AI-integrated diagnostic models for neurological disorders

Neurological diseases, among them Alzheimer’s and Parkinson’s diseases, epilepsy, and multiple sclerosis, are very difficult to diagnose and treat as they are characterized by diverse onset and symptoms. Early and precise detection is crucial to good control, whereas conventional methods tend to be subjective and time-consuming, leading to delayed intervention. Artificial intelligence (AI) solutions, which exploit large amounts of data from neuroimaging, genetics, electrophysiology, and electronic health records, present a variety of promising approaches. Machine learning and deep learning can recognize subtle patterns which are difficult to discern by humans, so as to help early screening and enhance diagnosis. For example, AI has been applied to predict Alzheimer’s evolution using MRI biomarkers, detect seizures from EEG signals, and monitor Parkinson’s evolving (wearable) symptomatology. These advances illustrate the capabilities of AI to improve precision and individualize treatment approaches. However, limitations must be met before successful integration of AI in neurology can be achieved. Robust methods can be implemented only when built based on big, broad, and solid datasets that are free from biases and therefore can be generalized. Ethical issues including privacy of the patient, consent, and transparency of the algorithm also need to be handled. In addition, AI regulations in health care are still in their infancy.