In e na ional Jou nal o Mul idisciplina y and Inno a i e Resea ch
ISSN(p in ): 3050-6883, ISSN(online): 3050-6891
Volume 02 Issue 10 Oc obe 2025
DOI: 10.58806/ijmi .2025. 2i10n01
Page No: 285-292
285 Volume 02 Issue 10 Oc obe 2025 Co esponding Au ho : Lakshmi P iya Bachina
AI Re olu ionizing Diagnos ic Imaging: Enhancing Accu acy and E iciency
D . Kanagala Anusha1, Ni edi ha Palle la2, Lakshmi P iya Bachina3*
1Assis an P o esso , Kone u Lakshmaiah Educa ion Founda ion
2Pha macy Manage , C aw o d Pha macy o Pleasan on
3Resea ch schola , Kone u Lakshmaiah Educa ion Founda ion
ABSTRACT
ARTICLE DETAILS
A i icial in elligence (AI) has he po en ial o e olu ionize diagnos ic imaging, leading o
signi ican imp o emen s in e iciency, accu acy, and pa ien ou comes. A i icial in elligence (AI)
in diagnos ic imaging signals he beginning o a e olu iona y pe iod in heal hca e, cha ac e ized by
p e iously un esol ed gains in pe cep ion and e ec i eness. The pape explo es how AI add esses
longs anding challenges in he ield, including human e o , ime-consuming manual p ocesses, and
subjec i e image in e p e a ion. The pape employed a sys ema ic li e a u e e iew o assess he
impac o AI on diagnos ic imaging e iciency and accu acy. S udies published be ween 2019 and
2023 we e e iewed, ocusing on pee - e iewed jou nals and con e ence p oceedings. The ini ial
po ion o he pape p o ides an o e iew o he classic di icul ies associa ed wi h diagnos ic
imaging, such as he possibili y o human mis akes, he labo ious manual p ocessing p ocess, and
he subjec i e in e p e a ion o pic u es. The pape examines how a i icial in elligence (AI)
echnologies a e e ec i e diagnos ic imaging echniques and how hey may imp o e e iciency and
accu acy in clinical se ings. By le e aging machine lea ning echniques like con olu ional neu al
ne wo ks (CNNs), AI can analyze complex imaging da a wi h excep ional p ecision, enabling ea ly
disease de ec ion, imp o ed isk assessmen , and mo e imely diagnoses. The pape also discusses
challenges associa ed wi h AI implemen a ion, such as algo i hm in e p e abili y and da a p i acy.
In conclusion, hough ul in eg a ion o AI holds immense p omise o ans o ming diagnos ic
imaging in o a new e a o p ecision medicine.
KEYWORDS: A i icial in elligence, Diagnos ics, Medical imaging, Accu acy
Published On:
06 Oc obe 2025
A ailable on:
h ps://ijmi .com
INTRODUCTION:
AI has become a po en ially use ul ool o add ess hese issues as he need o p ecise and e ec i e medical diagnos ics g ows. This
combina ion o e s p e iously unhea d-o p ospec s o imp o e accu acy, e iciency, and pa ien ou comes, po en ially
e olu ionizing he ield o medical imaging. The de elopmen o a i icial in elligence (AI) in diagnos ic imaging can be a ibu ed
o i s excep ional speed and accu acy in p ocessing la ge olumes o medical da a. AI sys ems can analyze complica ed imaging
da a, including X- ays, MRIs, and CT scans, and iden i y iny anomalies ha may be in isible o human eyes by u ilizing machine
lea ning algo i hms. As an illus a ion o how AI may imp o e diagnosis accu acy [14] showed how well deep lea ning algo i hms
pe o med in co ec ly iden i ying skin cance h ough he analysis o he moscopic pic u es. The e a e some imes bo lenecks in
adi ional diagnos ic sys ems, such as leng hy wai imes o imaging in e p e a ion and epo ing. AI echnology can speed up
pa ien ea men and lessen he wo kload on heal hca e p o ide s by quickly e alua ing pho os and p o iding eal- ime diagnos ics
suppo [8]. One p ominen ins ance is he applica ion o AI algo i hms o ches X- ays, which has demons a ed imp essi e esul s
in accele a ing he de ec ion o lung diso de s, such as diseases.[Fig1]AI-d i en diagnos ic solu ions can ill he gap by o e ing
emo e diagnos ic suppo in a eas whe e access o specialized medical knowledge is sca ce. I can acili a e p omp diagnosis and
ea men , especially in impo e ished a eas wi h limi ed access o medical esou ces. One p omising app oach o sc eening o
diabe ic e inopa hy in esou ce-cons ained si ua ions is he implemen a ion o AI-enabled mobile applica ions o e inal imaging,
which could enable ea ly de ec ion and in e en ion. AI can comple ely ans o m he diagnos ic imaging indus y by imp o ing
accu acy, op imizing wo k low e iciency, and add essing heal hca e inequi ies, which would e en ually esul in be e pa ien
ou comes and highe -quali y ca e.
AI Re olu ionizing Diagnos ic Imaging: Enhancing Accu acy and E iciency
286 Volume 02 Issue 10 Oc obe 2025 Co esponding Au ho : Lakshmi P iya Bachina
Fig 1: Diagnos ic imaging p ocedu e
BACKGROUND OF THE STUDY :
AI-based Diagnos ic imaging has a e olu ionizing phase. Be o e AI & A e AI his diagnos ic sec o has a lo o changes. Because
diagnos ic imaging p o ides medical p o essionals wi h in o ma ion abou he s uc u e and unc ions o he human body, i is c ucial
o mode n heal hca e. Medical p ac i ione s can use a ange o imaging modali ies, such as X- ays, MRIs, CT scans, ul asounds,
and nuclea medicine imaging, o non-in asi ely iew in e nal o gans, issues, and physiological p ocesses[3]. Diagnos ic imaging
is o help a wide ange o medical illnesses, om cance s and ac u es o neu ological diso de s and ca dio ascula ailmen s. Fo
example, MRI and CT scans p o ide comp ehensi e images o so issues, o gans, and blood essels, enabling he iden i ica ion o
illnesses like cance s, s okes, and hea disease. X- ays o assess bone ac u es. "AI has he po en ial o e olu ionize a ious
aspec s o heal hca e, including diagnos ics, ea men planning, and pa ien engagemen . Fo ins ance, [9] explo ed he use o AI-
powe ed ools o enhance pa ien engagemen , highligh ing i s po en ial o imp o e heal h ou comes." Abdominal o gan e alua ion
and e al de elopmen examina ion a e wo common uses o ul asound imaging du ing p egnancy. Sc eening p og ams o ea ly
disease de ec ion and p e en i e medicine depend hea ily on diagnos ic imaging. Fo example, mammog aphy is equen ly used
o sc een o b eas cance , while colonoscopy and i ual colonoscopy (CT colonog aphy) a e used o sc een o colo ec al cance .
GROWING IMPACT OF AI ON DIAGNOSTIC IMAGING :
The inc easing in luence o a i icial in elligence ( AI ) on diagnos ic imaging is ans o ming he way heal hca e is deli e ed by
imp o ing pa ien ou comes, accu acy, and e iciency. Deep lea ning is becoming inco po a ed in o diagnos ic imaging p ocesses,
e olu ionizing he in e p e a ion and analysis o medical images om modali ies, including MRIs, CT scans, ul asounds, and X-
ays. The inc ease in diagnosis accu acy is one o he e ec s o AI in diagnos ic imaging. A deep lea ning model su passed
adiologis s in iden i ying b eas cance on mammog ams, acco ding o a s udy by [1] , unde sco ing he po en ial o AI o enhance
diagnos ic capaci ies. In a simila ein, a me a-analysis conduc ed in 2020 by McKinney e al. demons a ed he e ec i eness o AI
algo i hms in diagnos ic accu acy in c i ical si ua ions by demons a ing high sensi i i y and speci ici y in iden i ying ce eb al
bleeding on head CT scans. Radiologis s may concen a e on complex cases and gi e apid diagnoses by using AI-powe ed ools
ha p io i ize u gen indings, iage cases, and sho en he ime needed o in e p e a ion. In de ma ologis s' manual assessmen , a
s udy by [3] showed he e iciency gains o an AI sys em o classi ying skin lesions, cu ing down on diagnos ic ime and inc easing
wo k low p oduc i i y. AI also makes pe sonalized medicine possible by combining imaging esul s and clinical da a o c ea e
cus omized ea men egimens and o ecas pa ien ou comes. A i icial in elligence (AI) algo i hms can help wi h isk assessmen ,
ea men esponse p edic ion, and disease p og ession acking by examining imaging bioma ke s and pa ien a ibu es. Fo
example, [16] s udy showed ha using MRI images, an AI model accu a ely p edic ed b eas cance pa ien s' esponse o neoadju an
chemo he apy, enabling indi idualized ea men plans.
FUNDAMENTALS OF DIAGNOSTIC IMAGING :
The inc easing in luence o a i icial in elligence (AI) on diagnos ic imaging is ans o ming he way heal hca e is deli e ed by
imp o ing pa ien ou comes, accu acy, and e iciency. Deep lea ning models a e becoming mo e inco po a ed in o diagnos ic
imaging p ocesses, e olu ionizing he in e p e a ion and analysis o medical images om modali ies, including MRIs, CT scans,
ul asounds, and X- ays. The inc ease in diagnosis accu acy is one o he majo e ec s o AI in diagnos ic imaging. La ge amoun s
o medical pic u e da a may be quickly analyzed by AI algo i hms, which can hen spo i egula i ies and sub le pa e ns ha human
adiologis s migh o e look. A deep lea ning model su passed adiologis s in iden i ying b eas cance on mammog ams, acco ding
o a s udy by [1], unde sco ing he po en ial o AI o enhance diagnos ic capaci ies. In a simila ein, a me a-analysis conduc ed in
AI Re olu ionizing Diagnos ic Imaging: Enhancing Accu acy and E iciency
287 Volume 02 Issue 10 Oc obe 2025 Co esponding Au ho : Lakshmi P iya Bachina
2020 by McKinney e al. demons a ed he e ec i eness o AI algo i hms in diagnos ic accu acy in c i ical si ua ions by
demons a ing high sensi i i y and speci ici y in iden i ying ce eb al bleeding on head CT scans. AI is inc easing wo k low
e iciency in diagnos ic imaging by speeding up pic u e analysis and au oma ing epe i i e p ocesses. Radiologis s may concen a e
on complex cases and gi e apid diagnoses by using AI-powe ed ools ha p io i ize u gen indings, iage cases, and sho en he
ime needed o in e p e a ion. In de ma ologis s' manual assessmen , a s udy by [3] showed he e iciency gains o an AI sys em o
classi ying skin lesions, cu ing down on diagnos ic ime and inc easing wo k low p oduc i i y. AI also makes pe sonalized medicine
possible by combining imaging esul s and clinical da a o c ea e cus omized ea men egimens and o ecas pa ien ou comes.
A i icial in elligence (AI) algo i hms can help wi h isk assessmen , ea men esponse p edic ion, and disease p og ession acking
by examining imaging bioma ke s and pa ien a ibu es. Fo example, [16] s udy showed ha using MRI images, an AI model
accu a ely p edic ed b eas cance pa ien s' esponse o neoadju an chemo he apy, enabling indi idualized ea men plans.
Fig 2: Diagnos ic imaging wo k low p ocess
Fig 2 ep esen s A pa ien o clinical issue as he i s s ep in he p ocess. The pa ien needs an imaging scan o his eason. The
Imaging Scan is he ollowing s age. The exac ype o diagnos ic imaging p ocedu e, such as a CT o MRI scan, will be pe o med.
Nex , he RIS/PACS ecei es he scan's images. These so wa e applica ions handle he s o age and managemen o medical images.
A e examining he pic u es, a adiologis o o he licensed medical p o essional will p epa e a epo . The s age o clinical
epo ing. The Imaging Repo is he las s age. The e e ing physician ecei es his epo and discusses he indings wi h he
pa ien . Digi al Imaging and Communica ions in Medicine is e e ed o as DICOM. I is a s anda d ha gua an ees he
in e changeabili y o medical images be ween a ious so wa e applica ions and imaging sys ems.
INTRODUCTION TO AI IN DIAGNOSTIC IMAGING :
Conside ing he p omise o imp o ed accessibili y, e iciency, and accu acy in illness diagnosis and pa ien ca e, he in oduc ion o
A i icial In elligence (AI) in o diagnos ic imaging has signaled a pa adigm shi in heal hca e. AI-powe ed solu ions p o ide a
e olu iona y app oach o image in e p e a ion.
AI APPLICATIONS FOR DIAGNOSTIC IMAGING:
By au oma ing ope a ions ha adiologis s once did, a i icial in elligence (AI) algo i hms a e changing diagnos ic imaging ac oss
many modali ies, including MRI, CT scan, ul asound, and X- ay. La ge olumes o imaging da a may be quickly analyzed by hese
algo i hms, which can iden i y abno mali ies and help iden i y diseases ema kably accu a ely. Fo example, [3] showed he
e ec i eness o a deep lea ning sys em in diagnosing skin cance wi h a pe o mance le el compa able o de ma ologis s in a s udy
published in Na u e Medicine. Like his, AI models ha e demons a ed po en ial in he diagnosis o neu ological illnesses h ough
b ain imaging, he in e p e a ion o e inal pic u es o diabe ic e inopa hy, and he de ec ion o b eas cance on mammog ams.
These applica ions a e as ollows :
1. Image Recogni ion and analysis
2. Au oma ed segmen a ion
3. Quan i a i e Analysis
4. Decision suppo sys em
5. P edic i e analysis
6. Quali y Con ol
7. Pe sonalized medicine
ENHANCING ACCURACY WITH AI:
AI Re olu ionizing Diagnos ic Imaging: Enhancing Accu acy and E iciency
288 Volume 02 Issue 10 Oc obe 2025 Co esponding Au ho : Lakshmi P iya Bachina
Th ough p e iously unhea d-o imp o emen s in accu acy and e iciency, a i icial in elligence is ans o ming diagnos ic imaging.
A i icial in elligence (AI) algo i hms ha e become indispensable ools o adiologis s and clinicians due o hei apid and accu a e
analysis o la ge amoun s o medical imaging da a. A i icial in elligence ( AI) may de ec minu e i egula i ies and ends in medical
images ha can go unno iced by humans by u ilizing sophis ica ed machine lea ning algo i hms. This abili y leads o be e pa ien
ou comes and p omp in e en ions by enabling ea lie disease diagnosis and imp o ing diagnos ic accu acy. AI also expedi es he
diagnos ic p ocedu e, hus inc easing he e ec i eness o heal hca e se ices. AI-powe ed decision suppo echnologies also help
adiologis s s eamline wo k low, gua an eeing ha pa ien s ecei e imely and app op ia e ca e and o e ing eal- ime insigh s and
ecommenda ions. Heal hca e p ac i ione s can inc ease h oughpu , lowe diagnos ic mis akes, and ul ima ely enhance he s anda d
o pa ien ca e by inco po a ing AI in o diagnos ic imaging wo k lows.
IMPROVING EFFICIENCY AND DIAGNOSTIC WORKFLOW :
Th ough d ama ically inc eased p oduc i i y and s eamlined diagnos ic wo k lows, AI is ans o ming diagnos ic imaging. AI
sys ems enable adiologis s o ocus on cases and judgmen s by au oma ing segmen a ion and analysis, eeing hem om manual
ac i i ies. To u he imp o e diagnos ic con idence and accu acy, AI-powe ed decision sys ems gi e adiologis s access o pe inen
clinical da a, eal- ime insigh s, and p oposed di e en ial diagnoses. AI in eg a ion in diagnos ic imaging boos s p oduc i i y, aises
h oughpu , and ul ima ely imp o es pa ien ou comes by s eamlining wo k low and lessening he di icul y o epe i i e ac i i ies.
REVIEW OF PREVIOUS RESEARCH :
AI could signi ican ly inc ease he p ecision and e ec i eness o diagnos ic imaging [10] emphasize how AI may enhance wo k low,
yield epea able ou comes, and acili a e eal- ime isk assessmen and diagnosis o applica ions ela ed o he hea and lungs,
whe e AI can au oma e he de ec ion and cha ac e iza ion o cha ac e is ics, s anda dize pic u e cap u e and p ocessing, and o ecas
indi idual esul s[5] unde sco es he capaci y o deep lea ning and machine lea ning me hodologies o augmen he p ecision o
medical pic u e analysis, speci ically in b ain umo iden i ica ion. Resea ch on a i icial in elligence in diagnos ic imaging has
signi ican ly inc eased, acco ding o a comp ehensi e su ey be ween 2019 and 2023. The Scopus da abase yielded 1750
documen s, o which 480 we e judged signi ican o he medical and heal h ields (Figu e 3). Because AI-powe ed diagnos ic
imaging has he po en ial o ans o m clinical p ac ice and enhance pa ien ou comes, he e is a g owing amoun o li e a u e ha
e lec s his in e es and in es men [15]. Examine and explo e he ways ha a i icial in elligence is being used in a ious medical
ields, including neu ology, pulmonology, and ca diology. Examine how AI algo i hms a e cus omized o handle diagnos ic issues
pa icula o each special y. P o ide quan i iable s a is ics o demons a e he g owing esea ch a ound AI-powe ed diagnos ic
imaging.
Figu e 3: Scopus da abase (2019-2023) G owing g aph
The e is no doub ing he ise in AI esea ch in diagnos ic imaging. A good indica ion is he 1750 eco ds we e ound in jus ou
yea s (Figu e 3). As he po en ial o AI o enhance pa ien ou comes and heal hca e deli e y becomes inc easingly e iden , his
numbe con inues climbing.
AI Re olu ionizing Diagnos ic Imaging: Enhancing Accu acy and E iciency
289 Volume 02 Issue 10 Oc obe 2025 Co esponding Au ho : Lakshmi P iya Bachina
Sou ce: Web o science Ba cha
The abo e ba cha shows he ollowing hings:
Neu ology: A i icial in elligence (AI) can examine b ain scans o indica ions o neu odegene a i e illnesses like Pa kinson's and
Alzheime 's. Addi ionally, i can help in he diagnosis o s okes.
Pulmonology: By examining CT scans and ches X- ays, AI is used o iden i y lung cance .
Ca diology: AI can iden i y i egula i ies in he hy hm and shape o he hea by analyzing echoca diog ams, a o m o ul asound
used o e alua e hea unc ion.
Oncology: AI is being used o diagnose b eas cance and o he cance s by analyzing pic u es om CT scans, mammog ams, and
o he es s.
Radiology: AI can help adiologis s unde s and a a ie y o medical pic u es, including CT scans, MRIs, and X- ays[2]
The Bibliome ic analysis is execu ed wi h he keywo ds used wi h AI. F om he analysis AI diagnos ic ela ed a icles signi ican ly
wi h analyze, design, and coo dina ion (Figu e 4)
Sou ce : Vos Viewe
The abo e diag am shows a ne wo k o a eas connec ed by lines. Wo ds abou a i icial in elligence (AI) in diagnos ics and imaging
o medicine can be ound in hese a eas. The "pa hology," "imaging," and "diagnosis" sphe es a e a he cen e o he ne wo k,
indica ing ha hese a e undamen al ideas in AI-powe ed medical diagnos ics. The ac ha ph ases like "machine lea ning,"
"decision suppo sys ems," and "compu e -aided diagnosis" a e nea he co e concep s sugges s ha hey a e c ucial pa s o AI
diagnos ics. The pic u e also illus a es he possibili y o AI o in eg a e di e en kinds o medical da a o diagnosis by using mo e
gene al ca ego ies like "genomics," "p o eomics," and "pheno ype." The ne wo k includes secu i y and p i acy ea u es,
emphasizing when de eloping and u ilizing AI diagnos ics.
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EXPLORATION OF AI TECHNIQUES SUCH AS MACHINE LEARNING, DEEP LEARNING, AND NEURAL
NETWORKS:
A i icial in elligence (AI) me hods like machine lea ning, deep lea ning, and neu al ne wo ks ha e become e olu iona y ools o
inc easing p ecision and p oduc i i y in diagnos ic imaging. Sys ems may lea n om da a and make p edic ions o judgemen s
wi hou explici p og amming algo i hms ha can be ained on massi e da ase s o medical pic u es in he con ex o diagnos ic
imaging o iden i y pa e ns and abno mali ies, assis ing in he iden i ica ion and diagnosis o illnesses. Deep lea ning is a sub ield
o Machine lea ning ha has gained no o ie y due o i s abili y o hie a chical da a ep esen a ions. One kind o deep lea ning model
ha has demons a ed ema kable pe o mance in asks like objec de ec ion, segmen a ion, and pic u e ca ego iza ion is he
con olu ional neu al ne wo k (CNN). In diagnos ic imaging, CNNs can p ecisely iden i y and de ine anomalies wi h pixel-le el
accu acy.
A neu al ne wo k’s basic s uc u e, which is in deep lea ning, is shown in he image you submi ed. Machine lea ning, a subse o
a i icial in elligence (AI), includes deep lea ning as a sub ield. Some key AI echniques ega ding Machine and deep lea ning.
Algo i hms a e used in machine lea ning o lea n om da a wi hou explici p og amming. I can be applied o applica ions like
clus e ing, eg ession, and classi ica ion. Algo i hms a e used in machine lea ning o lea n om da a wi hou explici p og amming.
I can be applied o applica ions like clus e ing, eg ession, and classi ica ion. Th ee p ima y ca ego ies o machine lea ning exis .
Labeled da a is used in supe ised o ain a model. Un supe ised lea ning looks o pa e ns in da a by u ilizing unlabeled da a. An
unsupe ised lea ning model. Fo ins ance, migh clien base in o a ious g oups acco ding o he consume s pas pu chases. T ial
and e o a e how ein o cemen lea ning. A ewa d signal is a ein o cemen lea ning model o ac i i ies ha i deems desi able
and a punishmen signal o ac s ha i deems undesi able. The model gains he abili y o beha e in a way ha maximizes he ewa d
signal o e ime.
METHODOLOGY:
To de e mine how AI will a ec he p ecision and e ec i eness o diagnos ic imaging. The pape employed a sys ema ic li e a u e
e iew o assess he impac o AI on diagnos ic imaging e iciency and accu acy. A sys ema ic li e a u e e iew will be conduc ed
o iden i y ele an s udies published be ween 2019 and 2023 (expanding he ime ame o a mo e obus analysis). Pee - e iewed
jou nals and con e ence p oceedings will be sea ched using elec onic da abases such as PubMed, Scopus, and Web o Science. The
keywo ds included ‘’AI,’’ ‘’a i icial in elligence,’’ ‘’machine lea ning,’ ‘’deep lea ning,’’ ‘’diagnos ic imaging,’’ ‘’medical
imaging,’’ ‘’ adiology’’’, ‘’accu acy’’, as well as ‘’e iciency’’. Techniques a e u ilized o imp o e accu acy and e iciency in he
quickly de eloping ield o AI applica ions in diagnos ic imaging [4]. Among hese echniques a e e idence-based algo i hms, which
a e c ea ed, es ed, and ained using benchma k da ase s [8]. AI can s eamline p ocesses and p oduce unbiased ou comes in
ca dio ascula imaging [10]. To p e en o e diagnosis, AI imaging esea ch mus concen a e on clinically signi ican goals like
su i al and symp oms [12]. Fu he mo e, he esul s o a ew selec ed s udies we e examined using he quali a i e analysis me hod.
The da a ex ac ion p ocedu e includes he AI algo i hms, imaging modali ies, sample size, s udy design, pe o mance indica o s,
and indings on diagnos ic accu acy and e iciency. To asce ain he possibili y o bias and con i m he alidi y and eliabili y o he
syn hesis e idence, a c i ical assessmen o he included s udies was conduc ed.
CHALLENGES & LIMITATIONS:
A i icial in elligence (AI) has he po en ial o e olu ionize diagnos ic imaging and is widely adop ed. Especially hose con aining
de ailed medical images a e essen ial o AI models o iden i y ends and gene a e p ecise p edic ions, bu ob aining hem can be
di icul because o da a silos, p i acy issues, and he equi emen o s anda dized o ma s. The in e p e abili y and explainabili y
o AI algo i hms p o ide ano he di icul y. Deep lea ning models a e equen ly employed in diagnos ic imaging, bu hey unc ion
as "black boxes," making i challenging o deciphe he logic unde lying hei p edic ions. Wi hou unde s anding how decisions a e
o med, adiologis s and o he medical p o essionals could be hesi an o us diagnoses p oduced by AI. Building con idence and
accep abili y among heal hca e p o essionals equi es ensu ing ha AI algo i hms a e anspa en . Addi ionally, AI sys ems wo k
wi h cu en heal hca e IT sys ems and elec onic heal h eco ds (EHRs) o seamlessly in eg a e in o clinical wo k lows.
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FINDINGS:
The indings om he AI- e olu iona y diagnos ic imaging a e 1. Enhanced accu acy
2. E iciency Gains
3. Ea ly disease de ec ion
4. Pe sonalized Medicine
5. Remo e diagnosis
6. Quali y Con ol
FUTURE SCOPE:
The u u e o diagnos ic imaging lies in he ad ancemen o a i icial in elligence (AI) echnology. Ad ances in machine lea ning
and a i icial in elligence enhance pa ien ou comes by aising he p ecision and e icacy o diagnos ic p ocedu es. Also, he
de elopmen o cus omized AI models sui ed o ce ain imaging modali ies and clinical applica ions by inno a i e mul idisciplina y
coope a ion in ol ing adiologis s, medical p o essionals, and AI expe s. As AI becomes mo e widely used in diagnos ic imaging,
i will be c i ical o add ess e hical and legal issues ela ed o pa ien pe mission, algo i hm openness, and da a p o ec ion o ensu e
i s esponsible and equi able applica ion in heal hca e p ocedu es.
CONCLUSION:
These de elopmen s enhance pa ien ou comes and heal hca e inequi ies wi h medical p o essionals wi h s ong diagnos ic and
he apeu ic ins umen s. The issues o da a p i acy, algo i hm anspa ency, and egula o y compliance mus be acknowledged and
add essed as we emb ace he ans o ma i e p omise o AI in diagnos ic imaging. Fo AI esea che s, clinicians, legisla o s, and
indus y s akeholde s o e ec i ely na iga e hese obs acles and gua an ee he e hical and esponsible applica ion o AI echnology
in clinical p ac ice, coope a ion is essen ial. We can c ea e a u u e whe e diagnos ic imaging se s new benchma ks o accu acy,
e iciency, and accessibili y in heal hca e by u ilizing AI while pu ing pa ien sa e y, p i acy, and equi y i s . In summa y, a i icial
in elligence (AI) in diagnos ic imaging is a game-change o he heal hca e indus y, p esen ing o imp o e pa ien ca e, e iciency,
and accu acy. A i icial in elligence (AI)-d i en algo i hms ha e p o en ema kably e ec i e a iden i ying minu e i egula i ies,
op imizing wo k low p ocedu es, and suppo ing ea ly illness iden i ica ion.
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