The potential of machine learning in diagnosing neurological and psychiatric diseases: a review

Abstract

Purpose of the study: Artificial intelligence (AI) is rapidly transforming medical practice, from patient care to diagnosis and treatment personalization. With our work, we aim to explore the application of machine learning (ML) and deep learning (DL) algorithms in the diagnosis of neurological and psychiatric diseases, exploring into both the benefits and the challenges associated with these new technologies. Findings: The examined technologies have shown considerable success in early diagnosis, as well as in the identification of risk factors and symptom management for diseases such as Alzheimer’s, Parkinson’s and psychiatric disorders. These models could help improve diagnostic accuracy and enable a more personalized therapeutic approach by utilizing large datasets with information such as biomarkers and medical images. However, certain challenges persist, including concerns about data quality, patient privacy and the ethical implications of algorithmic decisions. Summary: Artificial intelligence-based diagnostic methods offer great potential to enhance early diagnosis and, consequently, the management of neurological and psychiatric disorders. To maximise their application, it is essential to ensure transparency and interpretability of the models, which are fundamental for their safe and effective use in medical practice.