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Related Concept Videos

Disorders of the Nervous Tissue01:28

Disorders of the Nervous Tissue

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Related Experiment Video

Updated: May 8, 2026

A Novel Experimental and Analytical Approach to the Multimodal Neural Decoding of Intent During Social Interaction in Freely-behaving Human Infants
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Applying artificial intelligence in neurodevelopmental disorders management and research.

Siham Mohamed1, Adam Ben-Jaafar2, Mabel Frimpong3

  • 1, London, UK.

European Journal of Medical Research
|January 4, 2026
PubMed
Summary

Artificial intelligence (AI) aids in diagnosing and treating neurodevelopmental disorders with machine learning and deep learning. AI shows promise as a clinical tool, but requires validation and ethical considerations for integration.

Keywords:
Artificial intelligenceComputational neuroscienceDeep learningMachine learningNeurodevelopmental disordersPaediatric neurology

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Area of Science:

  • Neuroscience and computational methods
  • Application of artificial intelligence in healthcare
  • Pediatric neurodevelopmental disorders research

Background:

  • Artificial intelligence (AI) is increasingly utilized in the diagnosis, treatment, and monitoring of neurodevelopmental disorders.
  • Traditional machine learning and advanced deep learning (DL) models offer distinct advantages in analyzing complex clinical and neuroimaging data.
  • Current AI applications aim for earlier detection, personalized interventions, and continuous patient support.

Purpose of the Study:

  • To review the current applications of AI in neurodevelopmental disorders.
  • To highlight the potential of AI as a clinical adjunct for improved patient care.
  • To identify challenges and future directions for AI integration in this field.

Main Methods:

  • Review of traditional machine-learning models (logistic regression, random forests, support vector machines) for interpretability and multimodal data integration.
  • Analysis of deep-learning (DL) approaches (CNNs, transformers) for neuroimaging and behavioral data analysis.
  • Exploration of AI-driven robotic platforms for therapeutic engagement and skill acquisition.

Main Results:

  • AI enables earlier detection, personalized interventions, and continuous support for neurodevelopmental disorders.
  • DL models enhance diagnostic and prognostic performance by improving analysis of neuroimaging and behavioral data.
  • AI-driven robotics show potential in enhancing therapeutic engagement and skill acquisition in children.

Conclusions:

  • AI tools have significant potential to augment clinical practice in neurodevelopmental disorders, rather than replace human clinicians.
  • Addressing challenges such as DL transparency, data privacy, algorithmic bias, and dataset diversity is crucial for widespread adoption.
  • Realizing AI's full potential necessitates rigorous validation, robust ethical safeguards, and seamless integration into human-led care pathways.