JMIR AI

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.

Large language model–based chatbots (LLM-CBs) are increasingly used as mental health support tools. Risks and harms are discussed especially for unsupervised use and for vulnerable groups. A naturalistic characterization of the use of LLM-CBs by patients with mental health disorders is currently lacking.

Adverse event reports referring to the same case but treated as independent can negatively impact statistical analysis and may mislead clinical assessment. Pharmacovigilance relies on large databases of adverse event reports to discover potential new causal associations. Their size necessitates computational methods to identify duplicates at scale. The current state-of-the-art is statistical record linkage which outperforms rule-based approaches. In particular, vigiMatch is in routine use for VigiBase, the World Health Organization global database of adverse event reports, and represents the first statistical duplicate detection approach in pharmacovigilance deployed at scale. Originally developed for both drugs and vaccines, its application to vaccines has been limited due to inconsistent performance across countries.

Symptom detection is essential in global disease surveillance to detect potential outbreaks, as symptoms are the first observable signs of infection. To reflect real-time ground truth conditions during pandemics, social media has emerged as a valuable data source. Moreover, effective digital disease surveillance systems must operate across diverse linguistic settings, and large language models (LLMs) have been shown to perform inconsistently across languages, tending to have lower performance in low-resource languages. While multilingual approaches have been explored in various health-related natural language processing tasks, a critical gap remains in understanding whether LLM-based symptom detection can perform consistently across languages for global disease surveillance. Southeast Asia demonstrates this challenge, combining diverse languages and the potential for emerging infectious disease outbreaks, making it a case for evaluating how multilingual performance disparities manifest in symptom detection.