Psychiatric disorders affect millions, yet diagnosis depends on subjective assessments and uneven access to care. To address these challenges, there is a growing need for Contestable AI (CAI), a framework that extends beyond Explainable AI (XAI) by allowing clinicians to inspect, question, and revise algorithmic outputs, thereby reducing automation bias and strengthening accountability. We present Heart2Mind 1, a human-centered CAI system for psychiatric disorder prediction that provides objective evidence while preserving clinical oversight. Heart2Mind collects R-R interval (RRI) time series from Polar H9/H10 wearable ECG sensors via a Cardiac Monitoring Interface and analyzes them using a Multi-Scale Temporal-Frequency Transformer (MSTFT) that combines time-domain and frequency-domain features. For contestability, the Contestable Diagnosis Interface integrates model explanations with dialogue. Self-Adversarial Explanations compare attention-based and gradient-based explanation maps to flag inconsistent predictions, and a collaboration chatbot helps users verify and challenge outputs. On the HRV-ACC dataset, MSTFT achieved 91. 7% accuracy under leave-one-out cross-validation, outperforming benchmark methods. Human-centered evaluation with the Human-CAI Consensus Rate showed experts and CAI could confirm correct decisions and correct errors through readable, efficient dialogues (\ (FKGL 15\), median 8. 3 minutes, 4 turns). These results support low-cost wearable CAI screening with objective biomarkers, safeguards, and an interactive path for clinicians to refine recommendations.
Nguyen et al. (Mon,) studied this question.