Adopting AI-Enabled Digital Pathology in Resource-Limited Settings: A Practical Framework for Developing Countries
Recognising that most laboratories in low- and middle-income countries lack whole-slide scanners and digital infrastructure, this review proposes affordable entry points for pathologists to begin their digital transformation journey. The authors outline practical options for data capture, storage and AI adoption that can work within severe financial and resource constraints prevalent in developing-world health systems.
The original study
A suggested way forward for adoption of AI-Enabled digital pathology in low resource organizations in the developing world.
- Authors
- Zehra T, Parwani A, Abdul-Ghafar J, Ahmad Z
- Journal
- Diagnostic pathology
- Type
- Journal Article, Review
- PMID
- 37202805
Original abstract
Low- and middle-income countries (LMICs) represent a big source of data not only for endemic diseases but also for neoplasms. Data is the fuel which drives the modern era. Data when stored in digital form can be used for constructing disease models, analyzing disease trends and predicting disease outcomes in various demographic regions of the world. Most labs in developing countries don't have resources such as whole slide scanners or digital microscopes. Owing to severe financial constraints and lack of resources, they don't have the capability to handle large amounts of data. Due to these issues, precious data cannot be saved and utilized properly. However, digital techniques can be adopted even in low resource settings with significant financial constraints. In this review article, we suggest some of the options available to pathologists in developing countries which can enable them to start their digital journey and move forward despite resource-poor health system.