This chapter explores how AI talks, listens, and helps people living with SMIs, examining the nature and limitations of AI in transforming the diagnosis and treatment of SMIs.
Abstract
In addition, Artificial Intelligence (AI) has revolutionised approaches used in the management of mental diseases that put one's life at risk, such as schizophrenia, bipolar disorder, and major depressive disorder. Challenges associated with conventional techniques include a lack of symptom identification, subjectivity in assessing patient status, and limited access to specialised healthcare services. In that regard, AI has developed advanced predictive analytics, machine learning (ML), and natural language processing (NLP) to facilitate the deployment of SMIs by leveraging pattern recognition of behaviour, speech, and physiological parameters. AI Chatbots and virtual psychologists can monitor a person's mental wellness throughout the day without necessarily involving healthcare specialists. In addition, wearable devices worn by patients fitted with AI produce real-time signals, including heart rate variations and sleep patterns, which help pace the respondent's mental state. Recommendation engines based on artificial intelligence provide personalised plans for pharmaceutical and therapeutic interventions. With the above innovations in the hood, however, ethical concerns related to AI, such as data privacy, biases, and AI over-extrapolation, remain a concern. It is crucial to ensure transparency and regulation, as well as a human-AI partnership, for AI to thrive in mental healthcare services. This chapter explores how AI talks, listens, and helps people living with SMIs, examining the nature and limitations of AI in transforming the diagnosis and treatment of SMIs.
These technologies show promise in reducing human error and enhancing mental health care delivery; however, persistent challenges include data privacy, ethical considerations, and the need for diverse, large-scale datasets.
Juster Donal Sinaga· Journal of Society Counselin...· 0 citations
AI is significantly impacting the way that mental health diagnostic tools are developed through their ability to
provide affordable, accessible and efficient means of detecting psychological disorders like depression. While the current
state of screening includes many effective tools (i.e., Clinical Interviews and Self- Reported Questionnaires) they have some
inherent shortcomings; these include, but are not limited to, being subjective and/or delayed diagnoses, lack of access to
individuals who may be experiencing difficulties with their mental health, and dependency on the availability of
professional interventions. The shortfalls identified above demonstrate the need for the development of intelligent systems,
capable of conducting rapid and accurate evaluations of an individualʼs mental health. An AI-driven Depression Level
Prediction System was therefore created to collect structured clinical information, as well as unstructured textual input in
order to create a full and complete assessment of an individualʼs mental health condition. Utilizing the PHQ 9 survey
instrument as the basis for collecting clinical information, the system utilizes Natural Language Processing techniques to
evaluate user-generated text, thereby gaining further insight into an individualʼs emotional and psychological trends.
The system described herein utilizes three machine learning-based predictive models Random Forest, SVM,
XGBoost) to predict an individualʼs level of depression as one of four categories (minimal, mild, moderate or severe).
Unlike prior binary prediction models utilized in the context of mental health evaluations, the described model
provides fine-tuned evaluations that can be more effectively used in practical applications of mental health
monitoring. Additionally, Explainable AI techniques were incorporated into the design of the system to improve
transparency and interpretability of the results produced by the system. Such capabilities enable both patients and
clinicians to identify specific variables within the results that contributed to the systemʼs predictions. The modular nature
of the system enables scalability, flexibility and efficient operation of the system even when utilizing lightweight
hardware that does not require extensive computing capabilities. Experimental validation demonstrated that the described
system achieved greater accuracy and better generalization than other systems currently available. Through its ability to
process both behavioral inputs, questionnaire responses and textual sentiment analysis, the system offers a holistic view
of an individualʼs mental health status. Beyond improving early detection, the described system can assist clinicians and
patients in making informed decisions regarding treatment options for issues related to mental health. Therefore, the
system serves as a connection between traditional healthcare practices and emerging AI technology to provide a private
and secure method for evaluating mental health conditions.
Danda Shruthi, Annamaneni Sai, Kalal Taruni et al.· International Journal of Inn...· 0 citations
Evidence suggests that while AI tools can temporarily reduce symptoms and improve accessibility to professional help for mild to moderate conditions, they are less effective in cases of severe or complex disorders.
Nan-Xi Zhang· Theoretical and Natural Scie...· 0 citations
The International Workshop on AI for Cognitive and Mental Health Support is proposed, a half-day interdisciplinary forum that brings together researchers and practitioners from data mining, machine learning, NLP, NLP, HCI, healthcare, and social sciences to advance trustworthy, effective, and socially responsible AI solutions for cognitive and mental health support.
Xiangmeng Wang, Haoyang Li, Chen Li et al.· Proceedings of the 32nd ACM...· 0 citations
Menopause marks a crucial transition in a woman's life and is often accompanied by physical and psychological changes that can adversely affect mental health. Depression, anxiety, cognitive changes, and sleep disturbances are common during the menopausal transition, yet they are frequently underdiagnosed and undertreated, particularly in lowand middle-income countries. Emerging technologies, especially artificial intelligence (AI), offer new opportunities to narrow this care gap. Although AI has shown considerable promise in identifying menopause-related physical health conditions (e.g., osteoporosis and endometrial cancer), its use for mental health during this life stage remains limited. We discuss the potential of AI-driven tools-including machine learning algorithms, digital therapeutics, symptom trackers, and large language models-to improve the detection, monitoring, and personalized management of menopause-associated mental health disorders. By integrating genetic, clinical, lifestyle, and wearable data, AI systems may help predict risk, identify symptom patterns, and support tailored interventions. These approaches could enable scalable, accessible, and cost-effective mental healthcare, reduce stigma, and address service gaps. Harnessing AI in this area offers a significant opportunity to improve quality of life for millions of women worldwide.
Rowaida Sadat, K. G. Saçıntı, A. Panattoni et al.· JBRA Assisted Reproduction· 0 citations
Artificial Intelligence (AI) is rapidly transforming healthcare by supporting clinical decision-making, patient monitoring, documentation, education, research, and personalized care. Mental health nursing is an important area in which AI has the potential to improve early identification of mental health problems, continuous monitoring, therapeutic support, risk assessment, and access to care. Recent advances in machine learning, natural language processing, predictive analytics, conversational agents, and generative AI have expanded the possibilities for supporting individuals experiencing depression, anxiety, psychosis, substance-use disorders, and other mental health conditions. Evidence suggests that AI-based systems can assist in detecting symptoms, predicting clinical risks, monitoring changes in behaviour and mood, and providing accessible digital interventions. However, the use of AI in mental health also creates significant ethical and professional concerns, particularly regarding privacy, confidentiality, informed consent, algorithmic bias, transparency, accountability, patient safety, therapeutic relationships, and the risk of over-reliance on automated systems. Mental health nurses are uniquely positioned to ensure that AI remains person-centred and clinically appropriate because they combine continuous patient observation with therapeutic communication and holistic assessment. This article reviews the major applications and opportunities of AI in mental health nursing, discusses ethical and professional challenges, and proposes future directions for education, research, clinical practice, and policy. AI should be regarded as a supportive technology rather than a replacement for professional nursing judgment or human therapeutic relationships.
Payal Sharma, Milan Agravat, Pranali Mackwan et al.· Adolescência e Saúde· 0 citations
