Digital pathology and AI: enhancing molecular diagnostics in low- and middle-income countries
Year: 2025
Type: article
Abstract: INTRODUCTION: Molecular diagnostics in Low- and Middle-Income Countries (LMICs) face significant barriers: limited expertise, geographical access, and infrastructure. These impede effective disease management and public health. Traditional workflows lack scalability and objectivity. AREAS COVERED: This review explores how Digital Pathology (DP) and Artificial Intelligence (AI) can enhance LMIC molecular diagnostics. DP, via whole slide imaging, enables remote expert consultation and quality control. AI integration automates quantitative analysis (e.g. cell counting, biomarker scoring), facilitating rapid interpretation and extending specialized diagnostic reach. We discuss successful telepathology pilots and their cost-effectiveness. The manuscript highlights DP/AI's capacity to bolster molecular screening and accelerate research, while addressing implementation barriers: infrastructure, cost, training, and regulation. EXPERT OPINION: Strategic integration of Digital Pathology (DP) and Artificial Intelligence (AI) offers an unparalleled opportunity to transform molecular diagnostics in LMICs. By providing scalable, objective, and accessible capabilities, these technologies can significantly improve public health outcomes and medical research. However, successful adoption demands targeted investment in digital infrastructure, capacity building, robust ethics, and public-private partnerships. Prioritizing direct clinical utility and molecular diagnostic applications is key to sustainable implementation and equitable access to advanced diagnostics.
Authors: Arman Rahman, Chee Leong Cheng, Reza Salim, Henry Goh Di Shen, William M Gallagher, Jabed Iqbal
Language: English
Topic: AI in cancer detection
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
Awards: Precision Oncology Ireland, Optimal Management of Gender-Specific Cancers via Efficient Use of Protein Profiling, Digital Pathology and Systems Medicine Tools (OPTi-PREDICT), National Spatial Tissue Profiling (NaSPro) Platform for Precision Medicine: Integration of Chemical, Brightfield and Fluorescence Imaging of Tissues Combined with Single Cell Molecular Profiling, G5054930892, CHEMPREDICT DL Segregation of breast cancer patients at low and high risk of recurrence with chemical imaging and deep learning, G6759151040, G7022471852, G8173526563, G936396391
OpenAlex ID: W4415610007