Deep Learning and AI in the Medical Field

Innovating the Future of Medicine.

Artificial Intelligence (AI) refers to machine learning and deep learning, where a machine takes information from its surroundings and implements the most optimal decision suitable to the situation.

In medicine, AI has emerged due to the digitized and large datasets that require analysis. AI’s application can be seen during screening processes, where analyzing images, lifestyle, and health data can support the early diagnosis or prediction of diseases. It allows for the implementation of preventative measures before disease manifestation and the potential reduction of false-positive diagnoses.

AI has the potential to assist in regions with less hands-on healthcare where isolated communities can benefit from AI that could replace physicians. Countries like Korea already employ AI to detect tuberculosis from chest X-rays.

A notable example of AI in practical use is Deep Patient, a machine-learning method modeled from the health records of 700,000 patients. It successfully predicted diseases like severe diabetes, schizophrenia, and certain cancers with 80-90% accuracy.

Despite the promising results, AI reliance does pose some challenges. The basis on which AI learns still needs to be understood fully. There is uncertainty as to why algorithms give specific outputs, even if they are correct.

Overall, AI in medicine holds enormous promise for improving healthcare delivery. However, its application also poses challenges, such as understanding the underlying principles of its accuracy. As AI evolves, it will open up new opportunities to improve medical diagnostics and treatments.