From Explainability to Action: A Generative Operational Framework for Integrating XAI in Clinical Mental Health Screening

Current XAI tools fail to bridge the gap between technical accuracy and clinical usefulness: While techniques like SHAP and LIME successfully produce feature importance scores, they don't generate the actionable insights that clinicians need or the understandable explanations that patients require, creating a significant barrier to real-world adoption in mental health screening.

Large Language Models can serve as a "translation engine" between AI systems and clinicians: The paper proposes using LLMs to convert raw technical outputs from XAI tools into human-readable clinical narratives that are grounded in evidence-based guidelines, potentially solving the lab-to-clinic implementation gap.

The framework addresses multiple operational barriers simultaneously: Beyond just improving communication, the proposed system is designed to tackle workflow integration challenges, help mitigate bias in mental health screening, and tailor explanations for different stakeholders (clinicians vs. patients).

Moving from isolated data points to integrated narratives is essential for clinical adoption: The paper argues that the field needs to shift away from generating disconnected technical outputs toward delivering comprehensive, contextualized explanations that clinicians can actually use in their decision-making processes.

This represents a strategic shift in how we think about AI explainability in healthcare: Rather than focusing solely on making AI "transparent," the framework emphasizes making AI "operationally useful"—ensuring that explanations are not just technically faithful but also clinically relevant and actionable.