Integrating explainable AI with clinical features to enhance ADHD diagnostic understanding.

ABSTRACT:

Introduction

Attention Deficit Hyperactivity Disorder (ADHD) in adults remains challenging to diagnose accurately, with over- and under-diagnosis common due to reliance on subjective clinical judgement. Although machine learning (ML) tools have shown promise in improving diagnostic accuracy, their limited transparency restricts clinical adoption. Existing research rarely integrates broad clinical, substance-use, and quality-of-life measures into a unified predictive framework, nor does it systematically compare explainable artificial intelligence (XAI) outputs with traditional statistical analyses.

Methods

We retrospectively analysed 786 anonymised adult assessments (January 2019-December 2024) from a UK specialist mental health service. The dataset included demographics; va

SUBMITTER: Shakeel HM 

PROVIDER: S-EPMC12690393 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature