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Mental Health · 48 stories · page 1 of 4

Enhancing the Role of Digital Health Navigators With Lived Experience: AI-Assisted Conavigation for Safety, Support, and Engagement
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Enhancing the Role of Digital Health Navigators With Lived Experience: AI-Assisted Conavigation for Safety, Support, and Engagement

As artificial intelligence (AI) expands across mental health services, questions arise about whether it can replace digital health navigators (DHNs), including those with lived experience. The DHN role generally resists automation because lived experience deepens specific dimensions that AI cannot replicate. Drawing on emerging evidence of AI's limitations in contextual reasoning, cultural sensitivity, and clinical safety, the authors identify where DHNs with lived experience are least substitutable: real-world…

Psychiatric Services
OpenAI hack on Australian government reveals anxiety at heart of global artificial intelligence dilemma
Evidence-backed problem

OpenAI hack on Australian government reveals anxiety at heart of global artificial intelligence dilemma

When the Australian prime minister, Anthony Albanese, sat down for an interview in the heart of Silicon Valley at the weekend he had known for two days that his was the first government known to have been attacked by a rogue AI agent. He didn’t reveal the attack then, but he sounded a warning about the march of AI: “the risk is that AI develops in a way in which humans are no longer in control of what AI is producing.” But Albanese noted, too, that two countries – the US and China – would dominate the world’s re…

AI-Driven precision targeted therapy for EGFR-mutant non-small cell lung cancer: From therapeutic evolution and resistance mechanisms to intelligent decision systems
Evidence-backed problem

AI-Driven precision targeted therapy for EGFR-mutant non-small cell lung cancer: From therapeutic evolution and resistance mechanisms to intelligent decision systems

ObjectiveTo critically review the current pharmacologic treatment landscape for epidermal growth factor receptor (EGFR)-mutant non-small cell lung cancer (NSCLC), focusing on clinical pharmacology, therapeutic evolution, and emerging challenges of EGFR-targeted therapies.Data SourcesPeer-reviewed literature in PubMed, Web of Science, and Embase databases (January 2000 to June 2026) was searched using keywords including "EGFR-mutant NSCLC," "EGFR-TKIs," "drug resistance," "artificial intelligence," and "precision…

Methodological transitions in mental health research driven by machine learning
Evidence-backed gain

Methodological transitions in mental health research driven by machine learning

Machine learning is increasingly reshaping psychological and mental health research by complementing traditional theory-driven approaches with data-driven predictive modelling. This encompasses three key transitions: from hypothesis-testing to pattern discovery; from controlled experimental settings to naturalistic and multidimensional data ecosystems; and from explanatory theoretical models to prediction-informed theory-building and clinical translation. Nevertheless, this methodological reorientation remains i…

Serum Protein Glycopatterns as Biomarkers for Machine Learning Diagnosis of Major Depressive Disorder
Evidence-backed gain

Serum Protein Glycopatterns as Biomarkers for Machine Learning Diagnosis of Major Depressive Disorder

The diagnosis of major depressive disorder (MDD) currently relies on subjective clinical assessment, underscoring the need for objective biomarkers. Glycosylation, a common post-translational modification, is involved in neuroinflammation and immune regulation, both implicated in MDD pathogenesis. This study aimed to identify serum protein glycopatterns as biomarkers for MDD diagnosis and severity stratification. Serum samples from 150 individuals, including healthy volunteers (HV, n = 38), mild-to-moderate MDD…

Health

Development of an artificial intelligence model to estimate psychiatrist-assessed mental health-related presenteeism

Objectives This study aimed to contribute to the development of an AI-based system that supports worker health and productivity by enabling early detection of presenteeism. We tested whether an AI model could assess mental health-related presenteeism with accuracy comparable to that of psychiatrists and whether the frequency of application use was comparable between avatar-based and real-person interfaces. Methods This study aimed to design a multivariable prediction model among white-collar employees in a Japan…

Development of an artificial intelligence model to estimate psychiatrist-assessed mental health-related presenteeism
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Radiomics and dosiomics in radionecrosis prediction in brain metastasis treated with Stereotactic Radiation Therapy: a machine learning approach

Introduction Stereotactic Radiotherapy plays a main role in Brain Metastases treatment. Radiomics and Dosiomics, coupled with Machine Learning approaches are emerging in radiation oncology as support in clinical decision-making workflow. In this study a machine learning approach was used to predict the late toxicities induced by Stereotactic Radiotherapy including clinical, radiomics and dosiomics features extracted from patients. Materials and methods Lesions contribution, concomitant and/or sequential treatmen…

Radiomics and dosiomics in radionecrosis prediction in brain metastasis treated with Stereotactic Radiation Therapy: a machine learning approach
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Predicting early psychiatric readmission among people with major depressive disorder: A machine learning analysis from the prospective multicentre DEEP READ study

Background Major depressive disorder (MDD) is associated with high relapse rates, with around 20% of inpatients experiencing readmission within 90 days after discharge. Routinely collected clinical information may help identify individuals at higher risk of readmission. Machine learning (ML) models could complement more traditional statistical approaches by exploring complex relationships among multiple vulnerability factors. Methods The DEEP READ study is a prospective, multicentre cohort study conducted across…

Predicting early psychiatric readmission among people with major depressive disorder: A machine learning analysis from the prospective multicentre DEEP READ study
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Clinical usability of an explainable AI decision support tool and evaluation of multimodal models in NSCLC

Despite a decade in, immunotherapy (IO) treatment selection in non-small cell lung cancer (NSCLC) remains largely guided by subgroup analyses and imperfect programmed death ligand 1 (PD-L1) and clinical scores. To our knowledge, I 3 LUNG ( NCT05537922 ) is currently the largest international, real-world, multimodal, artificial intelligence (AI)-based study, enrolling 2,396 patients. We integrated real-world clinical and blood (CB) data, computed tomography (CT) images, digital pathology (DP), and genomics into m…

Clinical usability of an explainable AI decision support tool and evaluation of multimodal models in NSCLC
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Machine learning identifies depression risk in older adults with chronic diseases: Clarifying shared risk factors stratified by cognitive impairment status

Background The prevalence of depression is higher among older adults with chronic diseases and cognitive impairment than the general population. The comorbidity of cognitive impairment and chronic diseases significantly impacts the lives of these patients. This study aims to develop machine learning models to identify depression risk among older adults with chronic illnesses across different levels of cognitive impairment. Methods Data were derived from the Chinese Longitudinal Healthy Longevity Survey (n = 5798…

Machine learning identifies depression risk in older adults with chronic diseases: Clarifying shared risk factors stratified by cognitive impairment status
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Stakeholder perspectives on artificial intelligence in schizophrenia care

Background Artificial intelligence (AI) is explored as a tool to expand access to mental health care, offering support and self-disclosure. To date, the perspectives of individuals with schizophrenia on AI have been absent from the medical literature. This exploratory study represents an effort to report schizophrenia patient perspectives on AI chatbots through a focus group. Methods We conducted an exploratory, cross-sectional, qualitative study using semi-structured focus groups at a large academic health cent…

Stakeholder perspectives on artificial intelligence in schizophrenia care
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Predicting longitudinal depressive symptom trajectories among older Chinese adults with chronic health conditions: An interpretable machine learning study

Objective This study leveraged interpretable machine learning (ML) to map heterogeneous trajectories of depressive symptoms in Chinese older adults with chronic diseases, aiming to develop an interpretable, prediction-oriented framework for personalized mental health interventions. Methods We analyzed four-wave longitudinal data from 5492 participants in the China Health and Retirement Longitudinal Study. Following trajectory identification, 10 ML algorithms were compared. A 50-iteration bootstrap Recursive Feat…

Predicting longitudinal depressive symptom trajectories among older Chinese adults with chronic health conditions: An interpretable machine learning study