AI models may soon be poised to recommend mental health treatments or referrals in primary care, yet little is known regarding their impact on physician decision-making. In this web-based study, primary care physicians (n = 420) were presented with a clinical scenario describing a patient with psychiatric symptoms, an AI tool for referring or prescribing, and the recommendation of the AI. A sequentially randomized vignette method was used to test the impact of initial assessments and AI output on physician decision-making patterns. Physicians were significantly more likely to change their decisions when the AI recommendation was misaligned with their initial assessment, especially when AI recommended treatment. There was no difference between the change-in-decision rate of physicians who received an AI recommendation to not treat, indicating that the direction of AI recommendations may influence physician decision-making, and raising important considerations for how physician decisions may be anticipated in the context of AI.

Concerns regarding suicide rates and declining mental health among service members highlight the need for impactful approaches to address behavioral health needs of U.S. military populations and to improve force readiness. Research in civilian populations has revealed that artificial intelligence and machine learning (AI/ML) have the promise to advance behavioral health care in the following 6 domains: Education and Training, Screening and Assessment, Diagnosis, Treatment, Prognosis, and Clinical Documentation and Administrative Tasks.

We conducted a narrative review of research conducted in U.S. military populations, published between 2019 and 2024, that involved AI/ML in behavioral health. Studies were extracted from Embase, PubMed, PsycInfo, and Defense Technical Information Center. Nine studies were considered appropriate for the review.

Compared to research in civilian populations, there has been much less research in U.S. military populations regarding the use of AI/ML in behavioral health. The studies selected using ML have shown promise for screening and assessment, such as predicting negative mental health outcomes in military populations. ML has also been applied to diagnosis as well as prognosis, with initial positive results. More research is needed to validate the results of the studies reviewed.

There is potential for AI/ML to be applied more extensively to military behavioral health, including education/training, treatment, and clinical documentation/administrative tasks. The article describes challenges for further integration of AI into military behavioral health, considering perspectives of service members, providers, and system-level infrastructure.

As mental health disorders continue to surge, exceeding the capacity of available therapeutic resources, the emergence of technologies enabled by artificial intelligence (AI) offers promising solutions for supporting and delivering patient care. However, there is limited research on mental health practitioners' understanding, familiarity, and adoption intentions regarding these AI technologies. We, therefore, examined to what extent practitioners' characteristics are associated with their learning and use intentions of AI technologies in four application domains (diagnostics, treatment, feedback, and practice management). These characteristics include medical AI readiness with its subdimensions, AI anxiety with its subdimensions, technology self-efficacy, affinity for technology interaction, and professional identification.

Mixed-methods data from N = 392 German and US practitioners, encompassing psychotherapists (in training), psychiatrists, and clinical psychologists, was analyzed. A deductive thematic approach was employed to evaluate mental health practitioners' understanding and familiarity with AI technologies. Additionally, structural equation modeling (SEM) was used to examine the relationship between practitioners' characteristics and their adoption intentions for different technologies.

Qualitative analysis unveiled a substantial gap in familiarity with AI applications in mental healthcare among practitioners. While some practitioner characteristics were only associated with specific AI application areas (e.g., cognitive readiness with learning intentions for feedback tools), we found that learning intention, ethical knowledge, and affinity for technology interaction were relevant across all four application areas, underscoring their relevance in the adoption of AI technologies in mental healthcare.

In conclusion, this pre-registered study underscores the importance of recognizing the interplay between diverse factors for training opportunities and consequently, a streamlined implementation of AI-enabled technologies in mental healthcare.

Rates of childhood mental health problems are increasing in the UK. Early identification of childhood mental health problems is challenging but critical to children's future psychosocial development. This is particularly important for children with social care contact because earlier identification can facilitate earlier intervention. Clinical prediction tools could improve these early intervention efforts.

Characterise a novel cohort consisting of children in social care and develop effective machine learning models for prediction of childhood mental health problems.

We used linked, de-identified data from the Secure Anonymised Information Linkage Databank to create a cohort of 26 820 children in Wales, UK, receiving social care services. Integrating health, social care and education data, we developed several machine learning models aimed at predicting childhood mental health problems. We assessed the performance, interpretability and fairness of these models.

Risk factors strongly associated with childhood mental health problems included age, substance misuse and being a looked after child. The best-performing model, a gradient boosting classifier, achieved an area under the receiver operating characteristic curve of 0.75 (95% CI 0.73-0.78). Assessments of algorithmic fairness showed potential biases within these models.

Machine learning performance on this prediction task was promising. Predictive performance in social care settings can be bolstered by linking diverse routinely collected data-sets, making available a range of heterogenous risk factors relating to clinical, social and environmental exposures.

Artificial intelligence (AI) has potential in promoting and supporting self-management in patients with chronic conditions. However, the development and application of current AI technologies to meet patients' needs and improve their performance in chronic condition self-management tasks remain poorly understood. It is crucial to gather comprehensive information to guide the development and selection of effective AI solutions tailored for self-management in patients with chronic conditions.

This scoping review aimed to provide a comprehensive overview of AI applications for chronic condition self-management based on 3 essential self-management tasks, medical, behavioral, and emotional self-management, and to identify the current developmental stages and knowledge gaps of AI applications for chronic condition self-management.

A literature review was conducted for studies published in English between January 2011 and October 2024. In total, 4 databases, including PubMed, Web of Science, CINAHL, and PsycINFO, were searched using combined terms related to self-management and AI. The inclusion criteria included studies focused on the adult population with any type of chronic condition and AI technologies supporting self-management. This review was conducted following the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews) guidelines.

Of the 1873 articles retrieved from the search, 66 (3.5%) were eligible and included in this review. The most studied chronic condition was diabetes (20/66, 30%). Regarding self-management tasks, most studies aimed to support medical (45/66, 68%) or behavioral self-management (27/66, 41%), and fewer studies focused on emotional self-management (14/66, 21%). Conversational AI (21/66, 32%) and multiple machine learning algorithms (16/66, 24%) were the most used AI technologies. However, most AI technologies remained in the algorithm development (25/66, 38%) or early feasibility testing stages (25/66, 38%).

A variety of AI technologies have been developed and applied in chronic condition self-management, primarily for medication, symptoms, and lifestyle self-management. Fewer AI technologies were developed for emotional self-management tasks, and most AIs remained in the early developmental stages. More research is needed to generate evidence for integrating AI into chronic condition self-management to obtain optimal health outcomes.

Artificial intelligence (AI) could revolutionize the delivery of mental health care, helping to streamline clinician workflows and assist with diagnostic and treatment decisions. Yet, before AI can be integrated into practice, it is necessary to understand perspectives of these tools to inform facilitators and barriers to their uptake. We gathered data on clinician and community participant perspectives of incorporating AI in the clinical management of eating disorders.

A survey was distributed internationally to clinicians (n = 116) with experience in eating disorder treatment (psychologists, psychiatrists, etc.) and community participants (n = 155) who reported occurrence of eating disorder behaviors.

59% of clinicians reported use of AI systems (most commonly ChatGPT) for professional reasons, compared to 18% of community participants using them for help-related purposes. While more than half of clinicians (58%) and community participants (53%) were open for AI to help support them, fewer were enthusiastic about their integration (40% and 27%, respectively) and believed that they would significantly improve client outcomes (28% and 13%, respectively). Nine in 10 agreed that AI may be improperly used if individuals are not adequately trained, and could pose new data privacy and security concerns. Most agreed that AI will be convenient, beneficial for administrative tasks, and an avenue for continuous support, but will never outperform human clinicians on relational skills.

While many clinicians and community participants are open to the use of AI in eating disorder treatment and recognize its possible wide-ranging benefits, most remain cautious and uncertain about its implementation.

To rate the level of patients' satisfaction with responses on questions regarding mental health provided by human psychiatrists, pharmacists, and chatbot platforms.

This cross-sectional study enrolled 89 patients who were pharmacologically treated for their mental disorder in one institution in Croatia and one in Bosnia and Herzegovina during October 2023. They asked psychiatrists, pharmacists, ChatGPT, and one Croatian chatbot questions about their mental disorder and medications and rated the satisfaction with the responses.

Almost half of the patients had used ChatGPT before the study, and only 12.4% had used the Croatian platform. The patients were most satisfied with the information provided by psychiatrists (4.67 out of 5 about mental disorder and 4.51 about medications), followed by pharmacists (3.94 about medications), ChatGPT (3.66 about mental disorder and 3.45 about medications), and the Croatian platform (3.66 about mental disorder and 3.44 about medications). Almost half of the participants believed it was easier for them to put a question to a psychiatrist than to a chatbot, and only 10% claimed it was easier to ask ChatGPT.

Patients with mental health disorders were more satisfied with responses from their psychiatrists than from chatbots, and satisfaction with chatbots' knowledge on mental disorders and medications was still too low to justify their usage in these patients.

Suicide is among the leading causes of death worldwide and a concerning public health problem, accounting for over 700,000 registered deaths worldwide. However, suicide deaths are preventable with timely and evidence-based interventions, which are often low-cost. Suicidal tendencies range from passive thoughts to ideation and actions, with ideation strongly predicting suicide. However, current screening methods yield limited accuracy, impeding effective prevention. The primary goal of this study was to validate a machine-learning-based model for screening suicidality using indirect questions, developed on data collected during the early COVID-19 pandemic and to differentiate suicide risk among subgroups like age and gender. The detection of suicidal ideation (SI) was based on habits, demographic features, strategies for coping with stress, and satisfaction with three important aspects of life. The model performed on par with the earlier study, surprisingly generalizing well even with different characteristics of the underlying population, not showing any significant effect of the machine learning drift. The sample of 1199 respondents reported an 18.6% prevalence of SI in the past month. The presented model for indirect suicidality screening has proven its validity in different circumstances and timeframes, emphasizing its potential as a tool for suicide prevention and intervention in the general population.

Chat-based counseling services are popular for the low-threshold provision of mental health support to youth. In addition, they are particularly suitable for the utilization of natural language processing (NLP) for improved provision of care.

Consequently, this paper evaluates the feasibility of such a use case, namely, the NLP-based automated evaluation of satisfaction with the chat interaction. This preregistered approach could be used for evaluation and quality control procedures, as it is particularly relevant for those services.

The consultations of 2609 young chatters (around 140,000 messages) and corresponding feedback were used to train and evaluate classifiers to predict whether a chat was perceived as helpful or not. On the one hand, we trained a word vectorizer in combination with an extreme gradient boosting (XGBoost) classifier, applying cross-validation and extensive hyperparameter tuning. On the other hand, we trained several transformer-based models, comparing model types, preprocessing, and over- and undersampling techniques. For both model types, we selected the best-performing approach on the training set for a final performance evaluation on the 522 users in the final test set.

The fine-tuned XGBoost classifier achieved an area under the receiver operating characteristic score of 0.69 (P<.001), as well as a Matthews correlation coefficient of 0.25 on the previously unseen test set. The selected Longformer-based model did not outperform this baseline, scoring 0.68 (P=.69). A Shapley additive explanations explainability approach suggested that help seekers rating a consultation as helpful commonly expressed their satisfaction already within the conversation. In contrast, the rejection of offered exercises predicted perceived unhelpfulness.

Chat conversations include relevant information regarding the perceived quality of an interaction that can be used by NLP-based prediction approaches. However, to determine if the moderate predictive performance translates into meaningful service improvements requires randomized trials. Further, our results highlight the relevance of contrasting pretrained models with simpler baselines to avoid the implementation of unnecessarily complex models.

Open Science Framework SR4Q9; https://osf.io/sr4q9.

This study examined mental health disparities among African Americans using AI and machine learning for outcome prediction. Analyzing data from African American adults (18-85) in Southeastern Virginia (2016-2020), we found Mood Affective Disorders were most prevalent (41.66%), followed by Schizophrenia Spectrum and Other Psychotic Disorders. Females predominantly experienced mood disorders, with patient ages typically ranging from late thirties to mid-forties. Medicare coverage was notably high among schizophrenia patients, while emergency admissions and comorbidities significantly impacted total healthcare charges. Machine learning models, including gradient boosting, random forest, neural networks, logistic regression, and Naive Bayes, were validated through 100 repeated 5-fold cross-validations. Gradient boosting demonstrated superior predictive performance among all models. Nomograms were developed to visualize risk factors, with gender, age, comorbidities, and insurance type emerging as key predictors. The study revealed higher mental health disorder prevalence compared to national averages, suggesting a potentially greater mental health burden in this population. Despite the limitations of its retrospective design and regional focus, this research provides valuable insights into mental health disparities among African Americans in Southeastern Virginia, particularly regarding demographic and clinical risk factors.

This review explores the transformative potential of Artificial Intelligence (AI) in the light of evolving threats to planetary health, particularly the dangers posed by the climate crisis and its emerging mental health impacts, in the context of a climate-vulnerable country such as the Philippines. This paper describes the country's mental health system, outlines the chronic systemic challenges that it faces, and discusses the intensifying and widening impacts of climate change on mental health. Integrated mental healthcare must be part of the climate adaptation response, particularly for vulnerable populations. AI holds promise for mental healthcare in the Philippines, and be a tool that can potentially aid in addressing the shortage of mental health professionals, improve service accessibility, and provide direct services in climate-affected communities. However, the incorporation of AI into mental healthcare also presents significant challenges, such as potentially worsening the existing mental health inequities due to unequal access to resources and technologies, data privacy concerns, and potential AI algorithm biases. It is crucial to approach AI integration with ethical consideration and responsible implementation to harness its benefits, mitigate potential risks, and ensure inclusivity in mental healthcare delivery, especially in the era of a warming planet.

This article explores the ethical issues arising from ordinary AI applications currently used in mental health care, rather than speculative future scenarios. AI tools are already in use for a variety of purposes, including data collection for screening and intake, documentation, decision support, non-clinical support, and, in limited cases, adjunctive treatment. After reviewing the range of and distinctions between those applications, including when those distinctions become blurred, the article discusses selected ethical considerations. The use of AI in psychiatry raises issues related to reflective practice, the seductive allure of AI, varieties of bias, data security, and liability. These examples highlight how seemingly simple AI applications can still present significant ethical implications, suggesting practical considerations for clinicians, professional organizations, treatment organizations, training programs, and policymakers.

Recent advancements in artificial intelligence (AI) have changed the care processes in mental health, particularly in decision-making support for health care professionals and individuals with mental health problems. AI systems provide support in several domains of mental health, including early detection, diagnostics, treatment, and self-care. The use of AI systems in care flows faces several challenges in relation to decision-making support, stemming from technology, end-user, and organizational perspectives with the AI disruption of care processes.

This study aims to explore the use of AI systems in mental health to support decision-making, focusing on 3 key areas: the characteristics of research on AI systems in mental health; the current applications, decisions, end users, and user flow of AI systems to support decision-making; and the evaluation of AI systems for the implementation of decision-making support, including elements influencing the long-term use.

A scoping review of empirical evidence was conducted across 5 databases: PubMed, Scopus, PsycINFO, Web of Science, and CINAHL. The searches were restricted to peer-reviewed articles published in English after 2011. The initial screening at the title and abstract level was conducted by 2 reviewers, followed by full-text screening based on the inclusion criteria. Data were then charted and prepared for data analysis.

Of a total of 1217 articles, 12 (0.99%) met the inclusion criteria. These studies predominantly originated from high-income countries. The AI systems were used in health care, self-care, and hybrid care contexts, addressing a variety of mental health problems. Three types of AI systems were identified in terms of decision-making support: diagnostic and predictive AI, treatment selection AI, and self-help AI. The dynamics of the type of end-user interaction and system design were diverse in complexity for the integration and use of the AI systems to support decision-making in care processes. The evaluation of the use of AI systems highlighted several challenges impacting the implementation and functionality of the AI systems in care processes, including factors affecting accuracy, increase of demand, trustworthiness, patient-physician communication, and engagement with the AI systems.

The design, development, and implementation of AI systems to support decision-making present substantial challenges for the sustainable use of this technology in care processes. The empirical evidence shows that the evaluation of the use of AI systems in mental health is still in its early stages, with need for more empirically focused research on real-world use. The key aspects requiring further investigation include the evaluation of the use of AI-supported decision-making from human-AI interaction and human-computer interaction perspectives, longitudinal implementation studies of AI systems in mental health to assess the use, and the integration of shared decision-making in AI systems.

Autism Spectrum Disorder (ASD) affects millions of individuals worldwide, presenting challenges in social communication, repetitive behaviors, and sensory processing. Despite its prevalence, diagnosis can be lengthy, and access to appropriate treatment varies greatly. This project utilizes the power of Artificial Intelligence (AI), particularly Machine Learning (ML) and Deep Learning (DL), to improve Autism Spectrum Disorder diagnosis and treatment. A central data hub, the Master Data Plan (MDP), will aggregate and analyze information from diverse sources, feeding AI algorithms that can identify risk factors for ASD, personalize treatment plans based on individual needs, and even predict potential relapses. Furthermore, the project incorporates a patient-facing chatbot to provide information and support. By integrating patient data, empowering individuals with ASD, and supporting healthcare professionals, this platform aims to transform care accessibility, personalize treatment approaches, and optimize the entire care journey. Rigorous data governance measures will ensure ethical and secure data management. This project will improve access to care, personalize treatments for better outcomes, shorten wait times, boost patient involvement, and raise ASD awareness, leading to better resource allocation. This project marks a transformative shift toward data-driven, patient-centred ASD care in Italy. This platform enhances treatment outcomes for individuals with ASD and provides a scalable model for integrating AI into mental health, establishing a new benchmark for personalized patient care. Through AI integration and collaborative efforts, it aims to redefine mental healthcare standards, enhancing the well-being for individuals with ASD.

In this editorial, I comment on the article by Zhang et al. To emphasize the importance of the topic, I discuss the relationship between the use of smart medical devices and mental health. Smart medical services have the potential to positively influence mental health by providing monitoring, insights, and interventions. However, they also come with challenges that need to be addressed. Understanding the primary purpose for which individuals use these smart technologies is essential to tailoring them to specific mental health needs and preferences.

Individuals in the veterinary profession are experiencing significant mental health and wellbeing challenges. A holistic view of wellbeing, which encompasses both physical and mental health, underscores their interconnected nature. This integrated approach reduces the artificial separation of wellbeing facets, and highlights how mental states influence not only individuals, but also their interactions with animals, the environment, and others in the workplace. Wellbeing challenges in veterinary medicine may contribute to negative impacts in animal, human, and environmental health. Veterinary education institutions and systems are also experiencing complex challenges as they adapt to rapidly changing societal, workforce, and professional wellbeing related pressures. This review paper explores the field of positive psychology and its application in educational contexts, commonly known as positive education. A thorough exploration of the systems-informed positive education approach and ways in which it can proactively enhance veterinary professional wellbeing from within the veterinary education ecosystem are presented. It is important to recognize that individual self-care, while valuable, cannot compensate for systemic dysfunctions such as poor team dynamics, ineffective leadership, or organizational culture issues. Addressing these systemic factors is critical for creating environments that support sustained flourishing. Positive psychology interventions delivered through the pathways of individuals, groups, and organizations specifically within a veterinary education context are discussed. Limitations, considerations, and proposed measurement strategies are reviewed. The implications of implementing a systems-informed positive psychology approach to enhance wellbeing in veterinary education include creating curriculum and cultures that enable flourishing within veterinary education institutions. Strengthening the individual and collective wellbeing of veterinary professionals has the potential to enhance the quality of care provided to animals, which has myriad positive implications for animal caregivers, their communities, the environment, and society.

Borderline Personality Disorder (BPD), impacting approximately 2% of adults worldwide, presents a formidable challenge in psychiatric diagnostics. Often underdiagnosed or misdiagnosed, BPD is associated with high morbidity and mortality. This scoping review embarks on a comprehensive exploration of observable cues in BPD, encompassing language patterns, speech nuances, facial expressions, nonverbal communication, and physiological measurements. The findings unveil distinctive features within the BPD population, including language patterns emphasizing external viewpoints and future tense, specific linguistic characteristics, and unique nonverbal behaviors. Physiological measurements contribute to this exploration, shedding light on emotional responses and physiological arousal in individuals with BPD. These cues offer the potential to enhance diagnostic accuracy and complement existing diagnostic methods, enabling early identification and management in response to the urgent need for precise psychiatric care in the digital era. By serving as possible digital biomarkers, they could provide objective, accessible, and stress-reducing assessments, representing a significant leap towards improved psychiatric assessments and an invaluable contribution to the field of precision psychiatry.

This review aims to evaluate the current psychiatric applications and limitations of machine learning (ML), defined as techniques used to train algorithms to improve performance at a task based on data. The review emphasizes the clinician's role in ensuring equitable and effective patient care and seeks to inform mental health providers about the importance of clinician involvement in these technologies.

ML in psychiatry has advanced through electronic health record integration, disease phenotyping, and remote monitoring through mobile applications. However, these applications face challenges related to health equity, privacy, translation to practice, and validation. Clinicians play crucial roles in ensuring data quality, mitigating biases, promoting algorithm transparency, guiding clinical implementation, and advocating for ethical and patient-centered use of ML tools. Clinicians are essential in addressing the challenges of ML, ensuring its ethical application, and promoting equitable care, thus improving the effectiveness of ML in practice.

Mental health disorders are currently the main contributor to poor quality of life and years lived with disability. Symptoms common to many mental health disorders lead to impairments or changes in the use of language, which are observable in the routine use of social media. Detection of these linguistic cues has been explored throughout the last quarter century, but interest and methodological development have burgeoned following the COVID-19 pandemic. The next decade may see the development of reliable methods for predicting mental health status using social media data. This might have implications for clinical practice and public health policy, particularly in the context of early intervention in mental health care.

This study aims to examine the state of the art in methods for predicting mental health statuses of social media users. Our focus is the development of artificial intelligence-driven methods, particularly natural language processing, for analyzing large volumes of written text. This study details constraints affecting research in this area. These include the dearth of high-quality public datasets for methodological benchmarking and the need to adopt ethical and privacy frameworks acknowledging the stigma experienced by those with a mental illness.

A Google Scholar search yielded peer-reviewed articles dated between 1999 and 2024. We manually grouped the articles by 4 primary areas of interest: datasets on social media and mental health, methods for predicting mental health status, longitudinal analyses of mental health, and ethical aspects of the data and analysis of mental health. Selected articles from these groups formed our narrative review.

Larger datasets with precise dates of participants' diagnoses are needed to support the development of methods for predicting mental health status, particularly in severe disorders such as schizophrenia. Inviting users to donate their social media data for research purposes could help overcome widespread ethical and privacy concerns. In any event, multimodal methods for predicting mental health status appear likely to provide advancements that may not be achievable using natural language processing alone.

Multimodal methods for predicting mental health status from voice, image, and video-based social media data need to be further developed before they may be considered for adoption in health care, medical support, or as consumer-facing products. Such methods are likely to garner greater public confidence in their efficacy than those that rely on text alone. To achieve this, more high-quality social media datasets need to be made available and privacy concerns regarding the use of these data must be formally addressed. A social media platform feature that invites users to share their data upon publication is a possible solution. Finally, a review of literature studying the effects of social media use on a user's depression and anxiety is merited.

Deep Reinforcement Learning is a branch of artificial intelligence that uses artificial neural networks to model reward-based learning as it occurs in biological agents. Here we modify a Deep Reinforcement Learning approach by imposing a suppressive effect on the connections between neurons in the artificial network-simulating the effect of dendritic spine loss as observed in major depressive disorder (MDD). Surprisingly, this simulated spine loss is sufficient to induce a variety of MDD-like behaviors in the artificially intelligent agent, including anhedonia, increased temporal discounting, avoidance, and an altered exploration/exploitation balance. Furthermore, simulating alternative and longstanding reward-processing-centric conceptions of MDD (dysfunction of the dopamine system, altered reward discounting, context-dependent learning rates, increased exploration) does not produce the same range of MDD-like behaviors. These results support a conceptual model of MDD as a reduction of brain connectivity (and thus information-processing capacity) rather than an imbalance in monoamines-though the computational model suggests a possible explanation for the dysfunction of dopamine systems in MDD. Reversing the spine-loss effect in our computational MDD model can lead to rescue of rewarding behavior under some conditions. This supports the search for treatments that increase plasticity and synaptogenesis, and the model suggests some implications for their effective administration.

Artificial intelligence (AI) has been increasingly recognized as a potential solution to address mental health service challenges by automating tasks and providing new forms of support.

This study is the first in a series which aims to estimate the current rates of AI technology use as well as perceived benefits, harms, and risks experienced by community members (CMs) and mental health professionals (MHPs).

This study involved 2 web-based surveys conducted in Australia. The surveys collected data on demographics, technology comfort, attitudes toward AI, specific AI use cases, and experiences of benefits and harms from AI use. Descriptive statistics were calculated, and thematic analysis of open-ended responses were conducted.

The final sample consisted of 107 CMs and 86 MHPs. General attitudes toward AI varied, with CMs reporting neutral and MHPs reporting more positive attitudes. Regarding AI usage, 28% (30/108) of CMs used AI, primarily for quick support (18/30, 60%) and as a personal therapist (14/30, 47%). Among MHPs, 43% (37/86) used AI; mostly for research (24/37, 65%) and report writing (20/37, 54%). While the majority found AI to be generally beneficial (23/30, 77% of CMs and 34/37, 92% of MHPs), specific harms and concerns were experienced by 47% (14/30) of CMs and 51% (19/37) of MHPs. There was an equal mix of positive and negative sentiment toward the future of AI in mental health care in open feedback.

Commercial AI tools are increasingly being used by CMs and MHPs. Respondents believe AI will offer future advantages for mental health care in terms of accessibility, cost reduction, personalization, and work efficiency. However, they were equally concerned about reducing human connection, ethics, privacy and regulation, medical errors, potential for misuse, and data security. Despite the immense potential, integration into mental health systems must be approached with caution, addressing legal and ethical concerns while developing safeguards to mitigate potential harms. Future surveys are planned to track use and acceptability of AI and associated issues over time.

Objective: We analyzed a natural language processing (NLP) toolkit's ability to classify unstructured EHR data by psychiatric diagnosis. Expertise can be a barrier to using NLP. We employed an NLP toolkit (CLARK) created to support studies led by investigators with a range of informatics knowledge. Methods: The EHR of 652 patients were manually reviewed to establish Depression and Substance Use Disorder (SUD) labeled datasets, which were split into training and evaluation datasets. We used CLARK to train depression and SUD classification models using training datasets; model performance was analyzed against evaluation datasets. Results: The depression model accurately classified 69% of records (sensitivity = 0.68, specificity = 0.70, F1 = 0.68). The SUD model accurately classified 84% of records (sensitivity = 0.56, specificity = 0.92, F1 = 0.57). Conclusion: The depression model performed a more balanced job, while the SUD model's high specificity was paired with a low sensitivity. NLP applications may be especially helpful when combined with a confidence threshold for manual review.

Neuropsychiatric disorders (NPDs) pose a substantial burden on the healthcare system. The major challenge in diagnosing NPDs is the subjective assessment by the physician which can lead to inaccurate and delayed diagnosis. Recent studies have depicted that the integration of artificial intelligence (AI) in neuropsychiatry could potentially revolutionize the field by precisely diagnosing complex neurological and mental health disorders in a timely fashion and providing individualized management strategies. In this narrative review, the authors have examined the current status of AI tools in assessing neuropsychiatric disorders and evaluated their validity and reliability in the existing literature. The analysis of various datasets including MRI scans, EEG, facial expressions, social media posts, texts, and laboratory samples in the accurate diagnosis of neuropsychiatric conditions using machine learning has been profoundly explored in this article. The recent trials and tribulations in various neuropsychiatric disorders encouraging future scope in the utility and application of AI have been discussed. Overall machine learning has proved to be feasible and applicable in the field of neuropsychiatry and it is about time that research translates to clinical settings for favorable patient outcomes. Future trials should focus on presenting higher quality evidence for superior adaptability and establish guidelines for healthcare providers to maintain standards.

Poor air quality can both trigger and aggravate lung and heart conditions, as well as affecting child development. It can even lead to neurological and mental health problems. However, the precise mechanisms by which air pollution affect human health are not well understood.

To promote interdisciplinary dialogue and better research based on a critical summary of evidence on air quality and health, with an emphasis on mental health, and to do so with a special focus on bioaerosols as a common but neglected air constituent.

A rapid narrative review and interdisciplinary expert consultation, as is recommended for a complex and rapidly changing field of research.

The research methods used to assess exposures and outcomes vary across different fields of study, resulting in a disconnect in bioaerosol and health research. We make recommendations to enhance the evidence base by standardising measures of exposure to both particulate matter in general and bioaerosols specifically. We present methods for assessing mental health and ideal designs. There is less research on bioaerosols, and we provide specific ways of measuring exposure to these. We suggest research designs for investigating causal mechanisms as important intermediate steps before undertaking larger-scale and definitive studies.

We propose methods for exposure and outcome measurement, as well as optimal research designs to inform the development of standards for undertaking and reporting research and for future policy.

This article contends that the responsible artificial intelligence (AI) approach-which is the dominant ethics approach ruling most regulatory and ethical guidance-falls short because it overlooks the impact of AI on human relationships. Focusing only on responsible AI principles reinforces a narrow concept of accountability and responsibility of companies developing AI. This article proposes that applying the ethics of care approach to AI regulation can offer a more comprehensive regulatory and ethical framework that addresses AI's impact on human relationships. This dual approach is essential for the effective regulation of AI in the domain of mental health care. The article delves into the emergence of the new "therapeutic" area facilitated by AI-based bots, which operate without a therapist. The article highlights the difficulties involved, mainly the absence of a defined duty of care toward users, and shows how implementing ethics of care can establish clear responsibilities for developers. It also sheds light on the potential for emotional manipulation and the risks involved. In conclusion, the article proposes a series of considerations grounded in the ethics of care for the developmental process of AI-powered therapeutic tools.

Diagnostic and interventional aspects of psychiatric care can be augmented by the use of digital health technologies. Recent studies have tried to explore the use of artificial intelligence-driven technologies in screening, diagnosing, and treating psychiatric disorders. This short communication presents a current perspective on using Artificial Intelligence in psychiatry.

Given that mental health problems in adolescence may have lifelong impacts, the role of primary care physicians (PCPs) in identifying and managing these issues is important. Artificial Intelligence (AI) may offer solutions to the current challenges involved in mental health care. We therefore explored PCPs' challenges in addressing adolescents' mental health, along with their attitudes towards using AI to assist them in their tasks.

We used purposeful sampling to recruit PCPs for a virtual Focus Group (FG). The virtual FG lasted 75 minutes and was moderated by two facilitators. A life transcription was produced by an online meeting software. Transcribed data was cleaned, followed by a priori and inductive coding and thematic analysis.

We reached out to 35 potential participants via email. Seven agreed to participate, and ultimately four took part in the FG. PCPs perceived that AI systems have the potential to be cost-effective, credible, and useful in collecting large amounts of patients' data, and relatively credible. They envisioned AI assisting with tasks such as diagnoses and establishing treatment plans. However, they feared that reliance on AI might result in a loss of clinical competency. PCPs wanted AI systems to be user-friendly, and they were willing to assist in achieving this goal if it was within their scope of practice and they were compensated for their contribution. They stressed a need for regulatory bodies to deal with medicolegal and ethical aspects of AI and clear guidelines to reduce or eliminate the potential of patient harm.

This study provides the groundwork for assessing PCPs' perceptions of AI systems' features and characteristics, potential applications, possible negative aspects, and requirements for using them. A future study of adolescents' perspectives on integrating AI into mental healthcare might contribute a fuller understanding of the potential of AI for this population.

Mental health is an essential component of the health and well-being of a person and community, and it is critical for the individual, society, and socio-economic development of any country. Mental healthcare is currently in the health sector transformation era, with emerging technologies such as artificial intelligence (AI) reshaping the screening, diagnosis, and treatment modalities of psychiatric illnesses. The present narrative review is aimed at discussing the current landscape and the role of AI in mental healthcare, including screening, diagnosis, and treatment. Furthermore, this review attempted to highlight the key challenges, limitations, and prospects of AI in providing mental healthcare based on existing works of literature. The literature search for this narrative review was obtained from PubMed, Saudi Digital Library (SDL), Google Scholar, Web of Science, and IEEE Xplore, and we included only English-language articles published in the last five years. Keywords used in combination with Boolean operators ("AND" and "OR") were the following: "Artificial intelligence", "Machine learning", Deep learning", "Early diagnosis", "Treatment", "interventions", "ethical consideration", and "mental Healthcare". Our literature review revealed that, equipped with predictive analytics capabilities, AI can improve treatment planning by predicting an individual's response to various interventions. Predictive analytics, which uses historical data to formulate preventative interventions, aligns with the move toward individualized and preventive mental healthcare. In the screening and diagnostic domains, a subset of AI, such as machine learning and deep learning, has been proven to analyze various mental health data sets and predict the patterns associated with various mental health problems. However, limited studies have evaluated the collaboration between healthcare professionals and AI in delivering mental healthcare, as these sensitive problems require empathy, human connections, and holistic, personalized, and multidisciplinary approaches. Ethical issues, cybersecurity, a lack of data analytics diversity, cultural sensitivity, and language barriers remain concerns for implementing this futuristic approach in mental healthcare. Considering these sensitive problems require empathy, human connections, and holistic, personalized, and multidisciplinary approaches, it is imperative to explore these aspects. Therefore, future comparative trials with larger sample sizes and data sets are warranted to evaluate different AI models used in mental healthcare across regions to fill the existing knowledge gaps.

Dementia is a progressive neurodegenerative disorder that affects cognitive abilities including memory, reasoning, and communication skills, leading to gradual decline in daily activities and social engagement. In light of the recent advent of Large Language Models (LLMs) such as ChatGPT, this paper aims to thoroughly analyse their potential applications and usefulness in dementia care and research.

To this end, we offer an introduction into LLMs, outlining the key features, capabilities, limitations, potential risks, and practical considerations for deployment as easy-to-use software (e.g., smartphone apps). We then explore various domains related to dementia, identifying opportunities for LLMs to enhance understanding, diagnostics, and treatment, with a broader emphasis on improving patient care. For each domain, the specific contributions of LLMs are examined, such as their ability to engage users in meaningful conversations, deliver personalized support, and offer cognitive enrichment. Potential benefits encompass improved social interaction, enhanced cognitive functioning, increased emotional well-being, and reduced caregiver burden. The deployment of LLMs in caregiving frameworks also raises a number of concerns and considerations. These include privacy and safety concerns, the need for empirical validation, user-centered design, adaptation to the user's unique needs, and the integration of multimodal inputs to create more immersive and personalized experiences. Additionally, ethical guidelines and privacy protocols must be established to ensure responsible and ethical deployment of LLMs.

We report the results on a questionnaire filled in by people with dementia (PwD) and their supporters wherein we surveyed the usefulness of different application scenarios of LLMs as well as the features that LLM-powered apps should have. Both PwD and supporters were largely positive regarding the prospect of LLMs in care, although concerns were raised regarding bias, data privacy and transparency.

Overall, this review corroborates the promising utilization of LLMs to positively impact dementia care by boosting cognitive abilities, enriching social interaction, and supporting caregivers. The findings underscore the importance of further research and development in this field to fully harness the benefits of LLMs and maximize their potential for improving the lives of individuals living with dementia.

Artificial intelligence (AI) and machine learning (ML) show promise in various medical domains, including medical imaging, precise diagnoses, and pharmaceutical research. In neuroscience and neurosurgery, AI/ML advancements enhance brain-computer interfaces, neuroprosthetics, and surgical planning. They are poised to revolutionize neuroregeneration by unraveling the nervous system's complexities. However, research on AI/ML in neuroregeneration is fragmented, necessitating a comprehensive review. Adhering to Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) recommendations, 19 English-language papers focusing on AI/ML in neuroregeneration were selected from a total of 247. Two researchers independently conducted data extraction and quality assessment using the Mixed Methods Appraisal Tool (MMAT) 2018. Eight studies were deemed high quality, 10 moderate, and four low. Primary goals included diagnosing neurological disorders (35%), robotic rehabilitation (18%), and drug discovery (12% each). Methods ranged from analyzing imaging data (24%) to animal models (24%) and electronic health records (12%). Deep learning accounted for 41% of AI/ML techniques, while standard ML algorithms constituted 29%. The review underscores the growing interest in AI/ML for neuroregenerative medicine, with increasing publications. These technologies aid in diagnosing diseases and facilitating functional recovery through robotics and targeted stimulation. AI-driven drug discovery holds promise for identifying neuroregenerative therapies. Nonetheless, addressing existing limitations remains crucial in this rapidly evolving field.

The advancement of artificial intelligence (AI) and the ubiquity of social media have become transformative agents in contemporary educational ecosystems. The spotlight of this inquiry focuses on the nexus between AI and social media usage in relation to academic performance and mental well-being, and the role of smart learning in facilitating these relationships. Using partial least squares-structural equation modeling (PLS-SEM) on a sample of 401 Chinese university students. The study results reveal that both AI and social media have a positive impact on academic performance and mental well-being among university students. Furthermore, smart learning serves as a positive mediating variable, amplifying the beneficial effects of AI and social media on both academic performance and mental well-being. These revelations contribute to the discourse on technology-enhanced education, showing that embracing AI and social media can have a positive impact on student performance and well-being.

AI tools intend to transform mental healthcare by providing remote estimates of depression risk using behavioral data collected by sensors embedded in smartphones. While these tools accurately predict elevated depression symptoms in small, homogenous populations, recent studies show that these tools are less accurate in larger, more diverse populations. In this work, we show that accuracy is reduced because sensed-behaviors are unreliable predictors of depression across individuals: sensed-behaviors that predict depression risk are inconsistent across demographic and socioeconomic subgroups. We first identified subgroups where a developed AI tool underperformed by measuring algorithmic bias, where subgroups with depression were incorrectly predicted to be at lower risk than healthier subgroups. We then found inconsistencies between sensed-behaviors predictive of depression across these subgroups. Our findings suggest that researchers developing AI tools predicting mental health from sensed-behaviors should think critically about the generalizability of these tools, and consider tailored solutions for targeted populations.

AI tools intend to transform mental healthcare by providing remote estimates of depression risk using behavioral data collected by sensors embedded in smartphones. While these tools accurately predict elevated symptoms in small, homogenous populations, recent studies show that these tools are less accurate in larger, more diverse populations. In this work, we show that accuracy is reduced because sensed-behaviors are unreliable predictors of depression across individuals; specifically the sensed-behaviors that predict depression risk are inconsistent across demographic and socioeconomic subgroups. We first identified subgroups where a developed AI tool underperformed by measuring algorithmic bias, where subgroups with depression were incorrectly predicted to be at lower risk than healthier subgroups. We then found inconsistencies between sensed-behaviors predictive of depression across these subgroups. Our findings suggest that researchers developing AI tools predicting mental health from behavior should think critically about the generalizability of these tools, and consider tailored solutions for targeted populations.

Heart failure (HF) brings not only physical pain but also psychological distress. This systematic review investigated the influence of spiritual care on the psychological well-being and quality of life in adults with HF.

We conducted a systematic literature review following PRISMA guidelines, searching seven electronic databases for relevant randomized controlled studies without language or temporal restrictions. The studies were assessed for quality using the Cochrane Bias Risk tool.

A total of 13 studies (882 participants) were reviewed, investigating interventions such as religion, meditation, mental health, cognitive interventions, and spiritual support. Key factors influencing the effectiveness of spiritual care implementation included integration into routine care, respect for diversity, patient engagement, intervention quality, and alignment with patient beliefs. The majority of the studies indicated that spiritual care has a potentially beneficial impact on the mental health and quality of life of patients with HF.

The findings provide valuable insights for healthcare professionals, highlighting the importance of adopting a spiritual care approach to healthcare for this population.

The use of social media for disseminating health care information has become increasingly prevalent, making the expanding role of artificial intelligence (AI) and machine learning in this process both significant and inevitable. This development raises numerous ethical concerns. This study explored the ethical use of AI and machine learning in the context of health care information on social media platforms (SMPs). It critically examined these technologies from the perspectives of fairness, accountability, transparency, and ethics (FATE), emphasizing computational and methodological approaches that ensure their responsible application.

This study aims to identify, compare, and synthesize existing solutions that address the components of FATE in AI applications in health care on SMPs. Through an in-depth exploration of computational methods, approaches, and evaluation metrics used in various initiatives, we sought to elucidate the current state of the art and identify existing gaps. Furthermore, we assessed the strength of the evidence supporting each identified solution and discussed the implications of our findings for future research and practice. In doing so, we made a unique contribution to the field by highlighting areas that require further exploration and innovation.

Our research methodology involved a comprehensive literature search across PubMed, Web of Science, and Google Scholar. We used strategic searches through specific filters to identify relevant research papers published since 2012 focusing on the intersection and union of different literature sets. The inclusion criteria were centered on studies that primarily addressed FATE in health care discussions on SMPs; those presenting empirical results; and those covering definitions, computational methods, approaches, and evaluation metrics.

Our findings present a nuanced breakdown of the FATE principles, aligning them where applicable with the American Medical Informatics Association ethical guidelines. By dividing these principles into dedicated sections, we detailed specific computational methods and conceptual approaches tailored to enforcing FATE in AI-driven health care on SMPs. This segmentation facilitated a deeper understanding of the intricate relationship among the FATE principles and highlighted the practical challenges encountered in their application. It underscored the pioneering contributions of our study to the discourse on ethical AI in health care on SMPs, emphasizing the complex interplay and the limitations faced in implementing these principles effectively.

Despite the existence of diverse approaches and metrics to address FATE issues in AI for health care on SMPs, challenges persist. The application of these approaches often intersects with additional ethical considerations, occasionally leading to conflicts. Our review highlights the lack of a unified, comprehensive solution for fully and effectively integrating FATE principles in this domain. This gap necessitates careful consideration of the ethical trade-offs involved in deploying existing methods and underscores the need for ongoing research.

The paper reviews the entire spectrum of Artificial Intelligence (AI) in mental health and its positive role in mental health. AI has a huge number of promises to offer mental health care and this paper looks at multiple facets of the same. The paper first defines AI and its scope in the area of mental health. It then looks at various facets of AI like machine learning, supervised machine learning and unsupervised machine learning and other facets of AI. The role of AI in various psychiatric disorders like neurodegenerative disorders, intellectual disability and seizures are discussed along with the role of AI in awareness, diagnosis and intervention in mental health disorders. The role of AI in positive emotional regulation and its impact in schizophrenia, autism spectrum disorders and mood disorders is also highlighted. The article also discusses the limitations of AI based approaches and the need for AI based approaches in mental health to be culturally aware, with structured flexible algorithms and an awareness of biases that can arise in AI. The ethical issues that may arise with the use of AI in mental health are also visited.

College students are currently grappling with severe mental health challenges, and research on artificial intelligence (AI) related to college students mental health, as a crucial catalyst for promoting psychological well-being, is rapidly advancing. Employing bibliometric methods, this study aim to analyze and discuss the research on AI in college student mental health.

Publications pertaining to AI and college student mental health were retrieved from the Web of Science core database. The distribution of publications were analyzed to gage the predominant productivity. Data on countries, authors, journal, and keywords were analyzed using VOSViewer, exploring collaboration patterns, disciplinary composition, research hotspots and trends.

Spanning 2003 to 2023, the study encompassed 1722 publications, revealing notable insights: (1) a gradual rise in annual publications, reaching its zenith in 2022; (2) Journal of Affective Disorders and Psychiatry Research emerged were the most productive and influential sources in this field, with significant contributions from China, the United States, and their affiliated higher education institutions; (3) the primary mental health issues were depression and anxiety, with machine learning and AI having the widest range of applications; (4) an imperative for enhanced international and interdisciplinary collaboration; (5) research hotspots exploring factors influencing college student mental health and AI applications.

This study provides a succinct yet comprehensive overview of this field, facilitating a nuanced understanding of prospective applications of AI in college student mental health. Professionals can leverage this research to discern the advantages, risks, and potential impacts of AI in this critical field.

The rise of artificial intelligence (AI) applications in healthcare provides new possibilities for personalized health management. AI-based fitness applications are becoming more common, facilitating the opportunity for individualised exercise prescription. However, the use of AI carries the risk of inadequate expert supervision, and the efficacy and validity of such applications have not been thoroughly investigated, particularly in the context of diverse health conditions. The aim of the study was to critically assess the efficacy of exercise prescriptions generated by OpenAI's Generative Pre-Trained Transformer 4 (GPT-4) model for five example patient profiles with diverse health conditions and fitness goals. Our focus was to assess the model's ability to generate exercise prescriptions based on a singular, initial interaction, akin to a typical user experience. The evaluation was conducted by leading experts in the field of exercise prescription. Five distinct scenarios were formulated, each representing a hypothetical individual with a specific health condition and fitness objective. Upon receiving details of each individual, the GPT-4 model was tasked with generating a 30-day exercise program. These AI-derived exercise programs were subsequently subjected to a thorough evaluation by experts in exercise prescription. The evaluation encompassed adherence to established principles of frequency, intensity, time, and exercise type; integration of perceived exertion levels; consideration for medication intake and the respective medical condition; and the extent of program individualization tailored to each hypothetical profile. The AI model could create general safety-conscious exercise programs for various scenarios. However, the AI-generated exercise prescriptions lacked precision in addressing individual health conditions and goals, often prioritizing excessive safety over the effectiveness of training. The AI-based approach aimed to ensure patient improvement through gradual increases in training load and intensity, but the model's potential to fine-tune its recommendations through ongoing interaction was not fully satisfying. AI technologies, in their current state, can serve as supplemental tools in exercise prescription, particularly in enhancing accessibility for individuals unable to access, often costly, professional advice. However, AI technologies are not yet recommended as a substitute for personalized, progressive, and health condition-specific prescriptions provided by healthcare and fitness professionals. Further research is needed to explore more interactive use of AI models and integration of real-time physiological feedback.

This narrative literature review undertakes a comprehensive examination of the burgeoning field, tracing the development of artificial intelligence (AI)-powered tools for depression and anxiety detection from the level of intricate algorithms to practical applications. Delivering essential mental health care services is now a significant public health priority. In recent years, AI has become a game-changer in the early identification and intervention of these pervasive mental health disorders. AI tools can potentially empower behavioral healthcare services by helping psychiatrists collect objective data on patients' progress and tasks. This study emphasizes the current understanding of AI, the different types of AI, its current use in multiple mental health disorders, advantages, disadvantages, and future potentials. As technology develops and the digitalization of the modern era increases, there will be a rise in the application of artificial intelligence in psychiatry; therefore, a comprehensive understanding will be needed. We searched PubMed, Google Scholar, and Science Direct using keywords for this. In a recent review of studies using electronic health records (EHR) with AI and machine learning techniques for diagnosing all clinical conditions, roughly 99 publications have been found. Out of these, 35 studies were identified for mental health disorders in all age groups, and among them, six studies utilized EHR data sources. By critically analyzing prominent scholarly works, we aim to illuminate the current state of this technology, exploring its successes, limitations, and future directions. In doing so, we hope to contribute to a nuanced understanding of AI's potential to revolutionize mental health diagnostics and pave the way for further research and development in this critically important domain.