A Review on Artificial Intelligence and Machine Learning Techniques for Neurological Disorder Diagnosis: Challenges, Biases and Clinical Considerations

Abstract

The brain functions as the central control centre of the body and a growing number of novel neurological disorders are being recognized. The wide range of brain illnesses presents challenges for existing diagnostic and detection technologies, making it a persistent area of research. Effective treatment for neurological illnesses depends on early identification. The accuracy of predicting and diagnosing these illnesses has greatly increased with the use of artificial intelligence in medicine. Neurological diseases (ND) are increasingly affecting individuals across all age groups, including children, adults, pregnant women, parents, and even healthy infants. These disorders vary in their origins, symptoms, outcomes, and prognoses. Neuroimaging techniques such as MEG, MRI, and PET have shed light on brain function in recent years. These developments provide encouraging prospects for using computer-assisted diagnostic tools in conjunction with diverse machine learning and deep learning methodologies to diagnose neurological disorders. Our study is centered around discovering various ML/DL approaches that can be used to detect and classify neurological disorders, such as Alzheimer's disease, multiple cerebral palsy, Parkinson's disease, sclerosis, epilepsy, and brain tumours. We are especially interested in techniques that can detect these disorders at an early stage. This review explores the breadth of ML and deep learning (DL) techniques employed for diagnosing neurological conditions. We examine key datasets, model architectures, performance benchmarks, and highlight major gaps including data imbalance, lack of multimodal standardization, and demographic underrepresentation. Furthermore, we address the ethical and regulatory challenges tied to clinical deployment, such as patient privacy, algorithmic bias, and FDA compliance. The review concludes with future directions emphasizing explainable AI, federated learning, and clinical validation pathways.