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

Neil Natarajan, Sruthi Viswanathan, Xavier Roberts-Gaal, Michelle Marie Martel

TL;DR

The paper argues that mental health conditions are dynamic and that episodic diagnostics are ill-suited for conditions like ADHD. It proposes mental health digital twins (MHDTs) as continuously updated, multimodal models supervised by clinicians to support longitudinal diagnostic reconciliation. A concrete research agenda outlines validated intake probes, multimodal data fusion with uncertainty estimation, governance, and safety measures to operationalize MHDTs. Ultimately, the work envisions a shift from category-based diagnoses toward personalized, transdiagnostic models that optimize treatment outcomes and reduce access barriers.

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.

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

TL;DR

The paper argues that mental health conditions are dynamic and that episodic diagnostics are ill-suited for conditions like ADHD. It proposes mental health digital twins (MHDTs) as continuously updated, multimodal models supervised by clinicians to support longitudinal diagnostic reconciliation. A concrete research agenda outlines validated intake probes, multimodal data fusion with uncertainty estimation, governance, and safety measures to operationalize MHDTs. Ultimately, the work envisions a shift from category-based diagnoses toward personalized, transdiagnostic models that optimize treatment outcomes and reduce access barriers.

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.

Paper Structure

This paper contains 5 sections, 1 figure.

Table of Contents

  1. Introduction
  2. Limitations of Current Neuropsychological Assessment
  3. Dynamic Approaches to Diagnosis
  4. A Mental Health Digital Twin Research Agenda
  5. A Vision for Personalized Mental Health Models Beyond Diagnostic Categories

Figures (1)

  • Figure 1: Comparison of traditional episodic ADHD diagnostic process versus continuous AI-assisted diagnostic companion approach. The traditional model (top) shows discrete assessment points separated by long intervals, while the continuous model (bottom) demonstrates ongoing data collection, dynamic modeling, and iterative diagnostic refinement.