*Co esponding au ho : Venugopal Reddy I agam eddy.
Copy igh © 2025 Au ho (s) e ain he copy igh o his a icle. This a icle is published unde he e ms o he C ea i e Commons A ibu ion Liscense 4.0.
The ole o A i icial In elligence in diagnosing a e pedia ic diseases: A global
pe spec i e
Venugopal Reddy I agam eddy *
Depa men o Pedia ics, O um Woman and Child Speciali y Hospi al, Bangalo e, India.
Wo ld Jou nal o Biology Pha macy and Heal h Sciences, 2025, 21(01), 036-038
Publica ion his o y: Recei ed on 20 No embe 2024; e ised on 28 No embe 2024; accep ed on 31 Decembe 2024
A icle DOI: h ps://doi.o g/10.30574/wjbphs.2025.21.1.1108
Abs ac
Ra e pedia ic diseases o en p esen signi ican diagnos ic challenges due o hei a ypical mani es a ions and lack o
amilia i y among heal hca e p o ide s. A i icial In elligence (AI) o e s ans o ma i e po en ial in b idging diagnos ic
gaps, pa icula ly in esou ce-limi ed se ings. This e iew highligh s he ole o AI in iden i ying a e pedia ic
condi ions h ough ad anced algo i hms, pa e n ecogni ion, and machine lea ning. By examining success ul
implemen a ions globally, we explo e he po en ial o AI o e olu ionize pedia ic diagnos ics, add ess dispa i ies in
heal hca e access, and imp o e ou comes o child en. Challenges such as da a bias, e hical conside a ions, and
in as uc u al ba ie s a e also discussed, alongside ecommenda ions o u u e esea ch and in eg a ion s a egies.
Keywo ds: A i icial In elligence; Ra e pedia ic diseases; Machine lea ning; Heal hca e dispa i ies; Diagnos ic ools;
Global heal h
1. In oduc ion
The diagnosis o a e pedia ic diseases is a complex p ocess equi ing specialized knowledge and signi ican diagnos ic
esou ces. Many heal hca e sys ems, pa icula ly in low- and middle-income coun ies, lack hese esou ces, leading o
delayed o missed diagnoses. The eme gence o A i icial In elligence (AI) as a diagnos ic ool has opened new a enues
o add essing hese challenges. AI’s abili y o p ocess as da ase s, ecognize pa e ns, and suppo clinical decision-
making holds p omise o iden i ying a e condi ions mo e e ec i ely. This e iew examines he cu en ole o AI in
diagnosing a e pedia ic diseases, i s global implica ions, and he po en ial o mi iga e heal hca e dispa i ies.
2. Applica ions o AI in Diagnosing Ra e Pedia ic Diseases
2.1. Enhanced Diagnos ic Accu acy
AI-powe ed ools u ilize ad anced algo i hms o analyze clinical da a, imaging, and gene ic in o ma ion, imp o ing
diagnos ic accu acy o a e condi ions.
•Machine Lea ning Algo i hms: P edic i e models ained on la ge da ase s can iden i y a e disease
pa e ns o en missed by clinicians.
•Genomic Sequencing: AI assis s in analyzing genomic da a o pinpoin mu a ions associa ed wi h a e
pedia ic diseases such as gene ic synd omes o me abolic diso de s.
Wo ld Jou nal o Biology Pha macy and Heal h Sciences, 2025, 21(01), 036-038
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2.2. Ea ly De ec ion h ough Image Analysis
AI sys ems excel in analyzing medical images such as X- ays, MRIs, and CT scans, iden i ying sub le anomalies ha may
indica e a e condi ions.
• Case S udy: AI-d i en imaging ools ha e success ully de ec ed ea ly signs o condi ions like e inoblas oma and
congeni al hea de ec s.
• E iciency: Au oma ed image analysis educes he ime equi ed o diagnosis, enabling ea lie in e en ion.
2.3. B idging Resou ce Gaps in Low-Income Se ings
AI ools ha e demons a ed he po en ial o add ess dispa i ies in heal hca e access by suppo ing diagnos ics in
esou ce-cons ained en i onmen s.
• Telemedicine In eg a ion: AI-powe ed diagnos ic pla o ms enable emo e consul a ions, educing he
dependency on in-pe son specialis isi s.
• Low-Cos Diagnos ic Solu ions: AI ools, such as mobile applica ions and wea able de ices, p o ide cos -e ec i e
op ions o disease iden i ica ion in unde se ed egions.
3. Global Case S udies
• AI in Genomics: The Undiagnosed Diseases Ne wo k (UDN) uses AI o analyze genomic da a, leading o
diagnos ic b eak h oughs o a e diseases.
• AI-Powe ed Diagnos ics in A ica: Machine lea ning pla o ms in A ica a e aiding he diagnosis o pedia ic
neu ological diso de s wi h minimal specialis in ol emen .
• AI in Telemedicine: Ini ia i es like he AI-powe ed Babylon Heal h pla o m a e helping iden i y a e
condi ions in emo e egions by analyzing pa ien symp oms.
4. Challenges in AI In eg a ion
4.1. Da a Bias and Gene alizabili y
AI models o en ely on da ase s ha lack di e si y, leading o po en ial biases in diagnos ic p edic ions.
4.2. E hical and P i acy Conce ns
The use o pa ien da a in AI sys ems aises conce ns abou con iden iali y, consen , and po en ial misuse o sensi i e
in o ma ion.
4.3. In as uc u al Ba ie s
Limi ed access o ad anced echnology, inconsis en in e ne connec i i y, and inadequa e aining o heal hca e
p o ide s hinde AI adop ion in esou ce-limi ed se ings.
5. Fu u e Di ec ions
5.1. Collabo a i e Resea ch and Da a Sha ing
• Es ablish global da abases o imp o e AI model aining and educe bias.
• Fos e collabo a ions be ween high-income and low-income coun ies o p omo e equi able access o AI ools.
5.2. E hical F amewo ks and Regula ion
• De elop comp ehensi e guidelines o ensu e e hical use o AI in pedia ic diagnos ics.
• P omo e anspa ency and accoun abili y in AI-d i en decision-making.
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5.3. Capaci y Building
• T ain heal hca e p o essionals in AI ools o enhance hei diagnos ic capabili ies.
• In es in echnological in as uc u e o suppo AI in eg a ion in unde se ed egions.
6. Conclusion
A i icial In elligence has he po en ial o e olu ionize he diagnosis o a e pedia ic diseases by enhancing accu acy,
educing diagnos ic imelines, and add essing heal hca e inequi ies. Howe e , i s success ul implemen a ion equi es
o e coming challenges ela ed o da a bias, e hical conce ns, and in as uc u al limi a ions. By os e ing global
collabo a ion, in es ing in equi able heal hca e s a egies, and p omo ing e hical AI p ac ices, we can ha ness he
ans o ma i e powe o AI o imp o e ou comes o child en wi h a e diseases wo ldwide.
Compliance wi h e hical s anda ds
Acknowledgmen s
The au ho ex ends g a i ude o he global heal hca e and AI communi ies o hei ad ancemen s in pedia ic
diagnos ics and hei dedica ion o equi able heal hca e.
Disclosu e o con lic o in e es
The au ho decla es no con lic o in e es .
Re e ences
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[3] Li Q, e al. A i icial in elligence in a e disease diagnosis. F on ie s in Medicine. 2020; 7:599.
[4] Holzinge A, e al. Machine lea ning o heal h ca e and p ecision medicine. Annual Re iew o Biomedical Da a
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Au ho ’s sho Biog aphy
D . Venugopal Reddy is a dis inguished Medical Di ec o and Pedia ician a O um woman
and Child Speciali y Hospi al in Bangalo e, India. Wi h ex ensi e expe ise in pedia ic ca e,
esea ch, and communi y heal h ini ia i es, he has au ho ed nea ly 100 a icles in Scopus and
PubMed-indexed jou nals. He is ac i ely in ol ed in imp o ing heal hca e sys ems, child heal h
awa eness, and ma e nal well-being. His wo k has ea ned him ecogni ion as one o he op
p o essionals shaping heal hca e in India.
