Artificial intelligence in intimate partner violence risk pathways: a PRISMA-ScR review of femicide prevention and medico-legal accountability.

Introduction: Artificial intelligence (AI), machine learning, natural language processing and related decision-support methods are increasingly studied in intimate partner violence (IPV), domestic-violence and gender-based violence contexts. The key question is not whether AI can predict femicide as an individual lethal event, but whether AI-related methods may help institutions recognise, document, communicate and act on distributed signs of escalation across clinical, legal, police, social-service and digital settings. Methods: This PRISMA-ScR scoping review, informed by Joanna Briggs Institute guidance and structured using the Population-Concept-Context framework, mapped English-language AI-related literature in IPV, domestic violence, coercive-control and femicide-related risk pathways. Sexual violence was included only when embedded in IPV, domestic-abuse, coercive-control, family-violence, lethality-risk or femicide-related pathways. Results: Searches identified 4,099 records; after deduplication, 2,906 were screened, 166 reports were assessed at full text and 125 were included in the core evidence map. The evidence was heterogeneous, spanning clinical and electronic health records, police narratives, legal documents, social media or online posts, survey data, linked administrative data and survivor-facing digital tools. AI-related methods were used mainly for detection, classification, record linkage, risk stratification, text mining, triage or decision support rather than for direct evaluation of femicide-prevention interventions. Femicide, lethality and severe escalation were addressed in only part of the corpus, and few studies examined implementation, human oversight, false reassurance, fairness, privacy or downstream institutional action in depth. Discussion: The findings do not support individual femicide prediction or demonstrate that AI prevents lethal violence. Instead, they support a more defensible role for AI as a bounded component in human-led risk-recognition pathways. The review develops a six-layer conceptual synthesis linking distributed risk signals, AI-assisted signal processing, human contextual review, multi-agency response, legal-ethical governance and medico-legal accountability. AI may support institutional recognition and coordination, but it cannot substitute for professional judgment, survivor-centred practice, due process or adequately resourced prevention systems.