Artificial intelligence (AI) is increasingly integrated into mental health care, legal decision-making, and forensic practice. Proponents argue that AI can augment professional judgment by improving prediction, efficiency, and consistency. Yet AI operates fundamentally differently from human reasoning: AI predicts, whereas clinicians and lawyers understand. Predictions alone cannot replace the human reasoning, contextualisation, and justification that clinical and legal decision-making require. This article examines four central tensions that arise when integrating AI into mental health and legal contexts: epistemic, ethical, relational, and legal tensions. These tensions illustrate the limits of purely algorithmic decision-making and the critical importance of maintaining meaningful human oversight. To assist clinicians, lawyers, and institutions navigate these challenges, the HUMAN framework provides a structured approach grounded in five core principles: Human judgment, Understand the model, Monitor performance, Accountability, and Narrative. Each component places human reasoning and ethical responsibility at the centre of AI-supported practice, ensuring that technological innovation strengthens rather than undermines the foundations of psychiatry and law. While AI may transform certain aspects of practice, human professionals retain overall responsibility, with particular emphasis on compassion, empathy, care, presence, and moral reasoning. Ultimately, these are the qualities that matter most.
Brendan D. Kelly (Thu,) studied this question.