JMIR Mental Health

State-level regulation of AI used for mental health is emerging in the absence of a federal framework. States are taking different approaches to regulation, resulting in a fragmented regulatory landscape. This Viewpoint aims to identify the governance approaches that US states are using to regulate the use of AI in mental health and analyze the limitations of each. A 4-state case analysis was conducted using the statutory text of bills and laws in Illinois, Utah, New York, and Nevada. Two governance approaches were identified. The first regulates the use of AI in clinical contexts, and the second regulates the technology itself. Some states have combined elements of both approaches to address AI use more comprehensively. While these approaches aim to mitigate harm, they differ in where they believe risk lies in the use of AI for mental health support. The limitations of these divergent approaches include uneven protections for consumers and regulatory uncertainty for developers, vendors, deployers, and clinicians. Because AI in mental health operates across both clinical and consumer domains, neither approach alone can address the risks associated with its use for mental health support. A coordinated, risk-based federal regulatory floor is needed to ensure consistent protections across states.

Australia’s mental health care system has been characterized by complexity and fragmentation, as highlighted by numerous reports, commissions, and inquiries. In response, digital mental health care navigation tools have emerged as a promising solution to help individuals locate appropriate mental health services. The rapid proliferation of these tools—without a clear understanding of their definitions and characteristics—risks creating confusion rather than clarity for users. Terms such as “navigation” and “navigators” are often used interchangeably, further complicating the landscape.

Gambling disorder is associated with substantial psychiatric and functional burden, yet few individuals receive treatment. Limited real-world evidence exists evaluating outcomes of virtually delivered behavioral health care for gambling disorder, particularly among clinically complex patients.

Parents of very preterm infants admitted to the neonatal intensive care unit (NICU) experience high levels of psychological distress, yet access to timely, evidence-based mental health support is limited by staffing and resource constraints. Digital mental health interventions offer a scalable approach to addressing this gap; however, their effectiveness has not been well established in NICU caregiver populations, particularly during periods of acute stress.

Bipolar disorder (BD) is a complex and heterogeneous psychiatric condition, characterized by fluctuating clinical courses that affect approximately 1%‐2% of the global population in their lifetime. Despite pharmacological advances, treatment response varies significantly among patients, making the identification of individualized treatment strategies a major challenge. Artificial Intelligence (AI), through its classical approaches, has emerged as a powerful tool in precision psychiatry to identify subtle patterns in complex data and inform personalized clinical decisions.

The application of immersive technologies, particularly virtual reality, has expanded rapidly across health care domains, including mental health, rehabilitation, and education. These technologies enable the creation of controlled, interactive, and ecologically valid environments that can support therapeutic interventions, skill development, and behavioral assessment. Within forensic mental health services (FMHS) and prison settings, where individuals often present with complex psychological needs in restrictive and highly regulated environments, immersive technologies offer potential advantages such as safe simulation of real-world scenarios, enhanced engagement, and personalized intervention delivery. However, despite increasing interest, the evidence base remains fragmented, and questions persist regarding effectiveness, ethical implications, and feasibility of implementation in secure and resource-constrained contexts.

Temporal fluctuations in distress and suicidal ideation across daily, weekly, and seasonal cycles may influence the use and effectiveness of digital suicide prevention tools. Understanding patterns of app engagement, perceived suffering, and affective expression can inform the design of proactive, personalized digital interventions, thereby impacting adherence and efficacy.

Mobile mindfulness interventions have shown promise for reducing anxiety and depressive symptoms, but sustaining engagement remains a persistent challenge. Many digital programs still rely on formal practice that requires dedicated time, which may be difficult to integrate into daily life.

Digital cognitive interventions (DCIs) have emerged as scalable approaches for treating cognitive dysfunction across psychiatric, neurological, and aging populations. Despite growing evidence of efficacy, little is known about which intervention components drive therapeutic effects or through which neurocognitive mechanisms they operate. As a result, null findings are often difficult to interpret, making it unclear whether interventions failed to engage their intended targets, or whether the targets themselves are not causally related to meaningful outcomes. This limits intervention refinement, comparative evaluation, and precision personalization. Here, we argue that DCI research should shift from broad efficacy testing toward mechanistic trials designed to identify active ingredients—the intervention components responsible for engaging prespecified neurocognitive targets and producing clinically meaningful benefits. We propose adapting dismantling design methodology from psychotherapy research in order to integrate Research Domain Criteria constructs, mechanistic neuroscience, and high-resolution digital behavioral data to identify factors driving cognitive and functional outcomes. This approach aligns with the National Institute of Mental Health experimental therapeutics framework by explicitly linking target specification and target engagement with downstream clinical and functional outcomes. Mechanistic dismantling trials can determine whether specific DCI features, including adaptive difficulty, reward schedules, feedback contingencies, task variability, cognitive targets, and human support, are necessary, sufficient, or synergistic for engaging neural circuitry and producing durable and clinically meaningful transfer. Beyond optimizing intervention design, such studies may transform null or negative trials into mechanistically interpretable findings, while clarifying disease mechanisms and supporting the development of personalized, optimized, and usable DCIs.

The rapid evolution of large language models has accelerated the development of agentic artificial intelligence (AI) systems capable of pursuing autonomous goals, creating an urgent need for structural frameworks in psychiatry and psychotherapy. While existing classifications often draw parallels to autonomous driving, this paper argues that the mental health domain requires a distinct, domain-specific theoretical foundation, as the 2 domains differ fundamentally in their semantic, ideographic, and epistemological demands. Furthermore, they differ in their end goals, for which we introduce terms such as agentic guidance capability. To guide clinicians and researchers through these developments, we propose a 5-stage taxonomy for language-based AI systems that differentiates technical functionality from clinical effectiveness. The taxonomy progresses from level 1 (knowledge level), in which systems perform static benchmark tasks, to level 2 (elementary level), characterized by dynamic engagement in specific therapeutic microskills. At level 3 (integration level), systems achieve consistency across and within modules, as well as basic case-level conceptualization suitable for blended therapy under human oversight. Level 4 (saturation level) describes therapist-in-the-loop systems capable of autonomous functioning with minimal supervision, whereas level 5 (mastery level) represents AI systems that are technically capable of performing autonomous therapy. By distinguishing technical functionality from clinical effectiveness, we conclude that level 4 or level 5 performance does not automatically translate into full treatment effectiveness, even if high treatment fidelity can be achieved. We conclude by emphasizing the need to shift benchmarking from static knowledge tests to dynamic evaluations of therapeutic capabilities in order to safely navigate the transition toward autonomous care.

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In traditional human psychotherapy, the therapeutic alliance (TA) is regarded as a fundamental factor that describes the client-therapist relationship, mainly due to strong evidence demonstrating its impact on treatment outcomes regardless of theoretical orientation. More recently, advances in artificial intelligence (AI) and other technologies have led to the emergence of the concept of digital TA, used to characterize the relationship between clients and AI-based therapeutic systems. This approach replicates human dynamics but overlooks key differences between human therapists and digital agents. Prematurely translating the concept of TA into the digital context fails to address issues such as the sycophantic tendencies of current systems and the inherent limitations of algorithmic interaction. We propose the digital therapeutic nexus, a framework that recognizes these differences and provides a set of structured criteria for categorizing digital interactions into 3 progressive levels. This Viewpoint argues that only at the highest level can parallels be drawn to the human TA and stratifies the main risks associated with each nexus level. Transitioning from the concept of alliance to that of a nexus offers a more precise conceptual basis for describing and evaluating digital therapeutic relationships, with implications for research, design, and the ethical development of AI-based mental health interventions.