  Artificial neural network (ANN) ability to learn, correct errors, and
transform a large amount of raw data into useful medical decisions for
treatment and care have increased its popularity for enhanced patient safety
and quality of care. Therefore, this paper reviews the critical role of ANNs in
providing valuable insights for patients' healthcare decisions and efficient
disease diagnosis. We thoroughly review different types of ANNs presented in
the existing literature that advanced ANNs adaptation for complex applications.
Moreover, we also investigate ANN's advances for various disease diagnoses and
treatments such as viral, skin, cancer, and COVID-19. Furthermore, we propose a
novel deep Convolutional Neural Network (CNN) model called ConXNet for
improving the detection accuracy of COVID-19 disease. ConXNet is trained and
tested using different datasets, and it achieves more than 97% detection
accuracy and precision, which is significantly better than existing models.
Finally, we highlight future research directions and challenges such as
complexity of the algorithms, insufficient available data, privacy and
security, and integration of biosensing with ANNs. These research directions
require considerable attention for improving the scope of ANNs for medical
diagnostic and treatment applications.
