Clinical applications of deep learning in neurology and its enhancements with future predictions

Studies have indicated that data generated by healthcare industries can be harnessed utilizing artificial intelligence (AI) to improve medical practice such as diagnosis, evaluation, and treatment strategies. The neural network system can generate signals during physiological or pathophysiological mechanisms, which may be adapted for the prediction of complex neurological outcomes. The future direction of AI in clinical practice covers many disciplines such as physiology, histology, pathology, radiology, and neurology. Data from these areas can be utilized to generate high-resolution images and biomarkers for fast automated disease prediction, interpretation, and treatment strategies. Clinicians can have better decision-making options and more accurate diagnostic predictions when AI is incorporated into medical practice. Thus, this chapter provides a detailed account of advances made in the adoption of AI applications to enhance neurological techniques in clinical settings like medical imaging, translational bioinformatics, cheminformatics, pervasive sensing, public health, and medical informatics. The chapter also provides a critical analysis of potential pitfalls of these techniques.