Position: AI Will Transform Neuropsychology Through Mental Health Digital Twins for Dynamic Mental Health Care, Especially for ADHD
- URL: http://arxiv.org/abs/2510.07409v1
- Date: Wed, 08 Oct 2025 18:06:15 GMT
- Title: Position: AI Will Transform Neuropsychology Through Mental Health Digital Twins for Dynamic Mental Health Care, Especially for ADHD
- Authors: Neil Natarajan, Sruthi Viswanathan, Xavier Roberts-Gaal, Michelle Marie Martel,
- Abstract summary: We advocate a shift from static mental health diagnostic assessments to continuous, artificial intelligence (AI)-driven assessment.<n>We focus on Attention-Deficit/Hyperactivity Disorder (ADHD) as a case study.<n>We propose the use of mental health digital twins (MHDTs) as a transformative framework for personalized mental health care.
- Score: 5.844783557050257
- License: http://creativecommons.org/licenses/by-nc-nd/4.0/
- Abstract: Static solutions don't serve a dynamic mind. Thus, we advocate a shift from static mental health diagnostic assessments to continuous, artificial intelligence (AI)-driven assessment. Focusing on Attention-Deficit/Hyperactivity Disorder (ADHD) as a case study, we explore how generative AI has the potential to address current capacity constraints in neuropsychology, potentially enabling more personalized and longitudinal care pathways. In particular, AI can efficiently conduct frequent, low-level experience sampling from patients and facilitate diagnostic reconciliation across care pathways. We envision a future where mental health care benefits from continuous, rich, and patient-centered data sampling to dynamically adapt to individual patient needs and evolving conditions, thereby improving both accessibility and efficacy of treatment. We further propose the use of mental health digital twins (MHDTs) - continuously updated computational models that capture individual symptom dynamics and trajectories - as a transformative framework for personalized mental health care. We ground this framework in empirical evidence and map out the research agenda required to refine and operationalize it.
Related papers
- Interpretable Neuropsychiatric Diagnosis via Concept-Guided Graph Neural Networks [56.75602443936853]
One in five adolescents currently live with a diagnosed mental or behavioral health condition, such as anxiety, depression, or conduct disorder.<n>While prior works use graph neural network (GNN) approaches for disorder prediction, they remain black-boxes, limiting their reliability and clinical translation.<n>In this work, we propose a concept-based diagnosis framework that that encodes interpretable functional connectivity concepts.<n>Our design ensures predictions through clinically meaningful connectivity patterns, enabling both interpretability and strong predictive performance.
arXiv Detail & Related papers (2025-10-02T19:38:46Z) - A Comprehensive Review of Datasets for Clinical Mental Health AI Systems [55.67299586253951]
We present the first comprehensive survey of clinical mental health datasets relevant to the training and development of AI-powered clinical assistants.<n>Our survey identifies critical gaps such as a lack of longitudinal data, limited cultural and linguistic representation, inconsistent collection and annotation standards, and a lack of modalities in synthetic data.
arXiv Detail & Related papers (2025-08-13T13:42:35Z) - MoodAngels: A Retrieval-augmented Multi-agent Framework for Psychiatry Diagnosis [58.67342568632529]
MoodAngels is the first specialized multi-agent framework for mood disorder diagnosis.<n>MoodSyn is an open-source dataset of 1,173 synthetic psychiatric cases.
arXiv Detail & Related papers (2025-06-04T09:18:25Z) - Towards Privacy-aware Mental Health AI Models: Advances, Challenges, and Opportunities [58.61680631581921]
Mental health disorders create profound personal and societal burdens, yet conventional diagnostics are resource-intensive and limit accessibility.<n>This paper examines these challenges and proposes solutions, including anonymization, synthetic data, and privacy-preserving training.<n>It aims to advance reliable, privacy-aware AI tools that support clinical decision-making and improve mental health outcomes.
arXiv Detail & Related papers (2025-02-01T15:10:02Z) - Artificial Intelligence in Mental Health and Well-Being: Evolution, Current Applications, Future Challenges, and Emerging Evidence [3.0655356440262334]
The paper discusses the evolution, present application, and future challenges in the field of AI for mental health and well-being.<n>The integration of AI in mental health has grown rapidly to augment traditional treatment and open innovative solutions.<n> Ethical challenges persist, however, most notably around privacy, data security, and algorithmic bias.
arXiv Detail & Related papers (2024-12-13T22:06:35Z) - MentalArena: Self-play Training of Language Models for Diagnosis and Treatment of Mental Health Disorders [59.515827458631975]
Mental health disorders are one of the most serious diseases in the world.<n>Privacy concerns limit the accessibility of personalized treatment data.<n>MentalArena is a self-play framework to train language models.
arXiv Detail & Related papers (2024-10-09T13:06:40Z) - Emotional Intelligence Through Artificial Intelligence : NLP and Deep Learning in the Analysis of Healthcare Texts [1.9374282535132377]
This manuscript presents a methodical examination of the utilization of Artificial Intelligence in the assessment of emotions in texts related to healthcare.
We scrutinize numerous research studies that employ AI to augment sentiment analysis, categorize emotions, and forecast patient outcomes.
There persist challenges, which encompass ensuring the ethical application of AI, safeguarding patient confidentiality, and addressing potential biases in algorithmic procedures.
arXiv Detail & Related papers (2024-03-14T15:58:13Z) - Generative AI-Driven Human Digital Twin in IoT-Healthcare: A Comprehensive Survey [53.691704671844406]
The Internet of things (IoT) can significantly enhance the quality of human life, specifically in healthcare.
The human digital twin (HDT) is proposed as an innovative paradigm that can comprehensively characterize the replication of the individual human body.
HDT is envisioned to empower IoT-healthcare beyond the application of healthcare monitoring by acting as a versatile and vivid human digital testbed.
Recently, generative artificial intelligence (GAI) may be a promising solution because it can leverage advanced AI algorithms to automatically create, manipulate, and modify valuable while diverse data.
arXiv Detail & Related papers (2024-01-22T03:17:41Z) - Empowering Psychotherapy with Large Language Models: Cognitive
Distortion Detection through Diagnosis of Thought Prompting [82.64015366154884]
We study the task of cognitive distortion detection and propose the Diagnosis of Thought (DoT) prompting.
DoT performs diagnosis on the patient's speech via three stages: subjectivity assessment to separate the facts and the thoughts; contrastive reasoning to elicit the reasoning processes supporting and contradicting the thoughts; and schema analysis to summarize the cognition schemas.
Experiments demonstrate that DoT obtains significant improvements over ChatGPT for cognitive distortion detection, while generating high-quality rationales approved by human experts.
arXiv Detail & Related papers (2023-10-11T02:47:21Z) - Behavior quantification as the missing link between fields: Tools for
digital psychiatry and their role in the future of neurobiology [0.0]
Current technologies are an exciting opportunity to improve behavioral characterization.
New capabilities for continuous collection of passive sensor streams, such as phone GPS or smartwatch accelerometers, open avenues of novel questioning.
There is huge potential in what can theoretically be captured by current technologies, but this in itself presents a large computational challenge.
arXiv Detail & Related papers (2023-05-24T17:45:10Z) - AI-Augmented Behavior Analysis for Children with Developmental
Disabilities: Building Towards Precision Treatment [2.0324247356209835]
We present an AI-Augmented Learning and Applied Behavior Analytics (AI-ABA) platform to provide personalized treatment and learning plans.
By defining systematic experiments along with automated data collection and analysis, AI-ABA can promote self-regulative behavior.
arXiv Detail & Related papers (2021-02-21T16:15:40Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.