A Review of Challenges, Advancements, and AI-Driven Approaches for Mental Disorder ASD and ADHD Detection

  • Authors

    • Afsha Firdose Research Scholar, Vidyavardhaka College of Engineering, Mysuru, VTU, India
    • C Balarengadurai Professor, Vidyavardhaka College of Engineering, Mysuru, VTU, India
    • Boddu Ravi Prasad Professor, Marri Laxman Reddy Institute of Technology and Management, Hyderabad, India
    https://doi.org/10.14419/2e4wmv36

    Received date: May 7, 2025

    Accepted date: September 29, 2025

    Published date: October 15, 2025

  • AI-driven Mental Health Diagnosis, Multimodal Neuroimaging Analysis, Deep Learning in Psychiatry, ASD and ADHD Classification, Ethical AI in Mental Healthcare

  • Abstract

    Autism Spectrum Disorder (ASD) and attention deficit hyperactivity disorder (ADHD) are neurodevelopmental conditions that frequently co-occur with mental illnesses such as anxiety, depression, schizophrenia, and bipolar disorder. The complexity of these disorders, coupled with overlapping symptoms, presents significant challenges in accurate diagnosis and effective intervention. Traditional diagnostic methods rely on symptom-based assessments, which are often subjective and prone to misdiagnosis or underdiagnosis. In recent years, artificial intelligence (AI) and deep learning have emerged as promising tools for enhancing diagnostic precision by leveraging neuroimaging (MRI, fMRI), speech, and text-based data. This survey provides a comprehensive review of state-of-the-art AI-driven approaches for diagnosing ASD, ADHD, and associated mental illnesses. It explores the integration of multimodal data sources, discusses current challenges in AI-driven mental health assessments, and highlights ethical and privacy concerns. Furthermore, the paper identifies key research gaps, including the need for large-scale multimodal datasets, improved model interpretability, and real-world clinical validation. By bridging the gap between computational intelligence and clinical practice, this survey aims to pave the way for more accurate, scalable, and personalized mental health diagnostics, ultimately improving early detection and treatment outcomes for individuals with ASD, ADHD, and comorbid conditions.

  • References

    1. S. Albahri, R. A. Hamid, I. A. Zahid, M. E. Alqaysi, O. S. Albahri, and A. H. Alamoodi, "Artificial intelligence-based approaches for improving the diagnosis of Autism Spectrum Disorder: A comprehensive review," Artificial Intelligence Review, vol. 54, no. 3, pp. 1693–1720, Apr. 2021.
    2. J. Huynh et al., "AI may aid in diagnosing adolescents with ADHD," presented at the Radiological Society of North America Annual Meeting, Chicago, IL, USA, Nov. 2023.
    3. Integrating AI into ADHD and Autism Diagnostics, Qatar Biomedical Research Institute, Doha, Qatar, Dec. 2024.
    4. Brain Scans for Mental Illness: Advancements in Neuroimaging," NeuroLaunch, Sep. 2024.
    5. Advances in neuroimaging: Insights into neurological and psychiatric disease," BioMedical Engineering Online, vol. 18, no. 1, p. 20, Jan. 2019.
    6. Editorial: Recent developments in neuroimaging in mood disorders," Frontiers in Psychiatry, vol. 15, p. 1371347, 2024.
    7. “On future directions in neuroimaging for mental health," Neuropsychopharmacology, vol. 49, pp. 1–11, 2024.
    8. AI for ADHD: Opportunities and Challenges," Journal of Attention Disorders, vol. 27, no. 5, pp. 567–578, Apr. 2023.
    9. Revolutionizing attention deficit hyperactivity disorder with artificial intelligence: A comprehensive review," PLOS Mental Health, vol. 1, no. 3, p. e0000181, Dec. 2024.
    10. Utilizing Artificial Intelligence in the Diagnosis of Neuropsychiatric Disorders," Journal of Clinical and Diagnostic Research, vol. 17, no. 1, pp. 1–7, Jan. 2024.
    11. Patil M, Iftikhar N, Ganti L. Neuroimaging Insights into Autism Spectrum Disorder: Structural and Functional Brain. Health Psychology Research. 2024;12. doi:10.52965/001c.123439.
    12. Temur HO, Yurtsever I, Yesil G, Sharifov R, Yilmaz FT, Dundar TT, Alkan A. Correlation Between DTI Findings and Volume of Corpus Callo-sum in Children with AUTISM. Curr Med Imaging Rev. 2019;15(9):895-899. doi: 10.2174/1573405614666181005114315. PMID: 32008536.
    13. L. Zhang, J. -R. Wang and Y. Ma, "Graph Convolutional Networks via Low-Rank Subspace for Multi-Site rs-fMRI ASD Diagnosis," 2021 14th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI), Shanghai, China, 2021, pp. 1-6, doi: 10.1109/CISP-BMEI53629.2021.9624374.
    14. R. Haweel et al., "A Novel Framework for Grading Autism Severity Using Task-Based FMRI," 2020 IEEE 17th International Symposium on Bio-medical Imaging (ISBI), Iowa City, IA, USA, 2020, pp. 1404-1407, doi: 10.1109/ISBI45749.2020.9098430.
    15. W. Farzana, F. Sarker, T. Chau and K. A. Mamun, "Technological Evolvement in AAC Modalities to Foster Communications of Verbally Chal-lenged ASD Children: A Systematic Review," in IEEE Access, doi: 10.1109/ACCESS.2021.3055195.
    16. J. Shin, M. Maniruzzaman, Y. Uchida, M. A. M. Hasan, A. Megumi and A. Yasumura, "Handwriting-Based ADHD Detection for Children Having ASD Using Machine Learning Approaches," in IEEE Access, vol. 11, pp. 84974-84984, 2023, doi: 10.1109/ACCESS.2023.3302903.
    17. S. Ali et al., "A Preliminary Study on Effectiveness of a Standardized Multi-Robot Therapy for Improvement in Collaborative Multi-Human Inter-action of Children With ASD," in IEEE Access, vol. 8, pp. 109466-109474, 2020, doi: 10.1109/ACCESS.2020.3001365.
    18. O. Le Meur, A. Nebout, M. Cherel and E. Etchamendy, "From Kanner Austim to Asperger Syndromes, the Difficult Task to Predict Where ASD People Look at," in IEEE Access, vol. 8, pp. 162132-162140, 2020, doi: 10.1109/ACCESS.2020.3020251
    19. M. A. Rashidan et al., "Stimuli Video Quantification Based on Valence-Arousal Elicitation in Children With Autism Spectrum Disorder (ASD)," in IEEE Access, vol. 11, pp. 111142-111153, 2023, doi: 10.1109/ACCESS.2023.3321962.
    20. S. Ali et al., "An Adaptive Multi-Robot Therapy for Improving Joint Attention and Imitation of ASD Children," in IEEE Access, vol. 7, pp. 81808-81825, 2019, doi: 10.1109/ACCESS.2019.2923678.
    21. Poonam Chaudhary; Nikki Rani; Diksha Aggarwal; Srishti Sharma, "Temporal Normalization and Brain Image Analysis for Early‐Stage Prediction of Attention Deficit Hyperactivity Disorder (ADHD)," in Generative Artificial Intelligence for Biomedical and Smart Health Informatics , IEEE, 2025, pp.103-119, doi: 10.1002/9781394280735.ch6.
    22. N. K. S. Putri, U. L. Yuhana, D. O. Siahaan, W. Rahayu and E. Pardede, "Assistive Technology Classification for Students With Disabilities in Higher Education: A Systematic Literature Review," in IEEE Access, vol. 13, pp. 28135-28149, 2025, doi: 10.1109/ACCESS.2025.3538322.
    23. M. Hong, S. -Y. Dong, R. S. McIntyre, S. -K. Chiang and R. Ho, "fNIRS Classification of Adults With ADHD Enhanced by Feature Selection," in IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 33, pp. 220-231, 2025, doi: 10.1109/TNSRE.2024.3522121.
    24. N. Haroon, H. Jabbar, U. Shahbaz Khan, T. Ted. Jeong and N. Naseer, "Mental Fatigue Classification Aided by Machine Learning-Driven Model Under the Influence of Foot and Auditory Binaural Beats Brain Massages via fNIRS," in IEEE Access, vol. 12, pp. 187160-187191, 2024, doi: 10.1109/ACCESS.2024.3508875.
    25. C. Nash, R. Nair and S. M. Naqvi, "Insights Into Detecting Adult ADHD Symptoms Through Advanced Dual-Stream Machine Learning," in IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 32, pp. 3378-3387, 2024, doi: 10.1109/TNSRE.2024.3450848.
    26. Z. Xia, T. Zhou, Z. Jiao and J. Lu, "Learnable Brain Connectivity Structures for Identifying Neurological Disorders," in IEEE Transactions on Neu-ral Systems and Rehabilitation Engineering, vol. 32, pp. 3084-3094, 2024, doi: 10.1109/TNSRE.2024.3446588.
    27. J. E. W. Koh et al., ‘‘Automated classification of attention deficit hyperactivity disorder and conduct disorder using entropy features with ECG signals,’’ Comput. Biol. Med., vol. 140, Jan. 2022, Art. no. 105120.
    28. C. Nash, R. Nair, and S. M. Naqvi, ‘‘Machine learning and ADHD mental health detection—A short survey,’’ in Proc. 25th Int. Conf. Inf. Fusion (FUSION), Jul. 2022, pp. 1–8.
    29. T. Zhang, A. M. Schoene, S. Ji, and S. Ananiadou, ‘‘Natural language processing applied to mental illness detection: A narrative review,’’ NPJ Dig-it. Med., vol. 5, no. 1, p. 46, Apr. 2022.
    30. V. Pereira-Sanchez and F. X. Castellanos, ‘‘Neuroimaging in attentiondeficit/hyperactivity disorder,’’ Current Opinion Psychiatry, vol. 34, no. 2, p. 105, 2021.

  • Downloads

  • How to Cite

    Firdose, A. ., Balarengadurai, C. ., & Prasad, B. R. . (2025). A Review of Challenges, Advancements, and AI-Driven Approaches for Mental Disorder ASD and ADHD Detection. International Journal of Basic and Applied Sciences, 14(6), 296-302. https://doi.org/10.14419/2e4wmv36