Neuropsychiatric Disease and Treatment

Artificial Intelligence and Machine Learning in Neuropsychiatric Disease Diagnosis, Prognosis, and Treatment

Artificial intelligence (AI), machine learning (ML), and data mining (DM) have become pivotal tools in revolutionizing the diagnosis, treatment, and management of neuropsychiatric diseases. These technologies, capable of handling vast amounts of complex data, offer innovative solutions to traditional clinical approaches. The intersection of AI, ML, and neuropsychiatry is expanding rapidly, bringing about groundbreaking advancements in understanding conditions such as Autism Spectrum Disorder, Attention Deficit Hyperactivity Disorder, Anxiety and Depressive Disorders, Schizophrenia, and other cognitive disorders. By leveraging data-driven techniques, it is now possible to detect patterns in patient data that were previously difficult to identify, enabling early detection, personalized treatment, and improved patient outcomes. This Article Collection will explore how AI, ML, and DM are being applied to neuropsychiatric diseases, highlighting recent research and methodologies.

The importance of this topic lies in its potential to address some of the most pressing challenges in neuropsychiatric healthcare. Traditional diagnostic methods, based on clinical judgment and limited patient data, often fail to capture the complexity of brain disorders. The integration of AI and ML techniques, such as neural networks, deep learning, and reinforcement learning, promises to enhance diagnostic accuracy, predict disease progression, and optimize treatment strategies. Additionally, the ability of AI and ML to analyze multimodal data, including genetic, neuroimaging, and behavioral data, could lead to the discovery of novel biomarkers and personalized therapeutic interventions. As these technologies become more refined, they hold the potential to revolutionize how neuropsychiatric conditions are understood and treated.

This Article Collection will focus on the cutting-edge applications of AI, ML, and DM in the field of neuropsychiatry. We welcome original research, survey papers, meta-analyses, and review articles that explore the following subtopics including but not limited to:

• AI/ML in Neuropsychiatric Disease Diagnosis and Prognosis: Studies on how AI models, including deep learning and reinforcement learning, can be used to predict disease onset, progression, and therapeutic response.
• AI/ML Models for Biomarker Discovery: Utilizing machine learning algorithms to identify novel biomarkers for neuropsychiatric conditions, focusing on genetic, neuroimaging, and clinical data integration.
• Personalized Treatment through AI/ML: Exploration of AI-driven approaches in tailoring treatments for patients based on their unique genetic, clinical, and environmental factors.
• Data Mining Techniques in Cognitive Disorder Management: Application of advanced data mining algorithms to clinical datasets for the early detection and monitoring of conditions like schizophrenia, bipolar disorder, and depression.
• Neuroimaging and Neuroinformatics in AI/ML: Development of AI/ML algorithms to analyze neuroimaging data (e.g., MRI, PET scans) for better understanding of brain structures associated with neuropsychiatric disorders.
• Challenges and Ethical Considerations in AI/ML in Neuropsychiatry: Addressing issues related to data privacy, ethical implications, and bias in AI/ML models when applied to sensitive health data.

This Article Collection will appeal to researchers, clinicians, and technologists interested in how AI and ML can improve the diagnosis, treatment, and management of neuropsychiatric diseases.

Dr. Sathishkumar V Easwaramoorthy is a researcher and lecturer at Department of Computing and Information Systems in Sunway University, Malaysia. Over the past ten years, his research has centered around constructing predictive models using real-time datasets from various domains, including Intelligent Transportation Systems, Smart Farming, Smart Grids, and Healthcare. Currently, he is dedicated to developing applications within the Healthcare domain. Prior to this, he has been involved in the AI Convergence and Research project. His primary focus is on analyzing real-time datasets using Data Mining techniques, which involves the analysis of real-time data from multiple Korean data hubs to construct predictive models. His research interests encompass a wide range of topics, including Smart Farming, Cryptography, Biometric Technologies, Data Mining, Machine Learning, and Big Data Analytics. Parallel to his research, he is passionate about education, dedicated to imparting knowledge and technical skills to students, particularly in the domain of Data Science and related fields.

Prof. Vetriselvan Subramaniyan is a distinguished pharmacologist with over fifteen years of teaching and research experience across leading institutions in Malaysia, Ethiopia, and India. He has held key leadership positions, including Acting Dean and Deputy Dean, and has been involved in global health initiatives under the UNDP program. His research, highlighted by over 240 publications and eight international patents, has secured numerous prestigious grants. Dr. Vetriselvan is a fellow of multiple scientific societies, a keynote speaker, Ph.D. examiner, and visiting professor in India and China. He also serves as an overseas executive member of the Indian Pharmacological Society.

All manuscripts submitted to this Article Collection will undergo desk assessment and peer review as part of our standard editorial process. Guest Advisors for this collection will not be involved in peer-reviewing manuscripts unless they are existing members of the Editorial Board. Please review the journal Aims and Scope and Author Submission Instructions prior to submitting a manuscript.

Please submit your manuscript on our website, quoting the promo code CAMOU to indicate that your submission is for consideration in this Article Collection.

Please contact the Commissioning Editor, Rebecca Kearns at [email protected] with any queries and promo codes regarding this Article Collection.