How AI is Changing Healthcare in Developing Countries
AI Beyond Borders
Transfer Learning
There is a concept in AI called transfer learning which can play a crucial role in making advanced healthcare AI technologies accessible and effective in developing countries.
Transfer learning is about copying a model often built on large high quality data and applying it to smaller, local datasets. This prevents the need of building new models from scratch and enables us to “transfer” what the model has learnt from one dataset and apply it to another. Now, how can this concept be applied to help improve healthcare quality in developing countries?
Existing AI models that have been built with extensive data from developed countries can be fine-tuned and applied to smaller datasets in developing countries. This makes it possible to deploy complex and high performing AI tools in regions where large-scale data collection is not feasible.
To give a practical example, let us take medical imaging. Transfer learning enables models that are trained on vast datasets from well-resourced healthcare systems to diagnose diseases such as…
