o AI ac oss
key domains, including heal hca e, enginee ing, en i onmen al sus-
ainabili y, business, and human-compu e in e ac ion. Specifically,
AI ad ancemen s in heal hca e ha e e olu ionized diagnos ics and
pe sonalized medicine, while i s con ibu ions o enginee ing and
en i onmen al applica ions ha e p omo ed sus ainabili y and sma
in as uc u e. In business, AI enhances decision suppo sys ems and
ope a ional e ficiency, and i s influence on digi al in as uc u e
imp o es human-compu e in e ac ions. Addi ionally, ou opic
modeling analysis p o ides no el insigh s in o he b oad applicabili y
o AI, highligh ing i s ole in deep lea ning echnologies, educa ion,
indus y, blockchain, cybe secu i y, and e hical conside a ions. These
findings no only answe c i ical esea ch ques ions bu also iden i y
R. Raman, D. Pa naik, L. Hughes e al. Jou nal o Inno a ion & Knowledge 9 (2024) 100517
2
u u e di ec ions and inno a ions, se ing a new benchma k o li e -
a u e e iews in apidly e ol ing scien ific domains.
In he emaining sec ions o he s udy, Sec ion 2 discusses he
s udy me hods, de ailing he sys ema ic app oach and ools used o
da a collec ion and analysis. Sec ion 3 highligh s he key esul s, p e-
sen ing he majo findings om ou scien ome ic and opic model-
ing analyses. In Sec ion 4, we o e a ho ough discussion o he
esul s, emphasizing he implica ions o ou findings o bo h p ac ice
and heo y and sugges ing po en ial a enues o u u e esea ch.
Finally, in Sec ion 5, we conclude he wo k by summa izing he
s udy’s con ibu ions and eflec ing on i s significance in ad ancing
he unde s anding o AI applica ions.
Me hods
The p oposed esea ch me hods o e a unique con ibu ion o he
analysis o a ificial in elligence (AI) esea ch by combining he sys-
ema ic igo o he P e e ed Repo ing I ems o Sys ema ic Re iews
and Me a-Analyses (PRISMA) p o ocol o da a collec ion, he in e -
disciplina y insigh o All Science Jou nals Classifica ion (AJSC) sub-
jec ca ego ies o hema ic clus e ing, and he ad anced seman ic
analysis capabili ies o BERTopic modeling. As illus a ed in Fig. 1,
his me hodology ensu es a comp ehensi e and bias-minimized
da ase , unco e s c oss-disciplina y hema ic clus e s h ough a
s uc u ed classifica ion sys em, and ex ac s nuanced eme ging
esea ch hemes by le e aging s a e-o - he-a na u al language
p ocessing echniques. Toge he , hese elemen s cons i u e a no el
app oach ha enhances he dep h, accu acy, and ele ance o AI
esea ch analysis, se ing a new benchma k o conduc ing li e a u e
e iews in apidly e ol ing scien ific domains.
PRISMA
To ensu e a sys ema ic and anspa en app oach in conduc ing
his me a- e iew, we adhe ed o he PRISMA guidelines (Mohe
e al., 2009). The PRISMA p o ocol p o ides a s uc u ed amewo k
o iden i ying, selec ing, and c i ically app aising ele an s udies, as
well as o collec ing and analyzing da a om hese s udies. This p o-
ocol enhances he igo and ep oducibili y o sys ema ic e iews by
p o iding a s anda dized epo ing me hodology. Following hese
guidelines, we sys ema ically collec ed a icles om he Scopus da a-
base on 23 d Janua y 2024 (Page e al., 2021;Rama e al., 2023;
Raman e al., 2022). Scopus was chosen due o i s comp ehensi e
co e age and high-quali y indexing o pee - e iewed li e a u e. The
s udy pe iod spans om 2014 o 2023, wi h he sea ch e ms “((a ifi-
cial in elligence”OR “AI”OR “machine lea ning”OR “deep lea ning”OR
“neu al ne wo k*”OR “supe ised lea ning”OR “unsupe ised lea ning”
OR “ ein o cemen lea ning”OR “na u al language p ocessing”OR “NLP”
OR “compu e ision”OR “cogni i e compu ing”) AND (biblio* OR scien-
ome* OR “li e a u e e iew”OR “sys ema ic e iew”). The documen
ypes included we e a icles, con e ence pape s, e iews, and book
chap e s, leading o a final da ase o 3767 a icles o analysis.
Pe o mance analysis
In ou scien ome ic analysis, we used a comp ehensi e se o
me ics o sc u inize he pe o mance, collabo a ion dynamics, and
impac o schola ly publica ions, as epo ed in p e ious e iews
(Kokol e al., 2021;Pa naik e al., 2021,2023,2024). The app oach
unde aken o he design o his esea ch ocuses on a ange o indi-
ca o s o ully explo e e iews on he applica ions o AI. The me ic
o al e iews (TRs) showcased ou o e all esea ch. By dis inguishing
e iews ha a e solo-au ho ed (SA) and coau ho ed (CA), we iden i y
he collabo a ion pa e ns ha a e c ucial o knowledge dissemina-
ion (Bake e al., 2020). While CA places mo e emphasis on eam-
wo k, SA eflec s indi idual con ibu ions. The le el o collabo a ion
e iden in he o me e iews is u he in es iga ed by analyzing he
numbe o con ibu ing au ho s (NCA), which cap u es he ange o
ne wo ks and academic in ol emen . Fu he mo e, he a e age num-
be o au ho s pe coau ho ed a icle (AACA), collabo a ion index (CI),
and collabo a ion coe ficien (CC) p o ide nuanced insigh s in o he
shi ing in ensi y and pa e ns o collabo a ion o e ime (Don hu
e al., 2021).
In addi ion o e iewing he numbe o a icles, we analyzed ci a-
ions o assess he impac o e iew a icles. The numbe o ci ed
e iews (NCR) indica es he numbe o e iew a icles equen ly
e e ed o, while o al ci a ions (TCs) p o ide an o e iew o he
o e all impac . To s anda dize o a ia ions in publica ion age, we
calcula e a e age ci a ions pe ci ed e iew (TC/CR), which o e s an
adjus ed impac measu e. Con e sely, a ious indices, including he
h-index, g-index, and i-index, a e u ilized o deepen ou unde s and-
ing o ci a ion influence. The h-index ocuses on highly ci ed e iews,
ep esen ing he numbe o e iews wi h a leas h ci a ions. The g-
index emphasizes p oduc i i y by indica ing he numbe o op-ci ed
e iews wi h a leas g2 ci a ions. Simul aneously, he i-indices (i-10,
i-100, and i-200) e eal he numbe o e iews ci ed a leas 10, 100,
and 200 imes, espec i ely. Addi ional me ics such as he numbe
o ac i e yea s (NAY) e eal he du a ion o e iew publica ions in AI
applica ions. Combining his wi h p oduc i i y pe ac i e yea (PAY)
gi es us a be e sense o sus ained schola ly ou pu o e ime.
Thema ic clus e ing
The All Science Jou nals Classifica ion (ASJC) subjec ca ego ies, a
classifica ion sys em used by Scopus o indexing sou ce i les wi hin
a s uc u ed hie a chy spanning a ious disciplines and subdisci-
plines, se es as he amewo k o c oss-disciplina y hema ic analy-
sis (Haddawy e al., 2017;Hassan e al., 2017). We employed
VOS iewe , a so wa e applica ion c a ed o cons uc ing and isu-
alizing bibliog aphic ne wo ks ( an Eck & Wal man, 2010). The basis
o ou analysis lies in he co-occu ence o ASJC subjec ca ego ies o
each publica ion. Each node ep esen s a dis inc ASJC publica ion,
wi h lines connec ing nodes indica ing he equency o co-occu -
ence. The colo o a node o en deno es he clus e o g oup o which
a publica ion belongs, and each colo signifies a di e en hema ic
clus e (Goodell e al., 2021;R. Raman e al., 2024). This clus e ing is
g ounded in he simila i y o co-occu ence pa e ns, implying e-
quen discussion oge he in he li e a u e. The dis ance be ween
nodes in he isualiza ion is also significan ; a sho e dis ance indi-
ca es a s onge o mo e equen co-occu ence, sugges ing close
ela ionships o a highe deg ee o opic ele ance.
Topic modeling
Al hough a numbe o opic modeling app oaches ha e been u i-
lized wi hin he li e a u e, s udies ha ha e de eloped an empi ical
analysis o a ious app oaches, such as nonnega i e ma ix ac o iza-
ion (NMF), To2Vec, and la en Di ichle alloca ion (LDA), ha e iden i-
fied BERTopic as “being able o gene a e no el insigh s using i s
embedding app oach”(Egge & Yu, 2022). A i s co e, BERTopic is a
modeling echnique ha le e ages he powe ul con ex ual embed-
dings wi hin BERT and he class-based e m equency-in e se docu-
men equency (c-TF-IDF) algo i hm o compa e he impo ance o
e ms wi hin a dense clus e and de elop e m ep esen a ion
(S
anchezF anco & ReyMo eno 2022). Pos da a ex ac ion, a ho -
ough p ep ocessing s ep in ol ing ex -cleaning p ocedu es, NLP
echniques, and okeniza ion, enhanced he quali y and uni o mi y o
he da ase . The u iliza ion o sen ence embeddings gene a ed using
he “all-mpne -base- 2”model om he Sen ence T ans o me and
dimensionali y educ ion using uni o m mani old app oxima ion and
p ojec ion (UMAP) u he gene a ed he da ase o meaning ul opic
ex ac ion and isualiza ion (McInnes e al., 2020). The BERTopic
R. Raman, D. Pa naik, L. Hughes e al. Jou nal o Inno a ion & Knowledge 9 (2024) 100517
3
Fig. 1. Resea ch amewo k.
R. Raman, D. Pa naik, L. Hughes e al. Jou nal o Inno a ion & Knowledge 9 (2024) 100517
4
model was fi ed o he p ep ocessed ex con en , ex ac ing dis inc
opics and co esponding p obabili ies o each a icle wi hin a opic.
The gene a ed opics we e sc u inized o cohe ence, and he dis i-
bu ion o a icles ac oss opics was examined, p o iding insigh s in o
he deg ee o associa ion be ween a icles and iden ified opics. This
comp ehensi e and ad anced app oach in ou opic modeling analy-
sis ensu es he eliabili y and obus ness o ou s udy findings.
Resul s
Pe o mance analysis
As shown in Table 1, ou s udy ho oughly examined he 3767
e iews ha we e ci ed 63,577 imes. Such s a is ics unde sco e he
ex ensi e schola ly engagemen and influence wi hin he specialized
field o esea ch. The high h-index o 109 and g-index o 176 sugges
a subs an ial impac , emphasizing he significance and ele ance o
he o me e iews.
Mo ing o he coau ho ship insigh s in Panel B, ou s udy un eils
a collabo a i e landscape wi h 18,189 con ibu ing au ho s, eflec ing
a obus ne wo k o esea che s engaged in syn hesizing s a e-o - he-
a esea ch on AI applica ions. A collabo a ion index o 3.83 indica es
a conside able deg ee o eamwo k, os e ing a ich en i onmen o
knowledge exchange c ea ed and dissemina ed h ough e iews.
Addi ionally, he da a illus a e ha he a e age numbe o au ho s
pe coau ho ed a icle is 5, emphasizing he collec i e e o in p o-
ducing he e iewed con en . This collabo a i e spi i likely con ib-
u es o he field’s di e si y and dep h o pe spec i es. In Panel C, he
pape ca ego izes he ypes o e iews on AI applica ions, e ealing a
p edominan ocus on sys ema ic e iews and bibliome ic analyses,
wi h 2707 and 651 ins ances, espec i ely. This signifies a me hodo-
logical inclina ion owa d he comp ehensi e syn hesis o li e a u e.
Mo eo e , including o he e iew ypes, such as opic modeling and
me a-analysis, showcases he me hodological di e si y in unde -
s anding he dynamics o AI applica ions. O e all, he in e en ial sum-
ma y highligh s a mul idimensional explo a ion o he li e a u e,
encompassing collabo a ion pa e ns, impac me ics, and me hodo-
logical app oaches wi hin he AI domain.
The findings in Table 1 a e u he ex ended in Fig. 2, which maps
he e olu ion o he a ious o ms o e iews be ween 2014 and
2023. The subs an ial inc ease in sys ema ic e iews (SLRs) o e he
yea s, eaching 1061 in 2023, ein o ces ou p e ious finding o a sus-
ained in e es in comp ehensi e li e a u e syn hesis. Scien ome ic
e iews also gained p ominence, eaching 281, indica ing a g owing
ocus on quan i a i e analysis wi hin he domain.
Building on hese insigh s, we del e mo e deeply in o specific
aspec s o AI esea ch in he subsequen subsec ions. We explo e he
fields o esea ch analysis o iden i y key a eas o ocus and eme ging
ends. This will be ollowed by an examina ion o hema ic clus e s,
p o iding a de ailed discussion on he majo hemes and opics ha
ha e shaped he AI esea ch landscape. Addi ionally, we analyze
BERT-enabled opics o unco e nuanced pa e ns and insigh s
de i ed om ad anced na u al language p ocessing echniques.
Toge he , hese discussions aim o p o ide a holis ic unde s anding
o he cu en s a e and u u e di ec ions o AI esea ch.
Fields o esea ch analysis
The Scopus da abase u he uses he Aus alian and New Zealand
S anda d Classifica ion o Occupa ions (ANZSCO, 2013) o ca ego ize
publica ions in o fields o esea ch (FoRs). Fig. 3 ca ego izes he s udy
a icles unde he field o esea ch (FoR). Mos no ably, in o ma ion
and compu ing sciences domina e he landscape ega ding o al
e iews and ci a ions, unde sco ing hei pi o al ole in he syn hesis
o AI esea ch. This end is no su p ising gi en he echnical na u e
o AI. The e o e, biomedical and clinical sciences and heal h sciences
ha e p esen ed a significan olume o AI e iews, which indica es
he g owing impo ance o AI applica ions in hese fields. The high
ci a ion coun s in hese a eas sugges ha he e iews gene a ed a e
p olific and impac ul, influencing u he s udies and de elopmen s.
Al hough ha ing ewe publica ions in compa ison, o he fields, such
as enginee ing, comme ce, managemen , ou ism, se ices, and edu-
ca ion, s ill ha e a no able p esence, eflec ing AI echnologies’in e -
disciplina y and wide- eaching impac . Fields such as buil
en i onmen and design, biological sciences, psychology, ma hema i-
cal sciences, and human socie y, while con ibu ing less in numbe ,
e eal he di e se applica ion o AI ac oss a b oad spec um o
esea ch a eas. This di e si y in applica ion a eas highligh s he e -
sa ili y and b oad applicabili y o AI echnologies in a ious aspec s
o scien ific and echnological esea ch.
Thema ic clus e s
The FoRs based on he ANZSRC classifica ion sys em e eal he
dis ibu ion o AI ac oss b oad academic and esea ch disciplines. In
con as , subjec ca ego ies based on he ASJC sys em e ealed mo e
g anula in e connec ions ia ou dis inc clus e s wi hin AI esea ch
(Fig. 4).
Clus e 1 ( ed): AI in heal hca e and li e sciences in o ma ics
This clus e is cha ac e ized by he in eg a ion o AI wi h heal h-
ca e and li e sciences o enhance da a managemen , diagnos ics, and
ea men p o ocols. Key e ms such as “heal h in o ma ics”and
“heal h in o ma ion managemen ”sugges he applica ion o AI in
o ganizing and analyzing heal h da a. “Biomedical enginee ing”and
“bioenginee ing”poin o he design o AI-d i en medical de ices
Table 1
O e iew.
Panel A. Desc ip i e s a is ics
To al e iews (TR) 3767
Numbe o ci ed e iews (NCR) 2748
To al ci a ions (TC) 63,577
A e age ci a ions (TC/TR) 23
h-index 109
g-index 176
i-10 1190
i-100 129
i-250 28
i-500 9
Numbe o ac i e yea s (NAY) 10
P oduc i i y pe ac i e yea (PAY) 377
Panel B. Coau ho ship in o ma ion
Numbe o con ibu ing au ho s (NCA) 18,189
Numbe o a filia ed au ho s (excludes epe i ions) (NAA) 15,677
Au ho s o single-au ho ed documen s (ASA) 121
Au ho s o coau ho ed documen s (ACA) 15,568
Single-au ho ed documen s (SA) 124
Coau ho ed documen s (CA) 3643
Collabo a ion index (CI) 3.83
Collabo a ion coe ficien (CC) 0.79
A e age au ho s pe coau ho ed a icle 5
Panel C. Type o e iew
Sys ema ic e iew/sys ema ic li e a u e e iew (SLR) o he han Sci-
en ome ics/Bibliome ics
2707
Scien ome ic/Bibliome ic e iew 651
O he o m o e iew 409
Topic model 48
SLR and opic model 19
Scien ome ic/Bibliome ic and opic model 20
O he o m o e iew and opic model 9
Me a-analysis 4
SLR and me a-analysis 4
Scien ome ic/Bibliome ic and me a-analysis −
O he o m o e iew and me a-analysis −
No e: This able p esen s an o e iew o he s udy a icles. Ci a ions epo ed we e
confined o he sea ch da e.
R. Raman, D. Pa naik, L. Hughes e al. Jou nal o Inno a ion & Knowledge 9 (2024) 100517
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and sys ems. Wi h “neu ology”and “oncology,” he e is an implica-
ion o AI in specialized medical esea ch and ea men planning,
possibly using ML echniques o pa e n ecogni ion in disease diag-
nosis. “Cogni i e neu oscience”and “psychia y and men al heal h”
indica e he explo a ion o AI in unde s anding and ea ing neu o-
logical and men al heal h condi ions. This clus e also likely includes
he use o AI o genomic sequencing and pe sonalized medicine, as
sugges ed by “molecula biology”and “biochemis y.”Table 2 shows
some o he no able wo ks cons i u ing he clus e .
Significan con ibu ions o he field o AI in heal hca e we e
made h ough a sys ema ic e iew by Xiaoe al. (2018), who ocused
on deep lea ning applica ions in elec onic heal h eco d (EHR) da a.
Thei esea ch, conduc ed be ween 2010 and 2018, in ol ed a ho -
ough analysis o 98 a icles ocusing on he use o deep lea ning in
Fig. 2. Tempo al e olu ion o he ypes o e iews on AI applica ions.
Fig. 3. Classifica ion o AI e iews based on FoRs (ANZSRC 2020 code).
R. Raman, D. Pa naik, L. Hughes e al. Jou nal o Inno a ion & Knowledge 9 (2024) 100517
6
heal hca e in o ma ics. This s udy is pi o al o unde s anding how
deep lea ning a chi ec u es can be e ec i ely applied o a ious ypes
o heal h da a, add essing c i ical asks such as disease de ec ion and
he p edic ion o clinical e en s. The au ho s no ed deep lea ning’s
supe io i y in handling aw da a, which aligns wi h he ongoing shi
owa d mo e da a-d i en app oaches in heal hca e in o ma ics. How-
e e , he pape also del es in o he challenges inhe en in his field,
such as he need o imp o ed da a quali y and he complexi ies o
model in e p e abili y in a heal hca e con ex . These issues a e c u-
cial conside ing he sensi i e na u e o heal h da a and he need o
eliable and unde s andable AI sys ems in medical se ings. Mo e-
o e , he discussion on he di ficul ies in in eg a ing deep lea ning
models wi h exis ing EHR sys ems eflec s a significan challenge in
he b oade heme o AI in heal hca e in o ma ics.
Con e as and Vehi (2018) explo ed he in eg a ion o AI wi h
mode n echnologies such as medical de ices, mobile compu ing,
and senso s o enhance diabe es managemen , a c i ical issue in he
clus e heme o AI in heal hca e and li e sciences in o ma ics. Thei
comp ehensi e e iew, which analyzed 141 a icles om 2010 o
2018, ocused on using AI o manage diabe es and i s complica ions.
This pape highligh s he de elopmen o AI-d i en ools o p edic-
ion and p e en ion in diabe es ca e, emphasizing how hese
ad ancemen s can imp o e pa ien quali y o li e. Thei findings
e eal a significan shi owa d da a-d i en me hods in diabe es
managemen , unde sco ing he po en ial o AI in ailo ing ea men
o indi idual needs and in le e aging la ge da ase s o imp o ed
managemen s a egies. They also no ed he g owing esea ch in
closed-loop sys ems and blood glucose (BG) p edic ion models,
eflec ing he dynamic e olu ion o AI applica ions in his field. They
emphasized he impo ance o con inuing esea ch in AI o diabe es
managemen , pa icula ly in enhancing he sa e y o au oma ed pan-
c eas (AP) sys ems and open-loop ools. The pape also add esses he
e hical conside a ions o using AI in heal hca e, including he isks
associa ed wi h pe sonal da a elease and he po en ial o disc imi-
na ion.
A sys ema ic e iew conduc ed by Yassin e al. (2018) ocused on
he analysis o compu e -aided diagnosis/de ec ion (CAD) sys ems
applicable o b eas cance . Thei s udy, which analyzed 154 selec ed
academic a icles, del ed in o he cu en s a e and ad ancemen s o
CAD sys ems, especially in hei applica ion o b eas cance de ec-
ion. This pape highligh s he inc easing eliance on machine lea n-
ing echnologies, such as SVM classifie s, o b eas issue
classifica ion and no es he e ec i eness o hese AI me hods in sup-
po ing medical expe s. This e iew also discusses he p ac ical chal-
lenges and conside a ions in implemen ing CAD sys ems in clinical
se ings, including issues ela ed o alse posi i es, cos s, and he
Fig. 4. Clus e ing o c oss-disciplina y subjec s o AI esea ch.
Table 2
Highly ci ed a icles ep esen ing clus e 1.
To al Ci a ions Au ho (s) Ti le Subjec Ca ego y (ASJC)
380 Xiao e al. (2018) “Oppo uni ies and challenges in de eloping deep lea ning mod-
els using elec onic heal h eco ds da a: A sys ema ic e iew”
Heal h In o ma ics
257 Con e as and Vehi (2018) “AI o diabe es managemen and decision suppo : Li e a u e
e iew”
Heal h In o ma ics
239 Yassin e al.(2018) “Machine lea ning echniques o b eas cance compu e aided
diagnosis using di e en image modali ies: A sys ema ic
e iew”
Compu e Science Applica ions| So wa e| Heal h In o ma ics
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7
necessi y o p ope aining. The au ho s ad oca e o he in eg a-
ion o CAD sys ems in o clinical p ac ice, emphasizing ha o wide-
sp ead adop ion, CAD sys ems mus be ime-e ficien , cos -e ec i e,
and demons ably imp o e physician pe o mance. In he u u e, he
au ho s ecommend he de elopmen o s anda dized public image
da abases ha include di e se modali ies and e en gene ic da a o
enhance he accu acy and eliabili y o CAD sys ems. They also iden i-
fied deep lea ning and swa m in elligence as p omising a eas o
u u e esea ch in CAD sys em de elopmen . This pape concludes by
unde sco ing he impo ance o inco po a ing mul iple imaging
modali ies and ad anced echnologies such as 3D mammog aphy o
imp o e he e ficiency and e ficacy o CAD sys ems in b eas cance
de ec ion.
Clus e 2 (g een): AI in enginee ing sys ems and sus ainable echnologies
AI is seen as a ca alys o inno a ion ac oss a ious enginee ing
fields. “Compu e science applica ions”and “in o ma ion sys ems”
e e o AI’s ole in op imizing sys em ope a ions and da a p ocessing.
The e ms “elec ical and elec onic enginee ing”and “ins umen a-
ion”sugges de eloping sma senso s and con ols ha le e age AI
o imp o ed e ficiency and au oma ion. “Renewable ene gy, sus ain-
abili y, and he en i onmen ,”alongside “ene gy enginee ing and
powe echnology,”likely in ol e AI in sma g ids, ene gy consump-
ion p edic ion, and he op imiza ion o enewable ene gy sou ces.
The con ibu ion o AI o “sa e y, isk, eliabili y and quali y”implies
he use o p edic i e analy ics and ML o isk assessmen and quali y
con ol in enginee ing p ojec s. This clus e may also encompass he
de elopmen o AI ools o en i onmen al moni o ing and sus ain-
able u ban planning, as indica ed by “geog aphy, planning, and
de elopmen .”Table 3 p esen s some o he no able wo ks ep esen -
ing he clus e .
A sys ema ic e iew by Pa ício & Riede (2018) highligh ed he
in e sec ion o AI in enginee ing sys ems and sus ainable echnology,
specifically ocusing on he use o compu e ision and AI in p ecision
ag icul u e. Thei s udy, cen e ing on he fi e mos p oduced g ains
globally (maize, ice, whea , soybean, and ba ley), analyzed 25 pape s
om he pas fi e yea s. This e iew showcased a ious applica ions
o compu e ision in ag icul u e, such as disease de ec ion, g ain
quali y assessmen , and pheno yping. They emphasized he po en ial
o le e aging GPUs and ad anced AI echniques such as deep belie
ne wo ks o enhancing compu e ision me hods in ag icul u e.
Addi ionally, he s udy iden ified gaps in he de elopmen o in elli-
gen de ices ha in eg a e compu e ision wi h ag icul u al
machine y and d ones. The au ho s sugges ed ha he expansion o
GPUs and AI could benefi he classifica ion o glu en-con aining
g ains such as whea , oa s, and ba ley.
Simila ly, Sha ma e al. (2020) conduc ed a s udy c ucial o he
heme o AI in enginee ing sys ems and sus ainable echnology, pa -
icula ly ocusing on he use o ML in ag icul u al supply chains
(ASCs). Thei sys ema ic e iew, which encompassed 93 esea ch
pape s, explo ed he di e se applica ions o ML algo i hms ac oss
a ious phases o ASCs. This s udy emphasized he ole o ML in
enhancing ag icul u al sus ainabili y by add essing key challenges
such as p oduc i i y, wa e conse a ion, and soil heal h. A significan
con ibu ion o his wo k is he de elopmen o an ML applica ion
amewo k o sus ainable ASCs designed o guide eal- ime, da a-
d i en decision-making in ASCs. This amewo k aims o p o ide
ac ionable insigh s o esea che s, p ac i ione s, and policymake s o
manage ASCs e ec i ely, he eby imp o ing ag icul u al p oduc i i y
and sus ainabili y. This e iew unde sco es he as po en ial o ML
in ASCs, highligh ing i s e ec i eness in making p edic i e classifica-
ions and imp o ing he o e all e ficiency o ASC ope a ions. The
au ho s also discussed how ML-d i en echnologies could enhance
a m p oduc i i y and p ofi abili y h ough he analysis o da a om
senso s and d ones. Fu he mo e, hey no ed ha in eg a ing ML
da a wi h o he echnologies such as blockchain could imp o e sup-
ply chain isibili y, anspa ency, and aceabili y. Howe e , he
s udy also iden ified a eas needing u he in es iga ion, such as he
comp ehensi e managemen o da a ac oss ASC phases and he mea-
su able impac o ML on ASC isibili y.
Fu he mo e, Mosa i e al. (2019) in es iga ed he ole o ML in
enginee ing sys ems and sus ainable echnology, specifically in
ene gy sys em modeling, design, and p edic ion. Thei pape p esen s
an ex ensi e e iew and a no el axonomy o ML models used in
ene gy sys ems. This s udy iden ifies and classifies ML models based
on echnique, ene gy ype, and applica ion a ea, p o iding a comp e-
hensi e assessmen o hei pe o mance and discussing challenges
and u u e esea ch oppo uni ies. A key finding o hei esea ch is
he ema kable imp o emen in he accu acy, obus ness, p ecision,
and gene aliza ion abili ies o ML models, especially h ough hyb id-
iza ion. These hyb id ML models ha e shown significan e ec i eness
in enewable ene gy sys em applica ions, such as sola and wind
ene gy, con ibu ing o ene gy e ficiency, go e nance, and sus ain-
abili y. The s udy also highligh s he in eg a ion o ML wi h sma
senso s, sma g ids, and IoT echnologies, acili a ing he use o big
da a o in o med decision-making and enhancing model e ficiency.
The pape concludes ha no el hyb id ML models ou pe o m con-
en ional models, sugges ing a con inuing end owa d mo e
ad anced hyb id models o sophis ica ed ene gy sys em applica-
ions. This emphasis on hyb id models aligns wi h he inc easing
need o accu a e and e ficien enewable ene gy sys ems, conside -
ing hei en i onmen al dependency and he challenges in g id man-
agemen and powe gene a ion o ecas ing.
Clus e 3 (blue): AI-enhanced business in elligence and s a egic
managemen
The ocus o his clus e is on he applica ion o AI o imp o e
business in elligence, s a egic decision-making, and ope a ional e fi-
ciency. “In o ma ion sys ems and managemen ”and “gene al com-
pu e science”sugges using AI o da a-d i en decision suppo
sys ems. Wi h “s a egy and managemen ”and “indus ial and
manu ac u ing enginee ing,” he e is an indica ion o AI o op imiz-
ing manu ac u ing p ocesses and s a egic business planning. “Man-
agemen o echnology and inno a ion”and “managemen
in o ma ion sys ems”emphasize AI’s ole in managing echnological
ad ancemen s and in eg a ing AI in o co po a e in o ma ion in a-
s uc u es. Keywo ds such as “decision sciences”and “modeling and
simula ion”imply he use o AI o p edic i e modeling and simula-
ion in business scena ios. This clus e also sugges s AI applica ions
in finance and economics, as deno ed by “economics, econome ics,
Table 3
Highly ci ed a icles ep esen ing clus e 2.
To al Ci a ions Au ho (s) Ti le Subjec Ca ego y (ASJC)
508 Pa ício and Riede (2018) “Compu e ision and AI in p ecision ag icul u e o g ain
c ops: A sys ema ic e iew”
Ag onomy and C op Science| Fo es y| Ho icul u e| Com-
pu e Science Applica ions
312 Sha ma e al.(2020) “A sys ema ic li e a u e e iew on machine lea ning applica-
ions o sus ainable ag icul u e supply chain pe o mance”
Compu e Science| Managemen Science and Ope a ions
Resea ch| Modeling and Simula ion
289 Mosa i e al.(2019) “S a e o he a o machine lea ning models in ene gy sys-
ems, a sys ema ic e iew”
Ene gy | Fuel Technology| Renewable Ene gy, Sus ainabili y
and he En i onmen | | Con ol and Op imiza ion
R. Raman, D. Pa naik, L. Hughes e al. Jou nal o Inno a ion & Knowledge 9 (2024) 100517
8
he co e age and emphasis p esen in Scopus, po en ially limi ing he
comp ehensi eness and di e si y o pe spec i es in he b oade land-
scape o AI esea ch.
Conclusions
In conclusion, his s udy p o ides a comp ehensi e syn hesis o AI
esea ch by employing sys ema ic e iew me hodologies, hema ic
clus e ing, ad anced opic modeling echniques, and con en analy-
sis. Ou in es iga ion e ealed he ans o ma i e influence o AI
ac oss key domains, such as heal hca e, enginee ing, en i onmen al
applica ions, business, and echnology, cha ac e ized by significan
ad ancemen s in diagnos ics, pe sonalized medicine, sus ainabili y,
sma in as uc u e, decision suppo sys ems, and digi al expe ien-
ces. Specifically, AI in heal hca e and li e sciences has e olu ionized
diagnos ics and pe sonalized medicine (Clus e 1), while i s ole in
enginee ing and en i onmen al applica ions unde sco es i s con i-
bu ions o sus ainabili y and sma in as uc u e (Clus e 2). In busi-
ness and managemen , AI enhances decision suppo sys ems and
ope a ional op imiza ion (Clus e 3), and i s influence on digi al in a-
s uc u e and human-compu e in e aces highligh s i s b oad appli-
cabili y (Clus e 4). Ou findings demons a e AI’s pa adigm-shi ing
po en ial in heal hca e, ad ancing in elligen diagnos ics and pe son-
alized medicine, and sus ainable de elopmen , con ibu ing o en i-
onmen al sus ainabili y and sma ci y ini ia i es (RQ2). The
explo a ion o deep lea ning echnologies showcases AI’s impac on
emo ion analysis, molecula science, and na u al language p ocessing
(NLP) sys ems. AI-d i en inno a ions ex end o educa ion and indus-
y, op imizing supply chains and so wa e de elopmen , while in e-
g a ion wi h blockchain and cybe secu i y enhances da a secu i y
and p i acy. E hical conside a ions and bias mi iga ion emphasize
he necessi y o esponsible AI p ac ices. By syn hesizing di e se
insigh s and employing ad anced modeling echniques, we p o ide a
holis ic pe spec i e ha guides bo h cu en unde s anding and
u u e schola ly e o s (RQ3). This esea ch no only answe s key
esea ch ques ions bu also se s a new benchma k o conduc ing li -
e a u e e iews in apidly e ol ing scien ific domains. The
in eg a ion o scien ome ics and BERTopic modeling o e s a no el
app oach o un eiling he dynamics o AI applica ions, ensu ing a
deepe , mo e accu a e unde s anding o he ans o ma i e impac o
AI ac oss mul iple sec o s and pa ing he way o u u e ad ance-
men s in his c i ical field.
Decla a ion o compe ing in e es
The au ho s decla e ha hey ha e no known compe ing financial
in e es s o pe sonal ela ionships ha could ha e appea ed o influ-
ence he wo k epo ed in his pape .
CRediT au ho ship con ibu ion s a emen
Raghu Raman: W i ing − e iew & edi ing, W i ing −o iginal
d a , Supe ision, Me hodology, Da a cu a ion, Concep ualiza ion.
Debidu a Pa naik: W i ing − e iew & edi ing, W i ing −o iginal
d a , Me hodology, Da a cu a ion. Lau ie Hughes: W i ing − e iew
& edi ing, W i ing −o iginal d a . P ema Nedungadi: W i ing −
e iew & edi ing, W i ing −o iginal d a .
Da a a ailabili y s a emen
Da a associa ed wi h ou s udy is a ailable as supplemen a y file
Funding S a emen
This esea ch ecei ed no specific g an om unding agencies in
he public, comme cial, o no - o -p ofi sec o s.
Supplemen a y ma e ials
Supplemen a y ma e ial associa ed wi h his a icle can be ound
in he online e sion a doi:10.1016/j.jik.2024.100517.
Appendix
Topics, keywo ds and ep esen a i e ype o a icles on each opic
Topic Key e ms and hei P obabili y Rep esen a i e A icles APY
Ad ances in NLP
Sys ems
na u al language (0.33), na u al language p ocessing (0.32), p ocessing nlp
(0.23), language p ocessing nlp (0.23), ex mining (0.19), nlp me hods (0.17),
nlp sys ems (0.15), e iew na u al language (0.15), nlp applica ions (0.15),
language p ocessing sys ema ic (0.15)
K eimeye e al. (2017);Bannach-B own e al. (2019); Pa a e al.
(2021); Wahdan e al. (2020); Khanbhai e al. (2021); Tsou e al.
(2020); Mellia e al. (2021); Tu chioe e al. (2022); Ba iska
e al. (2021); Saman e al. (2022)
2021.4
AI in B ain Heal h
Analysis
alzheime disease (0.32), au ism spec um (0.24), mild cogni i e impai men
(0.21), eeg signals (0.19), neu oimaging da a (0.17), b ain compu e (0.16),
neu odegene a i e diso de (0.16), pa kinson disease pd (0.16), alzheime
disease sys ema ic (0.15), epilep ic seizu e (0.15)
Roy e al. (2019);Eb ahimighahna ieh e al. (2020); Mos a a e al.
(2019); de Belen e al. (2020); G ueso & Viejo-Sobe a (2021);
Loh e al. (2020); Saeidi e al. (2021); Alzahab e al. (2021); Tzi-
mou a e al. (2021); Mai ín e al. (2020)
2021.7
AI in Cance
Diagnosis
deep lea ning (0.20), b eas cance (0.19), medical imaging (0.15), e iew me a
analysis (0.15), p os a e cance (0.14), neu al ne wo k (0.14), con olu ional
neu al ne wo k (0.13), in elligence ai (0.13), ches ay (0.13), cance de ec-
ion (0.13)
Liu e al. (2019);B inke e al. (2018); Huang e al. (2020); So e
e al. (2020); Kassem e al. (2021); Ha is e al. (2019); Ab eu
e al. (2016); Ghade zadeh & Asadi (2021); Zhou e al. (2021);
Mahmood e al. (2020)
2022.0
AI in Educa ion ai educa ion (0.24), a ificial in elligence educa ion (0.23), in elligence educa-
ion (0.23), educa ional da a (0.19), educa ion a ificial (0.18), educa ion a i-
ficial in elligence (0.18), educa ion sys ema ic li e a u e (0.17), educa ional
da a mining (0.17), s uden d opou (0.17), online highe educa ion (0.16)
Tahi u (2021);Xu and Ouyang (2022); Seke oglu e al. (2021);
Okewu e al. (2021); Kaddou a e al. (2022); Salas-Pilco e al.
(2022); Baasha e al. (2021); Xu and Ouyang (2022); Issah e al.
(2023); Ramí ez Luelmo e al. (2021)
2022.1
AI in Ene gy and
Ag icul u e
deep lea ning (0.19), compu e ision (0.17), sys ema ic li e a u e e iew
(0.16), neu al ne wo k (0.16), emo e sensing (0.16), enewable ene gy
(0.15), neu al ne wo ks (0.14), c op yield p edic ion (0.14), ood image (0.13),
p ecision ag icul u e (0.13)
Mosa i e al. (2018);Ca alho e al. (2019); an Klompenbu g
e al. (2020); Mosa i e al. (2019);W
€
aldchen & M€
ade (2018);
Flah e al. (2021); Sony e al. (2021); Moayedi e al. (2020); Leu-
kel e al. (2021); Singh e al. (2021)
2021.9
AI in So wa e De ec
P edic ion
de ec p edic ion (0.35), so wa e de ec (0.33), so wa e de ec p edic ion
(0.32), so wa e es ing (0.28), so wa e de elopmen (0.28), lea ning so -
wa e (0.27), so wa e quali y (0.26), machine lea ning so wa e (0.26), so -
wa e aul p edic ion (0.23), code smell (0.22)
Li e al. (2020);Meiliana e al. (2017); Jo aye a e al. (2022);
Saha udin e al. (2020); Ma loob e al. (2021); S adowski &
Madeyski (2023); Hab ema iam e al. (2022); Kau e al. (2020);
B own e al. (2022); Sa hya aj & P abu (2016)
2020.8
(con inued)
R. Raman, D. Pa naik, L. Hughes e al. Jou nal o Inno a ion & Knowledge 9 (2024) 100517
15
Re e ences
ANZSCO, A. (2013). Aus alian and New Zealand S anda d Classifica ion o Occupa ions,
Ve sion 1.2. Canbe a: Aus alian Bu eau o S a is ics.
Bake , H. K., Kuma , S., & Pa naik, D. (2020). Twen y-fi e yea s o he Jou nal o Co po-
a e Finance: A Scien ome ic analysis. Jou nal o Co po a e Finance 101572.
doi:10.1016/j.jco pfin.2020.101572.
Bannach-B own, A., P zyby»a, P., Thomas, J., Rice, A. S. C., Ananiadou, S., Liao, J., &
Macleod, M. R. (2019). Machine lea ning algo i hms o sys ema ic e iew: Reduc-
ing wo kload in a p eclinical e iew o animal s udies and educing human sc een-
ing e o . Sys ema ic Re iews, 8(1), 23. doi:10.1186/s13643-019-0942-7.
Bouwmans, T., Ja ed, S., Sul ana, M., & Jung, S. K. (2018). Deep Neu al Ne wo k Concep s
o Backg ound Sub ac ion: A Sys ema ic Re iew and Compa a i e E alua ion.a Xi .
doi:10.48550/a Xi .1811.05255.
B inke , T. J., Hekle , A., U ikal, J. S., G abe, N., Schadendo , D., Klode, J., Be king, C.,
S eeb, T., Enk, A. H., & on Kalle, C. (2018). Skin cance classifica ion using con olu-
ional neu al ne wo ks: Sys ema ic e iew. Jou nal o Medical in e ne Resea ch, 20
(10), e11936. doi:10.2196/11936.
Bud ionis, A., Plikynas, D., Daniu
sis, P., & Ind ulionis, A. (2022). Sma phone-based
compu e ision a eling aids o blind and isually impai ed indi iduals: A sys-
ema ic e iew. Assis i e Technology: The O ficial Jou nal o RESNA, 34(2), 178–194.
doi:10.1080/10400435.2020.1743381.
Canedo, D., & Ne es, A. J. R. (2019). Facial Exp ession ecogni ion using compu e
ision: A sys ema ic e iew. Applied Sciences, 9(21). doi:10.3390/app9214678 A i-
cle 21.
Ca alho, T. P., Soa es, F. A. A. M. N., Vi a, R., F ancisco, R., da, P., Bas o, J. P., &
Alcal
a, S. G. S. (2019). A sys ema ic li e a u e e iew o machine lea ning me hods
applied o p edic i e main enance. Compu e s & Indus ial Enginee ing, 137,
106024. doi:10.1016/j.cie.2019.106024.
Ch is odoulou, E., Ma, J., Collins, G. S., S eye be g, E. W., Ve bakel, J. Y., &
Van Cals e , B. (2019). A sys ema ic e iew shows no pe o mance benefi o
machine lea ning o e logis ic eg ession o clinical p edic ion models. Jou nal o
Clinical Epidemiology, 110,12–22. doi:10.1016/j.jclinepi.2019.02.004.
Collins, C., Dennehy, D., Conboy, K., & Mikale , P. (2021). A ificial in elligence in in o -
ma ion sys ems esea ch: A sys ema ic li e a u e e iew and esea ch agenda.
In e na ional Jou nal o In o ma ion Managemen , 60, 102383. doi:10.1016/j.ijin-
omg .2021.102383.
Con e as, I., & Vehi, J. (2018). A ificial in elligence o diabe es managemen and deci-
sion suppo : li e a u e e iew. Jou nal o Medical In e ne Resea ch, 20,(5) e10775.
doi:10.2196/10775.
Da ko, A., Chan, A. P. C., Adab e, M. A., Edwa ds, D. J., Hosseini, M. R., &
Ameyaw, E. E. (2020). A ificial in elligence in he AEC indus y: Scien ome ic
analysis and isualiza ion o esea ch ac i i ies. Au oma ion in Cons uc ion, 112,
103081. doi:10.1016/j.au con.2020.103081.
(Con inued)
Topic Key e ms and hei P obabili y Rep esen a i e A icles APY
AI in T a fic Sa e y a ficflow (0.27), a fic conges ion (0.25), ajec o y p edic ion (0.23), ans-
po a ion sys ems (0.22), sa e y c i ical sys ems (0.22), pa h planning (0.21),
oad sa e y (0.20), a ficflow p edic ion (0.20), in elligen anspo (0.20),
public anspo a ion (0.20)
Si ohi e al. (2020);Noaeen e al. (2022); Nascimen o e al. (2020);
Sun e al. (2021); Al-Mas u Khan e al. (2020); Ali & Mahmood
(2018); Deshmukh (2018); Beh ooz & Haye i (2022); Di Felice
e al. (2019); Mannan e al. (2023)
2021.7
AI In eg a ion in
Sma Ci ies
li e a u e e iew (0.22), sma ci ies (0.21), in elligence ai (0.20), human
esou ce managemen (0.19), knowledge managemen (0.17), ai adop ion
(0.16), esea ch ai (0.15), ai ma ke ing (0.15), adop ion a ificial in elligence
(0.14), ai public (0.14)
Di Vaio e al. (2020);Da ko e al. (2020); V on is e al. (2022);
Yigi canla e al. (2020); Sousa e al. (2019); Mus ak e al.
(2021); Pe es e al. (2020); Zuide wijk e al. (2021); Ma iani
e al. (2022); Ribei o e al. (2021)
2022.0
AI Resea ch
Landscape
bibliome ic analysis (0.29), a ificial in elligence bibliome ic (0.19), in elli-
gence bibliome ic (0.19), in elligence bibliome ic analysis (0.17), a ificial
in elligence esea ch (0.16), in elligence esea ch (0.16), a ificial in elligence
ai (0.14), esea ch opics (0.14), la en di ichle alloca ion (0.14), ai esea ch
(0.13)
Guo e al. (2020);Hinojo-Lucena e al. (2019); De Felice & Poli-
meni (2020); Jha e al. (2017); Shukla e al. (2019); Hwang & Tu
(2021); Song & Wang (2020); Shen e al. (2022); Belmon e e al.
(2020); Zhang e al. (2022)
2021.4
AI-D i en Blockchain blockchain echnology (0.50), ai blockchain (0.42), in elligence blockchain
(0.38), a ificial in elligence blockchain (0.38), blockchain a ificial in elli-
gence (0.35), blockchain a ificial (0.35), ai enabled blockchain (0.27), block-
chain machine lea ning (0.25), in elligence ai blockchain (0.25), ai blockchain
echnology (0.23)
Kuma e al. (2023);Ek ami a d e al. (2020); Ka ge (2020); Mo -
iello (2019); Vincen e al. (2023); Hajizadeh e al. (2023);
Sha ma e al. (2023); Chen e al. (2023); de Bem Machado e al.
(2023); Abidemi e al. (2023)
2022.3
Deep Lea ning o
Emo ion Analysis
ace ecogni ion (0.41), exp ession ecogni ion (0.32), acial exp ession ecogni-
ion (0.31), objec de ec ion (0.28), ace li eness de ec ion (0.28), beha io
de ec ion (0.27), ace exp ession (0.25), human emo ion ecogni ion (0.25),
human ac i i y ecogni ion (0.24), deep lea ning ace (0.23)
Bouwmans e al. (2019); Canedo and Ne es (2019); Zhang e al.
(2021); Ullah e al. (2021); Ch ysle e al. (2021); Hassen e al.
(2022); Khai na e al. (2023); Panges u e al. (2022); C^
ı neanu
e al. (2023); Kau & Singh (2023)
2021.6
Deep Lea ning o
Recommenda ions
ecommende sys ems (0.62), ecommenda ion sys ems (0.49), lea ning ec-
ommenda ion (0.37), based ecommenda ion sys ems (0.34), lea ning ec-
ommende (0.33), deep lea ning based (0.32), deep lea ning ecommende
(0.32), lea ning based ecommenda ion (0.29), ecommende sys ems sys-
ema ic (0.29), ecommenda ion social (0.27)
Po ugal e al. (2018); Mu ad e al. (2019); Den Hengs e al.
(2020); B unial i e al. (2015); Necula & P
a
aloaia (2023); Lali-
ha & S eeja (2021); Selma e al. (2021); To kash and e al.
(2023); K ishnamoo hi & Shyam (2023); Li e al. (2023)
2020.7
Deep Lea ning in
Molecula Science
d ug disco e y (0.41), molecula simila i y (0.35), molecula simila i y sea ch-
ing (0.32), d ug design (0.29), p o ein unc ion p edic ion (0.28), sys ems
biology (0.27), deep lea ning d ug (0.26), lea ning d ug (0.26), p o ein sci-
ence (0.25), an icance d ug esponse (0.25)
Ma inelli (2022);Wu e al. (2022); Kou oumpa e al. (2023); Vil-
lalobos-Al a e al. (2022); P ocopio e al. (2023); Nasse e al.
(2023); Oguike e al. (2022); Yan e al. (2023); Faiz e al. (2023);
P a eena e al. (2023)
2022.0
Edge Compu ing and
Cybe secu i y
in usion de ec ion (0.25), in e ne hings (0.24), in e ne hings io (0.19),
hings io (0.19), edge compu ing (0.18), io secu i y (0.18), and oid malwa e
(0.17), denial se ice (0.17), ddos a acks (0.15), ake news de ec ion (0.15)
Ma ins e al. (2020);Manzoo e al. (2019); Lansky e al. (2021);
Shinan e al. (2021); Senanayake e al. (2021); Liu e al. (2022);
Aiyanyo e al. (2020); Busioc e al. (2020); Rosili e al. (2021);
Dakalbab e al. (2022)
2021.7
E hical Dimensions o
AI in Heal hca e
explainable a ificial in elligence (0.20), ai e hics (0.18), in elligence ai (0.18), ai
based (0.17), clinical ai (0.17), sys ema ic e iew (0.17), ai heal hca e (0.16),
a ificial in elligence heal hca e (0.16), in elligence heal hca e (0.15), a ifi-
cial in elligence xai (0.15)
Milne-I es e al. (2020);Loh e al. (2022); Xu e al. (2021); Choud-
hu y & Asan (2020); Ma hews (2019); Islam e al. (2022);
Schachne e al. (2020); Wells & Bedna z (2021); Oh e al.
(2021); on Ge ich e al. (2022)
2022.0
In elligen
Diagnos ics
sys ema ic e iew (0.19), me a analysis (0.18), isk bias (0.15), e iew me a
analysis (0.14), sys ema ic e iew me a (0.14), machine lea ning ml (0.13),
diabe ic e inopa hy (0.13), in elligence ai (0.12), machine lea ning models
(0.12), diagnos ic accu acy (0.11)
Ch is odoulou e al. (2019);Fleu en e al. (2020); Albah i e al.
(2020); Lee e al. (2018); Taya ani N. (2021); Balki e al. (2019);
Ahsan & Siddique (2022); Islam e al. (2020); Syeda e al.
(2021); Tejedo e al. (2020)
2021.8
Mi iga ing Bias in
Decision Sys ems
gende bias (0.71), gende bias ai (0.51), ai based decision (0.43), bias ai based
(0.42), bias a ificial in elligence (0.40), gende biases (0.38), mi iga ing gen-
de bias (0.32), gende bias a ificial (0.32), gende bias nlp (0.29), gende
biases ml (0.29)
Sun e al. (2020); Pagano e al. (2023); Va sha (2023); Sh es ha &
Das (2022); Nadeem e al. (2022); Reye o Lobo e al. (2023);
Hall & Ellis (2023); de Lima e al. (2023); Malhei o e al. (2023);
Sengewald & Lackes (2022)
2022.3
ML in Supply Chain
and Ma ke s
supply chain (0.40), s ock ma ke (0.35), ma ke p edic ion (0.27), s ock ma ke
p edic ion (0.25), supply chain managemen (0.25), li e a u e e iew (0.23),
aud de ec ion (0.19), p edic ing s ock (0.17), financial ime se ies (0.17),
machine lea ning ml (0.16)
Too ajipou e al. (2021);Goodell e al. (2021); Leo e al. (2019); Li
& Bas os (2020); Ahmed e al. (2022); Miklosik & E ans (2020);
Akba i & Do (2021); Jamwal e al. (2022); A bo e i e al. (2022);
Younis e al. (2022)
2021.7
Wea able Tech in
Rehabili a ion
gai analysis (0.33), sign language (0.29), ges u e ecogni ion (0.26), all de ec-
ion (0.22), wea able senso (0.20), physical ehabili a ion (0.19), ision
based (0.18), physical ac i i y (0.18), emg da a (0.18), human gai (0.18)
Gu chiek e al. (2019);Bud ionis e al. (2022); Laba i
e e e al.
(2020); Jou dan e al. (2021); Reine s e al. (2021); Bassyouni &
Elhajj (2021); Mennella e al. (2023); Donisi e al. (2022); De
Oli ei a e al. (2023); Lundg en e al. (2022)
2021.6
R. Raman, D. Pa naik, L. Hughes e al. Jou nal o Inno a ion & Knowledge 9 (2024) 100517
16
Da’u, A., & Salim, N. (2020). Recommenda ion sys em based on deep lea ning me hods:
A sys ema ic e iew and new di ec ions. A ificial In elligence Re iew, 53(4), 2709–
2748. doi:10.1007/s10462-019-09744-1.
Di Vaio, A., Palladino, R., Hassan, R., & Escoba , O. (2020). A ificial in elligence and
business models in he sus ainable de elopmen goals pe spec i e: A sys ema ic
li e a u e e iew. Jou nal o Business Resea ch, 121, 283–314. doi:10.1016/j.jbus-
es.2020.08.019.
Don hu, N., Reina z, W., Kuma , S., & Pa naik, D. (2021). A e ospec i e e iew o he
fi s 35 yea s o he in e na ional jou nal o esea ch in ma ke ing. In e na ional
Jou nal o Resea ch in Ma ke ing, 38(1), 232–269. doi:10.1016/j.ij es-
ma .2020.10.006.
Dwi edi, Y. K., Hughes, L., Ismagilo a, E., Aa s, G., Coombs, C., C ick, T., &
Williams, M. D (2021). A ificial In elligence (AI): Mul idisciplina y pe spec i es on
eme ging challenges, oppo uni ies, and agenda o esea ch, p ac ice and policy.
In e na ional Jou nal o In o ma ion Managemen , 57, 101994.
Dwi edi, Y. K., Kshe i, N., Hughes, L., Slade, E. L., Jeya aj, A., Ka , A. K., &
W igh , R. (2023). So wha i Cha GPT w o e i ?”Mul idisciplina y pe spec i es on
oppo uni ies, challenges and implica ions o gene a i e con e sa ional AI o
esea ch, p ac ice and policy. In e na ional Jou nal o In o ma ion Managemen , 71,
102642.
Eb ahimighahna ieh, M. A., Luo, S., & Chiong, R. (2020). Deep lea ning o de ec Alz-
heime ’s disease om neu oimaging: A sys ema ic li e a u e e iew. Compu e
Me hods and P og ams in Biomedicine, 187, 105242. doi:10.1016/j.
cmpb.2019.105242.
Egge , R., & Yu, J. (2022). A Topic Modeling Compa ison Be ween LDA, NMF, Top2Vec,
and BERTopic o Demys i y Twi e Pos s. F on ie s in Sociology, 7.h ps://www.
on ie sin.o g/a icles/10.3389/ soc.2022.886498.
Ek ami a d, A., Amin oosi, H., Seno, A. H., Dehghan anha, A., & Pa izi, R. M. (2020). A
sys ema ic li e a u e e iew o in eg a ion o blockchain and a ificial in elligence.
In A. Dehghan anha, R. M. Pa izi, K.-K. R. Choo (Eds.), Blockchain cybe secu i y, us
and p i acy (pp. 147−160). Sp inge In e na ional Publishing. doi:10.1007/978-3-
030-38181-3_8.
Fe a a, E. (2023). Fai ness and bias in a ificial in elligence: A b ie su ey o sou ces,
impac s, and mi iga ion s a egies. Sci, 6(1), 3.
Fleu en, L. M., Klausch, T. L. T., Zwage , C. L., Schoonmade, L. J., Guo, T., Rogge een, L. F.,
Swa , E. L., Gi bes, A. R. J., Tho al, P., E cole, A., Hoogendoo n, M., &
Elbe s, P. W. G. (2020). Machine lea ning o he p edic ion o sepsis: A sys ema ic
e iew and me a-analysis o diagnos ic es accu acy. In ensi e Ca e Medicine, 46
(3), 383–400. doi:10.1007/s00134-019-05872-y.
Gio anola, B., & Ti ibelli, S. (2023). Beyond bias and disc imina ion: Redefining he AI
e hics p inciple o ai ness in heal hca e machine-lea ning algo i hms. AI & socie y,
38(2), 549–563.
Goodell, J. W., Kuma , S., Lim, W. M., & Pa naik, D. (2021). A ificial in elligence and
machine lea ning in finance: Iden i ying ounda ions, hemes, and esea ch clus-
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