Dr Rahul Khanna

Abstract

The integration of computational methods into psychiatry presents profound ethical challenges that extend beyond existing guidelines for AI and healthcare. While precision medicine and digital mental health tools offer transformative potential, they also raise concerns about privacy, algorithmic bias, transparency, and the erosion of clinical judgment. This article introduces the Integrated Ethical Approach for Computational Psychiatry (IEACP) framework, developed through a conceptual synthesis of 83 studies. The framework comprises five procedural stages – Identification, Analysis, Decision-making, Implementation, and Review – each informed by six core ethical values – beneficence, autonomy, justice, privacy, transparency, and scientific integrity. By systematically addressing ethical dilemmas inherent in computational psychiatry, the IEACP provides clinicians, researchers, and policymakers with structured decision-making processes that support patient-centered, culturally sensitive, and equitable AI implementation. Through case studies, we demonstrate framework adaptability to real-world applications, underscoring the necessity of ethical innovation alongside technological progress in psychiatric care.

It was a pleasure to be invited to speak with youth advocate, survivor, and budding psychologist Nikoletta in conversation with Professor Stephanie Brown. The Stronger Futures Centre for Research Excellence is an initiative to inspire, encourage and support clinical and population health researchers to work towards greater social inclusion and equity in research. Stephanie’s groundbreaking longitudinal studies have opened our eyes to the appalling prevalence and life-long outcomes of family violence. Together, we explored the opportunities to transform the system to break the cycle of intergenetional trauma.

It was a priviledge to provide some commentary to a piece about Veteran Missy’s battle with bipolar disorder for the women’s media company, Mamamia. Read the full article here.

Abstract

Background

Post-Traumatic Stress Disorder (PTSD) manifests heterogeneously across cognitive, affective, physiological, and behavioral domains. Current gold-standard assessment methods face scalability limitations, creating barriers to timely diagnosis and intervention. Digital assessment paradigms offer potential solutions, but their diagnostic accuracy and feasibility need validation, particularly for at-risk populations such as military and police personnel. Aims & Objectives: This study aimed to develop and validate a digital assessment paradigm for PTSD using a contemporary theoretical framework with scalable deployment potential. Secondary objectives included examining whether remote assessment approaches could replicate previously identified differences between PTSD and control groups, collectively determining which components provide the greatest diagnostic utility.

Method

We employed an exploratory cross-sectional multi-component design with 42 participants from Australian Defence Force and police backgrounds, validated against the CAPS-5 structured clinical interview. The paradigm incorporated cognitive tasks (dot probe, CRAFT), physiological assessments (heart rate, blood pressure, facial affect during trauma discussions and exposure), and longitudinal assessment (Ecological Momentary Assessment over four days). Data were analyzed using mixed-effects modeling to examine group differences across tasks, and machine learning classification to integrate multi-modal data for optimizing diagnostic accuracy. Models were evaluated with cross-validation techniques and interpretability analyses.

Results

Several components demonstrated significant diagnostic value. The CRAFT task revealed PTSD participants responded significantly faster to disgust and fear faces compared to controls. During trauma narrative discussions, those with PTSD showed distinct emotional expression patterns, particularly slower resolution of negative affect. Linguistic analysis showed PTSD narratives contained significantly more negative emotional content, first-person singular pronouns, and less lexical diversity. EMA data revealed consistently higher symptom levels in the PTSD group across all time points. The machine learning classifier achieved an AUC of 0.75 on the test set. EMA-derived features, particularly symptom volatility metrics, contributed approximately 60% of the model’s predictive power, followed by narrative (25%) and acoustic features (10%).

Discussion & Conclusions

This study demonstrates the feasibility and utility of a multi-modal digital assessment approach for PTSD. The strongest performing features were also those least burdensome participants. These findings suggest that scalable digital assessment paradigms may be able to effectively identify PTSD, potentially enabling earlier intervention. Future refinements should focus on optimizing the most diagnostically valuable components, particularly dynamic symptom monitoring and linguistic analysis, to develop lighter-weight assessment protocols suitable for frontline deployment.