We are currently experiencing a technological revolution in the age of deep learning. Previously, computer systems functioned solely based on rules set and coded by human experts. However, an increasing number of systems depend on machine learning from extensive datasets, a process known as "deep learning."
Examples of deep learning in action include automatic friend tagging on social networks, real-time detection of suspicious activities in security footage, algorithmic trading in financial markets, machine translation between languages, and numerous other applications.
Deep learning is intrinsically linked to big data. On one hand, it serves as the primary algorithmic method for analyzing massive datasets. On the other hand, the effectiveness of deep learning depends on access to large volumes of data to train these systems.
As a result, data has become a new form of capital, often compared to oil in its economic value. Companies with exclusive access to large data repositories, such as search engine providers and social media platforms, are valued at market levels approaching (and sometimes exceeding) a trillion dollars.
This reality positions access to medical big data as a strategic asset for the future of healthcare. The question is whether this asset will be leveraged for private profit or channeled into creating accessible, socially-oriented digital health solutions that benefit all segments of society.