The advent of artificial intelligence (AI) has revolutionized neurological and neuropsychiatric research, offering powerful and efficient tools to analyze complex, multiomodal, and vast datasets that were previously intractable. Recent advancement of integrated Machine learning (ML) and deep learning (DL) algorithms have enabled researchers to uncover patterns and novel insights, advancing our understanding of neural mechanisms and wide range of disorders. This editorial introduces four recent research contributions that exemplify the transformative application of emerging AI-based technologies in this field, highlighting their substantial impact on diagnosis, treatment, and patient care. The finding of these advancements, and future work, will be an integral part of the future of personalized medicine, pushing the boundaries of what is possible in neurological and neuropsychiatric care.

Collectively, these articles highlight several emerging trends:

• Explainable and interpretable AI models for precision diagnosis: There is a shift toward using interpretable AI/ML models, enabling clinicians to understand and trust AI predictions. This is crucial for integrating AI tools into clinical practice.

• Data augmentation with generative AI: Generative models, such as GANs, offer solutions to data scarcity by generating high-quality synthetic data, enhancing model training, and addressing class imbalance in medical datasets.

• Multimodal biomarker integration: Combining different types of biomarkers (e.g., neuroimaging and biofluids) improves diagnostic accuracy by providing a more comprehensive view of the patient's condition.

• Practical and societal applications: Toward real-world, impactful applications of AI technologies, the contributing articles to this issue also emphasize on the growing need on developing monitoring tools for high-risk assessments.

• Advanced neural networks and multimodal fusion: The development of sophisticated neural networks and the fusion of multiple data modalities enhance the assessment of cognitive states, such as fatigue, leading to more accurate and reliable tools.

Despite significant progress, several challenges remain:

• Data quality and standardization: Ensuring high-quality data and developing standardized evaluation metrics are critical. Inconsistent data quality and data imbalance can hinder model performance and generalizability.

• Interpretability and trust: Complex AI models can be opaque, making it difficult for clinicians to interpret results. Emphasizing explainable AI is essential for clinical acceptance.

• Multimodal integration: Integrating various data sources necessitate advanced and novel data fusion algorithm. This in turn can complicate model development, increase the risk of overfitting, and the processing time.

• Interdisciplinary collaboration: Effective application of AI in neurology requires collaboration between AI specialists, neuroscientists, and clinicians to address practical clinical needs and ensure models are both technically sound and clinically relevant.

• Ethical considerations: Issues related to data privacy, consent, and algorithmic bias must be addressed to ethically implement AI technologies in healthcare.

Future research should focus on:

• Enhancing model robustness: Developing models that generalize across diverse populations and settings, possibly by incorporating longitudinal data and testing on real-world imbalanced datasets.

• Integrating advanced techniques: Exploring the combination of GANs with GCNs and self-attention mechanisms to improve the capture of complex neural patterns.

• Practical applications: Applying AI advancements to real-world scenarios, such as fatigue monitoring in transportation and occupational settings, to address pressing societal challenges.

The contributions in this special issue demonstrate the transformative potential of AI technologies in advancing neurological and neuropsychiatric research. By addressing complexities in data analysis, multi-information fusion, enhancing diagnostic accuracy, and providing objective assessments, these studies contribute valuable tools for improving patient care. The emerging trends emphasize the importance of explainable AI, data augmentation, multimodal integration, and advanced neural networks.

Moving forward, interdisciplinary collaboration and adherence to ethical standards will be essential in harnessing AI's full potential. By building on these foundations, future research can explore new frontiers, ultimately bridging the gap between technological innovation and real-world clinical application. The integration of AI into neurological research holds great promise for better understanding neural disorders and developing effective interventions that enhance patient outcomes.

AE: Conceptualization, Investigation, Project administration, Supervision, Writing – original draft, Writing – review & editing. FK: Conceptualization, Investigation, Project administration, Supervision, Writing – original draft, Writing – review & editing. ASo: Conceptualization, Investigation, Project administration, Supervision, Writing – original draft, Writing – review & editing. ASh: Conceptualization, Investigation, Project administration, Supervision, Writing – original draft, Writing – review & editing. ME: Conceptualization, Investigation, Project administration, Supervision, Writing – original draft, Writing – review & editing.

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

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