How AI and deep learning has revolutionised the medical industry

Feb 27
2 min read

Emir Can Aksoy

London, UK

*Attribution: Igor Omilaev from Unsplash*

Artificial Intelligence, in the form of machine learning and deep learning, has transformed the field of modern medicine. In recent years, these technologies have been significantly involved in the analysis of healthcare data, the diagnosis of diseases, and the delivery of customized treatment interventions in the medical field.

Machine learning has traditionally provided the foundation for computer-assisted decision support in the medical field. Machine learning algorithms have provided the capacity to learn from data and make predictions using the learned data. For instance, machine learning algorithms have provided the capability to predict the occurrence of diseases using laboratory data or demographic data.

However, with the increasing availability of large amounts of data in the medical field, deep learning algorithms have emerged as more powerful tools compared to machine learning algorithms. Deep learning is a subset of machine learning algorithms that use neural network architectures. Deep learning algorithms are data-intensive and have the ability to learn complex data relationships in the medical field that cannot be learned using machine learning algorithms. The use of deep learning algorithms over machine learning algorithms is a paradigm shift in data-driven medicine.

However, the impact of deep learning is not limited to images. Today's healthcare environment is associated with the production of tremendous amounts of structured and unstructured data, including lab test results, genetic information, and clinical notes included in the Electronic Medical Record system. Deep learning architectures are best suited to integrate and interpret these heterogeneous types of data to identify patterns that can be used to enable personalized medicine and personalized risk prediction.

The trajectory of AI in healthcare is certainly on the rise. With increased computational power and the availability of annotated medical data, deep learning architectures are expected to continue to improve in accuracy and generalization, and so in coming years, their role is indeed expected to expand.