Background: Suicide remains a leading public health concern internationally, with measurable changes over recent years that highlight both progress and persistent vulnerabilities in mental health systems (National Institute of Mental Health, 2024; Saunders & Panchal, 2023). Concurrently, development and deployment of large language models (LLMs) and AI-augmented mental health applications are accelerating, producing a contested landscape of opportunity and risk for suicide prevention and mental healthcare broadly (Omar et al., 2024; Karabacak & Margetis, 2023).
Objective: This article synthesizes extant literature to construct a comprehensive, publication-ready research manuscript that: (1) examines how LLMs encode clinical knowledge and their potential utility for mental healthcare and suicide prevention (Singhal et al., 2023; Omar et al., 2024); (2) situates LLMs within persistent structural barriers to access and delivery of mental health services (Ziller, Anderson & Coburn, 2010; Donohue, Goetz & Song, 2024; Coombs et al., 2021); (3) articulates principal technical risks (hallucinations, dataset quality, domain drift) and governance challenges (Islam et al., 2025; Chen et al., 2024; Wettig et al., 2024); and (4) proposes a detailed, ethically grounded methodological framework for evaluation, validation, and staged integration of LLM-based tools into clinical and community settings.
Methods: We performed an integrative synthesis of the provided sources, mapping empirical evidence on suicide epidemiology and service access to contemporary technical literature on LLM capabilities, training-data concerns, and evaluation strategies. From this synthesis we derived a multi-modal research framework combining qualitative stakeholder inquiry, simulated and retrospective validation experiments, prospective safety trials, and continuous monitoring guided by hybrid human-AI oversight. Each element is detailed with operational procedures, measurement constructs, and ethical safeguards drawn from the literature.
Results: The synthesis reveals convergent themes: (1) suicide prevention needs precise, equitable, and accessible interventions; (2) LLMs exhibit surprising clinical pattern understanding but retain unpredictable failure modes and hallucinations; (3) disparities in access to care create both need and risk when AI systems are unevenly distributed or poorly validated in underserved populations; (4) robust evaluation requires domain-specific high-quality data, multi-language and demographic validation, human-feedback loops, and transparency metrics.
Conclusions: LLMs can augment suicide prevention and mental healthcare, but safe, equitable deployment requires methodical evaluation, domain-specific fine-tuning with quality-controlled data, human-in-the-loop safeguards, and policy frameworks to mitigate access-related harms. The proposed research framework operationalizes these requirements and outlines steps for translational research aimed at realizing benefits while minimizing risks.

large language models, suicide prevention, mental health access, hallucination

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