JMIR AI

Large language models (LLMs) show promise for mental health counseling applications. However, their deployment faces significant challenges, including data privacy concerns requiring on-premise solutions, high operational costs, and Western-centric biases that limit cultural appropriateness for non–English-speaking populations.

The increasing availability of machine-readable research data has created a growing need for efficient large-scale data harmonization (DH). Although large language models (LLMs) show promise for reducing the time and labor required for DH, their effective integration into harmonization workflows remains an important methodological challenge.

Mentalization is a core human capacity involving interpretation of one’s own and others’ behavior in terms of underlying mental states. Within the mentalization-based treatment (MBT) framework, it is described along multiple dimensions, integrating cognitive, affective, relational, and regulatory processes. Large language models (LLMs) can generate linguistically reflective discourse, raising the question of whether the formal linguistic structure of mentalization can be reproduced without experiential and affective processes.

Depression is a common mental disorder that seriously affects mental health and quality of life. Traditional depression diagnosis mainly relies on clinical interviews and subjective assessments, which may lead to variability in diagnostic outcomes. Electroencephalogram (EEG)-based computer-aided diagnosis has emerged as a promising, objective approach for depression recognition. However, effectively modeling the complex nonlinear characteristics of EEG signals remains a major challenge for existing deep learning methods.

Identifying individuals on parole or probation can help inform care pathways and ensure continuity of care in health care systems. However, this sensitive justice-related information is often absent from structured electronic health record fields and is instead documented in narrative clinical notes. Developing automated parole and probation detection (PPD) systems typically requires expert-annotated clinical data, but manual annotation is costly, time-consuming, and constrained by data sparsity and privacy concerns.

Large language models (LLMs) are increasingly being integrated into clinical decision support, ranging from documentation aids to triage and patient communication. However, their behavior in ethically ambiguous medical situations is poorly characterized. Variability in how different LLMs balance approval of against refusal of a proposed action could materially shape the recommendations clinicians receive, yet no widely shared benchmark systematically quantifies this variability across contemporary model families.

Poststroke seizures (PSS) and poststroke epilepsy (PSE) are important neurological complications associated with poorer functional recovery, increased mortality, and reduced quality of life after stroke. Neuroimaging plays a central role in identifying patients at increased risk, and recent advances in AI have enabled the development of imaging-based prediction models that may improve individualized risk stratification. However, the current evidence remains fragmented, and the extent to which these approaches are applicable to African health care settings is unclear.

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AI has increasingly been used to generate synthetic images to overcome the scarcity of large, regulatory-compliant medical image datasets. However, their quality and actual utility for algorithm development across deep learning architectures, medical settings, and image types have been poorly assessed.

The proliferation of evaluative approaches for generative AI tools used for mental health purposes reflects much-needed attention to safety, ethics, and emerging risks. However, limited alignment across frameworks complicates comparisons, slows progress toward shared goals, and provides little clarity for regulatory processes. The result is a fragmented field that lacks cohesive guidance as the technology and its real-world patterns of use continue to evolve. This position paper synthesizes current evaluative approaches, outlines sources of divergence across existing frameworks, and identifies shared domains that could serve as anchors for coordinated development. We advocate for a living, flexible meta-framework maintained by a global, cross-sector consortium that can harmonize criteria, support consistent evaluation across jurisdictions, and adapt alongside scientific, clinical, and technological advances. Such a structure can strengthen safety, transparency, and methodological consistency while giving researchers, clinicians, developers, and policymakers a shared foundation for assessing generative AI tools in mental health care.

Measles is a major cause of death among infants despite free routine immunization (RI) in low- and middle-income countries (LMICs). Vaccination defaulting contributes to measles outbreak occurrences. Conventional immunization defaulter identification methods are expensive to implement, prone to data falsification, reactive, and overestimate uptake. Several machine learning studies leveraged the use of child and maternal characteristics as predictors of immunization defaulting, overlooking community, demographic, and socioeconomic factors. Similarly, there is limited research regarding the use of interpretable deep learning and advanced ensemble methods to improve model performance in predicting RI defaulters and establishing significant predictors at the national level in the LMIC context, like Uganda.