The Use of Artificial Intelligence Systems in Mental Health Treatment: A Thematic Review

Artificial Intelligence in Mental Health: Outcomes and Implications

The world is changing rapidly in the twenty-twenties due to the growth of Artificial Intelligence (AI). Generative AI and large language model chatbots are different types of AI that have quickly diffused into everyday life and psychological practice. The existence of unmet mental health requirements is what makes some members of the general population deem AI chatbots as an alternative to talk therapy. At the same time, AI-based therapeutic tools are integrated into the clinical decision support system, a web-based application, and professional education. This literature review discusses empirical studies and case studies of AI in the psychology field. The findings indicate that AI is potentially able to stimulate accessibility, engagement, and temporary relief of symptoms. Nevertheless, accidents are severe, most importantly, the chances of giving wrong answers in high-stakes situations. Reports of harmful crisis responses from chatbots demonstrate the limitations of AI as a replacement for human judgment or therapeutic relationships. Future research should focus on long-term outcomes and safeguards to ensure safe, transparent integration into mental health care.

Exploration on the Influence of Artificial Intelligence Application in Mental Health on People’s Help-Seeking Behaviour and Mental Health Awareness

Artificial Intelligence (AI) is being used in the mental health field to develop generative AI chatbots and digital phenotyping; thus, the traditional psychological model has been challenged. Examine all kinds of changes in people's help-seeking behaviour and awareness of mental health due to AI applications. Although there are many mental illnesses around the world, most people are still unaware of them due to social stigma, high costs and a lack of health education. AI-led interventions provide users with an easy-to-access and private space for conversation with artificial intelligence to offer new kinds of mental health support. How interaction with AI affects the cognition of psychological problem recognition. Explainable AI (XAI) can help boost people's mental health literacy and provide a foundation for seeking professional mental health services, but there is also a risk of developing emotional dependence on Generative AI technology. Based on the latest empirical evidence for user self-disclosure, anticipated stigma and the construction of a 'digital therapeutic alliance', this study presents a dual-edged nature of AI. To maximise the health benefits for the public, AI should be used to construct an auxiliary triage system that supports human clinicians and enables appropriate referrals when needed.

A Comparison of AI Mental Health Models and Human Therapy

The rapid expansion of artificial intelligence (AI) in healthcare has prompted growing interest in its application to mental health support. This review compares AI-based mental health tools to human psychotherapy from neuroscientific, computational, and clinical perspectives. The review outlines the structure and evidence-based human therapy, with a focus on cognitive behavioral therapy (CBT) and the therapeutic alliance; then, the mechanisms underlying AI mental health models, including large language models, natural language processing, and training techniques such as reinforcement learning from human feedback are explained. A comparison of the human brain and artificial neural networks, and the analysis of empathy plus emotional processing in both systems, is presented. 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. Their simulated empathy, which is strongly associated with the success of treatment, differs from natural human emotions and also contributes to the decrease in effectiveness. Key limitations, including privacy concerns, algorithmic bias, and inadequate crisis handling, are also discussed. The review concludes that a hybrid model integrating AI tools with human-delivered care is the most promising direction for the future of mental health treatment.

DETERMINANTS OF ARTIFICIAL INTELLIGENCE-BASED CLINICAL DECISION SUPPORT SYSTEMS ADOPTION AMONG MENTAL HEALTH PROFESSIONALS: THE ROLES OF EMPATHY, ACCOUNTABILITY AND TRUST

Artificial Intelligence as a Personal Coach: A Narrative Review of Benefits and Risks in Educational and Health Contexts.

The last decade has seen a rapid increase in individuals turning to artificial intelligence (AI) for advice related to their personal development, especially with the introduction of general-purpose large language models (LLMs) to the general public in 2022. This narrative review examines the potential benefits and risks of using AI for coaching purposes in educational and health contexts. Given that empirical studies on using general-purpose LLMs in these domains remain limited, this paper first synthesizes findings from purpose-built coaching chatbots designed to perform specific tasks that facilitate personal development in these domains and discusses limitations associated with studies that use older versions of chatbots. The reviewed evidence suggests that purpose-built chatbot coaching systems may have some benefits as they are generally well received and can support short-term motivation and selected behavior change, but effects for sustained, meaningful outcomes are inconsistent. We then reflect on the potential risks of using general-purpose LLMs as a coach without appropriate human oversight, by reviewing features of general-purpose LLMs, such as sycophancy, accuracy, and problematic patterns of use. Synthesizing these findings, we consider their implications before identifying potential directions for future research.

1359 Clinical Use of Artificial Intelligence Among Irish Doctors

With the growing use of artificial intelligence (AI) tools, the health service executive (HSE) has begun formally integrating AI into national digital and clinical governance structures. Despite this, real world patterns of AI use among doctors remain poorly characterised. We conducted a cross sectional, observational and descriptive study using an anonymous online questionnaire distributed to doctors working across Ireland. The survey captured demographic data, context of AI use and perceived influence on clinical decision making including benefits and concerns. Eighty-nine doctors consented and completed the survey. Seventy-four (83%) reported using AI during clinical care with fifty-two (58%) using it at least weekly. AI was used most commonly for rapid clarification of unfamiliar topics (71%), interpreting complex polypharmacy (60%) and checking medication interactions and doses (51%). Among those who use AI, chat GPT is most commonly used (82%). AI was reported to have a moderate to very strong influence formulating a differential diagnosis (49%), selecting investigations (45%) and in prescribing decisions (42%). Forty-eight (54%) reported identifying incorrect AI recommendations regularly. Information was most often verified by cross checking with colleagues (34%) or guidelines (27%). Sixty-four (72%) doctors expressed concern that AI may provide clinically unsafe information and seventy-five (84%) were unsure about how AI use fits within clinical governance. While clinicians recognise its efficiency and cognitive benefits, substantial concern remains regarding safety and regulation. These findings highlight the urgent need for clear national and hospital-level policies to support safe, regulated integration of AI into clinical practice.