  The integration of machine learning in medical image analysis can greatly
enhance the quality of healthcare provided by physicians. The combination of
human expertise and computerized systems can result in improved diagnostic
accuracy. An automated machine learning approach simplifies the creation of
custom image recognition models by utilizing neural architecture search and
transfer learning techniques. Medical imaging techniques are used to
non-invasively create images of internal organs and body parts for diagnostic
and procedural purposes. This article aims to highlight the potential
applications, strategies, and techniques of AutoML in medical imaging through
theoretical and empirical evidence.
