Diagnostic performance of artificial intelligence for dermatological conditions: a systematic review focused on low- and middle-income countries to address resource constraints and improve access to specialist care
Dublin Core
Title
Diagnostic performance of artificial intelligence for dermatological conditions: a systematic review focused on low- and middle-income countries to address resource constraints and improve access to specialist care
Subject
Dermatology, Artificial Intelligence, Low- and Middle-Income Countries, Convolutional Neural Networks,
Diagnosis
Diagnosis
Description
Abstract
Background Artificial Intelligence (AI) has emerged as a transformative tool in dermatology, particularly in Low- and
Middle-Income Countries (LMICs), where healthcare systems face challenges such as a shortage of dermatologists
and limited resources. AI technologies, including deep learning models like Convolutional Neural Networks (CNNs),
have demonstrated potential in improving diagnostic accuracy for skin diseases, which contribute significantly to
the global disease burden. However, most research has focused on High-Income Countries (HICs), leaving gaps in
understanding AI's applicability and effectiveness in LMICs.
Aim/Objective This systematic review critically evaluates the application of AI in dermatological practice within
LMICs, assessing the performance of AI technologies across diverse geographic regions.
Methodology The review adhered to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses
(PRISMA) guidelines and included 19 studies from databases including PubMed, Embase, and Cochrane. Eligible
studies evaluated AI applications in dermatology within LMICs, reporting metrics like sensitivity, specificity, precision,
and accuracy. Data extraction and quality assessment were performed independently by several reviewers using tools
like PROBAST and QUADAS-2. A qualitative synthesis as per SWiM guidelines was conducted due to heterogeneity in
study designs and outcomes.
Conclusion AI shows significant promise in enhancing dermatological diagnostics and expanding access to
dermatologic care in LMICs, with models achieving high accuracy (up to 99%) in tasks like skin cancer and infectious
disease detection. However, challenges such as underrepresented skin tones in datasets, limited clinical validation,
Background Artificial Intelligence (AI) has emerged as a transformative tool in dermatology, particularly in Low- and
Middle-Income Countries (LMICs), where healthcare systems face challenges such as a shortage of dermatologists
and limited resources. AI technologies, including deep learning models like Convolutional Neural Networks (CNNs),
have demonstrated potential in improving diagnostic accuracy for skin diseases, which contribute significantly to
the global disease burden. However, most research has focused on High-Income Countries (HICs), leaving gaps in
understanding AI's applicability and effectiveness in LMICs.
Aim/Objective This systematic review critically evaluates the application of AI in dermatological practice within
LMICs, assessing the performance of AI technologies across diverse geographic regions.
Methodology The review adhered to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses
(PRISMA) guidelines and included 19 studies from databases including PubMed, Embase, and Cochrane. Eligible
studies evaluated AI applications in dermatology within LMICs, reporting metrics like sensitivity, specificity, precision,
and accuracy. Data extraction and quality assessment were performed independently by several reviewers using tools
like PROBAST and QUADAS-2. A qualitative synthesis as per SWiM guidelines was conducted due to heterogeneity in
study designs and outcomes.
Conclusion AI shows significant promise in enhancing dermatological diagnostics and expanding access to
dermatologic care in LMICs, with models achieving high accuracy (up to 99%) in tasks like skin cancer and infectious
disease detection. However, challenges such as underrepresented skin tones in datasets, limited clinical validation,
Creator
Olivier Uwishema1* , Malak Ghezzawi1,2, Nicole Charbel1,3,4, Shireen Alawieh1,3,4, Subham Roy1,5, Magda Wojtara1,6,
Clyde Moono Hakayuwa1,7, Ibrahim Khalil Ja’afar1,8, Gerard Nkurunziza9
and Manya Prasad10
Clyde Moono Hakayuwa1,7, Ibrahim Khalil Ja’afar1,8, Gerard Nkurunziza9
and Manya Prasad10
Date
2025
Contributor
Peri Irawan
Format
PDF
Language
ENGLISH
Type
TEXT
Files
Collection
Citation
Olivier Uwishema1* , Malak Ghezzawi1,2, Nicole Charbel1,3,4, Shireen Alawieh1,3,4, Subham Roy1,5, Magda Wojtara1,6,
Clyde Moono Hakayuwa1,7, Ibrahim Khalil Ja’afar1,8, Gerard Nkurunziza9
and Manya Prasad10, “Diagnostic performance of artificial intelligence for dermatological conditions: a systematic review focused on low- and middle-income countries to address resource constraints and improve access to specialist care,” Repository Horizon University Indonesia, accessed April 20, 2026, https://repository.horizon.ac.id/items/show/13270.