# Position: AI Will Transform Neuropsychology Through Mental Health Digital Twins for Dynamic Mental Health Care, Especially for ADHD

arXiv: https://arxiv.org/abs/2510.07409 · HTML: https://mlsanity.com/paper/2510.07409/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 2 of its 40 references that are in the map.
- First posted on arXiv 8 Oct 2025.

## Builds on (most cited first, 2 of 2)

- [Copiloting Diagnosis of Autism in Real Clinical Scenarios via LLMs](https://mlsanity.com/paper/2410.05684/) (2024)
- [Ethical Challenges and Evolving Strategies in the Integration of Artificial Intelligence into Clinical Practice](https://mlsanity.com/paper/2412.03576/) (2024)

## Abstract (arXiv)

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.