ChatGPT and other Chatbots in Psychiatry

Mental health disorders are a significant global health challenge, affecting millions of individuals each year and often resulting in long-term disability, impaired quality of life, and considerable societal burden. Conditions such as depression, anxiety, bipolar disorder, schizophrenia, and other psychiatric illnesses have traditionally been diagnosed and treated using clinical assessments, psychotherapy, and pharmaceutical interventions. However, despite ongoing advancements in mental health treatment, existing methods often fall short in terms of early detection, personalized care, and long-term effectiveness. In recent years, the integration of artificial intelligence (AI) with natural product-based therapeutics has emerged as a promising and innovative solution for addressing these gaps in mental health care. AI, with its ability to analyze large datasets, recognize complex patterns, and provide predictive insights, holds great potential in enhancing the early diagnosis, personalized treatment, and continuous monitoring of mental health conditions. Meanwhile, natural products, including herbs, plant-based compounds, and nutraceuticals, offer time-tested therapeutic benefits that are increasingly being recognized and studied within modern psychiatric care. AI-powered tools can offer precise, individualized predictions of mental health conditions by analyzing patient data, including genetic markers, behavior patterns, environmental factors, and treatment responses. These technologies enable the identification of early warning signs and more accurate diagnoses, which are crucial for initiating effective interventions at an earlier stage, ultimately improving patient outcomes. Moreover, AI can optimize treatment plans by tailoring them to each patient's unique biology, ensuring the right combination of therapies, whether pharmaceutical or natural, based on real-time feedback and monitoring. On the other hand, natural product-based therapeutics, with their rich history in traditional medicine and growing body of scientific evidence, present a natural complement to AI-based approaches. Herbs, adaptogens, and nutraceuticals have been shown to have mood-regulating, anti-anxiety, and neuroprotective properties that may help alleviate symptoms of mental health disorders. When integrated with AI systems, these natural products can be used in a more personalized, targeted manner, enhancing their therapeutic effectiveness and minimizing potential side effects. The potential benefits of combining AI and natural therapeutics in mental health care are Early Diagnosis, Personalized Treatment AND Continuous Monitoring and Optimization. This white paper aims to provide an in-depth exploration of the current state of mental health disorder detection and treatment, outline the role of AI in improving diagnosis and therapeutic outcomes, and highlight the potential for integrating AI with natural product-based treatments. By addressing the challenges and exploring the opportunities, this paper envisions a future where AI and natural therapeutics work in synergy to provide more effective, personalized, and accessible mental health care.

IntroductionMental health is crucial and is the backbone of all dimensions of health; physical, social and spiritual. Mental health has multiple interfaces and it is important to bring mental health to the center stage as it is the key regulator of all human activities. Unfortunately, there are alarming gaps in mental health care especially in rural areas which require attention of mental health professionals and policy makers.The study aims to understand the causes of these gaps and suggest possible and practical solutions to bridge them.ObjectivesTo study the spectrum of mental health gaps present in rural areas of Haryana, a state in the northern part of India and find culturally sensitive and relevant solutions keeping in view the socio economic realities and prevalent legal framework.Methods Any factor having bearing on mental health but is operative sub-optimally would be considered as mental health gap for the current investigation. Rural camps were organized in 10 villages to assess the service gap at three different levels: overt (measurable), covert (including attitudinal) and ancillary (including those embedded in the psychiatry evaluation and treatment). The camps were organized by following these three basic steps: 1) Evaluating the geographic and demographic details of the villages selected. This was done by meeting the key stakeholders of the villages and the official health and service statistics available on the government website 2) Camp by multidisciplinary team in the villages with an advance intimation. The team members evaluated the mental health care awareness and the felt needs by interviewing all the villagers attending the camp on that particular day. 3) Post camp review by the team to analyze the service gaps and steps to address and narrow the gaps.ResultsApart from inadequate availability of professional and infrastructural resources, there were many attitudinal and ancillary gaps serving as obstacles to treatment seeking. Trust gaps leading to poor acceptance and legislation not congruent with the socio cultural needs were key impediments. Rural people had more faith in Spiritual leaders and faith healers for their mental health issues and medical help was sought only when they have signs of physical illness. Mental health and illnesses were not on priority. Availability, accessibility and affordability of health services were important factors needing immediate attention.Conclusions Rural services need to be augmented by de professionalization and task shifting is the key to address and cover the yawning gaps in the services. Massive, coordinated, multidisciplinary and sustainable efforts are needed to bridge the multitude of gaps keeping in view poverty and illiteracy as compounding factors.Disclosure of InterestNone Declared

Clinical Decision Support Tools

The healthcare industry increasingly values high-quality and personalized care. Patients with heart failure (HF) receiving home health care (HHC) often experience hospitalizations due to worsening symptoms and comorbidities. Therefore, close symptom monitoring and timely intervention based on risk prediction could help HHC clinicians prevent emergency department (ED) visits and hospitalizations. This study aims to (1) describe important variables associated with a higher risk of ED visits and hospitalizations in HF patients receiving HHC; (2) map data requirements of a clinical decision support (CDS) tool to the exchangeable data standard for integrating a CDS tool into the care of patients with HF; (3) outline a pipeline for developing a real-time artificial intelligence (AI)-based CDS tool. We used patient data from a large HHC organization in the Northeastern US to determine the factors that can predict ED visits and hospitalizations among patients with HF in HHC (9362 patients in 12,223 care episodes). We examined vital signs, HHC visit details (e.g., the purpose of the visit), and clinical note-derived variables. The study identified critical factors that can predict ED visits and hospitalizations and used these findings to suggest a practical CDS tool for nurses. The tool's proposed design includes a system that can analyze data quickly to offer timely advice to healthcare clinicians. Our research showed that the length of time since a patient was admitted to HHC and how recently they have shown symptoms of HF were significant factors predicting an adverse event. Additionally, we found this information from the last few HHC visits before the occurrence of an ED visit or hospitalization were particularly important in the prediction. One hundred percent of clinical demographic profiles from the Outcome and Assessment Information Set variables were mapped to the exchangeable data standard, while natural language processing-driven variables couldn't be mapped due to their nature, as they are generated from unstructured data. The suggested CDS tool alerts nurses about newly emerging or rising risks, helping them make informed decisions. This study discusses the creation of a time-series risk prediction model and its potential CDS applications within HHC, aiming to enhance patient outcomes, streamline resource utilization, and improve the quality of care for individuals with HF. This study provides a detailed plan for a CDS tool that uses the latest AI technology designed to aid nurses in their day-to-day HHC service. Our proposed CDS tool includes an alert system that serves as a guard rail to prevent ED visits and hospitalizations. This tool can potentially improve how nurses make decisions and improve patient outcomes by providing early warnings about ED visits and hospitalizations.

A few weeks ago, we published an editorial discussion on whether artificial intelligence applications should be authors of academic articles [1] . We were delighted to receive more than one interesting reply letter to this editorial in a short time [2, 3] . We hope that opinions on this

We updated a clinical decision support tool integrating predicted visual field (VF) metrics from an artificial intelligence model and assessed clinician perceptions of the predicted VF metric in this usability study. To evaluate clinician perceptions of a prototyped clinical decision support (CDS) tool that integrates visual field (VF) metric predictions from artificial intelligence (AI) models. Ten ophthalmologists and optometrists from the University of California San Diego participated in 6 cases from 6 patients, consisting of 11 eyes, uploaded to a CDS tool ("GLANCE", designed to help clinicians "at a glance"). For each case, clinicians answered questions about management recommendations and attitudes towards GLANCE, particularly regarding the utility and trustworthiness of the AI-predicted VF metrics and willingness to decrease VF testing frequency. Mean counts of management recommendations and mean Likert scale scores were calculated to assess overall management trends and attitudes towards the CDS tool for each case. In addition, system usability scale scores were calculated. The mean Likert scores for trust in and utility of the predicted VF metric and clinician willingness to decrease VF testing frequency were 3.27, 3.42, and 2.64, respectively (1=strongly disagree, 5=strongly agree). When stratified by glaucoma severity, all mean Likert scores decreased as severity increased. The system usability scale score across all responders was 66.1±16.0 (43rd percentile). A CDS tool can be designed to present AI model outputs in a useful, trustworthy manner that clinicians are generally willing to integrate into their clinical decision-making. Future work is needed to understand how to best develop explainable and trustworthy CDS tools integrating AI before clinical deployment.

By providing cutting-edge therapeutic interventions, improving accessibility, and creating immersive healing experiences, generative artificial intelligence (AI) and virtual reality (VR) are transforming mental health care. This study investigates how these new technologies affect mental health, looking at how they might enhance wellbeing while resolving ethical issues and inherent difficulties. By offering individualized interventions, cognitive behavioral therapy (CBT), and real-time emotional support, generative AI-powered chatbots and virtual assistants lower obstacles to mental health care. By establishing safe, immersive settings that promote gradual desensitization, virtual reality exposure therapy has shown promise in the treatment of phobias, anxiety disorders, and post-traumatic stress disorder (PTSD). Notwithstanding these benefits, issues with algorithmic bias, data privacy, and an excessive dependence on technology pose serious problems. To guarantee patient safety, the ethical ramifications of AI-generated mental health advice—specifically, its accuracy and dependability—need thorough confirmation. Guidelines for ethical use are necessary because VR's immersive nature can also result in dissociation, addiction, or unexpected psychological impacts. To ensure fair access and efficacy, technologists, psychologists, and legislators must work together to create standardized guidelines for integrating AI and VR into clinical practice. The implications of AI-driven mental health interventions for marginalized groups—who frequently face inequities in access to conventional care—are also examined in this research. Generative AI models' versatility makes it possible to create therapeutic applications that are inclusive of all languages and cultures, filling in gaps in mental health care around the globe. To solve issues with AI bias, false information, and responsibility in automated mental health solutions, legislative and ethical frameworks must change. The use of AI and VR in self-guided therapy, crisis intervention, and preventive care is growing as these technologies continue to transform the field of mental health. Research on these technologies' long-term effects on social connections, human emotional intelligence, and psychological resilience is still lacking, despite the fact that they have the potential to improve patient participation and lessen the workload for mental health practitioners. To optimize the advantages of generative AI and VR for mental health, this study emphasizes the need to strike a balance between technology innovation and human-centric ethical issues.

Purpose: Use of Artificial Intelligence (AI) as a diagnostic tool or clinical decision support tool has been investigated extensively. The purpose of this study is to review the literature to date, the number and type of systems that are already available.Methods: A search of MEDLINE, Embase, Cochrane Review, and Global Health Library (2000–2022) was performed. Key words used were Ophthalmology AND AI OR Clinical Assist OR Decision Support Tools. Inclusion criteria were articles written in the English language and studies with human participants. Exclusion criteria were editorials, articles using animal models, and pure validation studies.Results: 11 557 papers when ‘Ophthalmology and AI’ filtered applied, and 906 when ‘Decision Support Tool’ is specified. 387 titles filtered for inclusion/exclusion criteria, of which 26 working tools were identified, (Cataract 4, Cornea and refraction 3, Melanoma 2, Oculoplastic 3, Paediatric Ophthalmology 1, Triage 2, Glaucoma and Neuro‐Ophthalmology 4, Retina 7). Of these, 6 are available for clinical use.Conclusions: Out of a large number of studies, we find 6 examples of Diagnostic or Decision support tools ready for implementation. Two are diagnostic, based on input of risk factors alone, and the others use image analysis for disease management and screening. Most AI work is retrospective and observational in nature, with most trials in medical retinal diseases. Further work is needed to translate AI ideas into clinical practice.

This study investigates the integration of artificial intelligence (AI) cutting-edge technology, with Islamic education, and the opportunities, difficulties, ethics, and moral dilemmas related to Islamic education. Artificial intelligence (AI) has become a revolutionary force in many industries, and interest in its potential applications in education has grown usefully. The paper’s objective is to shed light on how artificial intelligence (AI) can be applied to improve Islamic education’s accessibility and quality while upholding the community’s core beliefs. The study analyzes Artificial Intelligence in Islamic Education: Opportunities, Challenges, and Ethical Considerations through the literature study method, especially on online sources. The results show that if we can apply artificial intelligence with Ethical Considerations, it will be beneficial in this digital age to improve the accessibility of Islamic education.

Breast cancer has the highest incidence and second-highest mortality rate among all cancers. The management of breast cancer is being revolutionized by artificial intelligence (AI), which is improving early detection, pathological diagnosis, risk assessment, individualized treatment recommendations, and treatment response prediction. Nuclear medicine has used artificial intelligence (AI) for over 50 years, but more recent advances in machine learning (ML) and deep learning (DL) have given AI in nuclear medicine additional capabilities. AI accurately analyzes breast imaging scans for early detection, minimizing false negatives while offering radiologists reliable, swift image processing assistance. It smoothly fits into radiology workflows, which may result in early treatments and reduced expenditures. In pathological diagnosis, artificial intelligence improves the quality of diagnostic data by ensuring accurate diagnoses, lowering inter-observer variability, speeding up the review process, and identifying errors or poor slides. By taking into consideration nutritional, genetic, and environmental factors, providing individualized risk assessments, and recommending more regular tests for higher-risk patients, AI aids with the risk assessment of breast cancer.The integration of clinical and genetic data into individualized treatment recommendations by AI facilitates collaborative decision-making and resource allocation optimization while also enabling patient progress monitoring, drug interaction consideration, and alignment with clinical guidelines. AI is used to analyze patient data, imaging, genomic data, and pathology reports in order to forecast how a treatment would respond. These models anticipate treatment outcomes, make sure that clinical recommendations are followed, and learn from historical data. The implementation of AI in medicine is hampered by issues with data quality, integration with healthcare IT systems, data protection, bias reduction, and ethical considerations, necessitating transparency and constant surveillance. Protecting patient privacy, resolving biases, maintaining transparency, identifying fault for mistakes, and ensuring fair access are just a few examples of ethical considerations. To preserve patient trust and address the effect on the healthcare workforce, ethical frameworks must be developed. The amazing potential of AI in the treatment of breast cancer calls for careful examination of its ethical and practical implications. We aim to review the comprehensive role of artificial intelligence in breast cancer management.

e20011 Background: The therapeutic landscape of lung cancer is rapidly evolving, presenting oncologists with the challenge of staying updated amidst an overwhelming influx of data. Clinical decision support (CDS) tools, including artificial intelligence (AI) and large language models (LLMs), may help bridge this gap. Evaluating the accuracy of LLMs in complex, real-world oncology scenarios is crucial to understanding their potential. Methods: Twenty-five de-identified lung cancer cases from the fellows’ clinic at Karmanos Cancer Institute, Detroit, MI, were analyzed. Two LLMs, GPT-4 (OpenAI) and Claude Opus (Anthropic), were assessed using advanced prompting techniques like persona-based and chain-of-thought prompting. Five board-certified lung cancer oncologists from NCI-designated centers evaluated LLM-generated responses based on accuracy, treatment recommendation comprehensiveness, and supportive care planning, using a 1–5 scale. Novel insights, the presence of fabricated information, and harmful recommendations were flagged as binary outcomes. Oncologists were blinded to the LLM source and actual treatment decisions. Results: Table 1 presents patient characteristics. GPT-4 achieved an average accuracy score of 4.2 (95% CI, 3.9–4.4), with 3.7 for comprehensiveness of medical/surgical treatment recommendations and 3.7 for supportive care planning. Six responses (32%) were flagged as potentially harmful, and two (8%) contained inaccuracies. Sixteen GPT-4 responses (64%) were rated trustworthy as a CDS tool. Claude Opus had an average accuracy score of 3.6 (95% CI, 3.1–4.1), scoring 3.6 for treatment recommendation comprehensiveness and 3.5 for supportive care planning. Nine responses (36%) were flagged for potential harm, and five (20%) included inaccuracies. Eleven Claude responses (44%) were deemed trustworthy. Significant differences were observed in accuracy (p=0.04) and trustworthiness (p=0.03) between models using McNemar's test. Other factors showed no statistical significance. Conclusions: GPT-4 outperformed Claude Opus in accuracy and trustworthiness, but both models demonstrated limitations, including harmful recommendations and inaccuracies. These findings highlight the need for improved LLM refinement before routine use as CDS tools in lung cancer treatment. Patient demographics and clinical characteristics. Category subcategory Number Median Age (range)- Yr 65 (26-78) Female 7 Male 18 Histology Adenocarcinoma 10 Squamous Cell Carcinoma (SCC) 7 Small Cell Carcinoma 6 Poorly Differentiated 2 Total 25 Stage NSCLC Stage 3 7 NSCLC Stage 4 13 Small Cell limited stage 3 Small Cell Extensive Stage 2

Artificial Intelligence (AI) is transforming healthcare globally, including in Pakistan. This research journal paper provides an overview of the role and significance of AI in healthcare, ethical considerations, regulatory frameworks, challenges faced byAI healthcare startups, and the current landscape of AI adoption in Pakistan's healthcare system. AI has the potential to revolutionize disease prevention, diagnosis, and treatment by leveraging large healthcare datasets, advanced computational power, and machine learning algorithms. It improves patient outcomes, enhances clinical decision-making, and optimizes healthcare delivery. Globally, AI applications in healthcare encompass medical imaging, clinical decision support, drug discovery, genomics, and remote patient monitoring. AI algorithms accurately diagnose various medical conditions, predict treatment responses, and identify therapeutic targets. Successful AI implementations include combating antimicrobial resistance and improving pediatric healthcare. Ethical considerations in AI healthcare involve bias mitigation, privacy, transparency, and the role of healthcare professionals in shared decision-making. Regulatory frameworks and guidelines are being developed worldwide to ensure safe and responsible AI implementation. Quality criteria for AI-based prediction models focus on performance, interpretability, generalizability, and robustness. Legal and ethical considerations encompass liability, accountability, and the principles of beneficence, autonomy, and justice. In Pakistan, the integration of AI in healthcare can address challenges like limited resources and uneven distribution of healthcare facilities. AI technologies can analyze medical data, diagnose disease outcomes, and personalize treatment plans.

Recently, the field of psychiatry has experienced a transformative shift with the integration of digital tools into traditional therapeutic approaches. Digital psychiatry encompasses a wide spectrum of applications, ranging from digital phenotyping, smartphone applications, wearable devices, virtual/augmented reality, and artificial intelligence (AI). This convergence of digital innovations has the potential to revolutionize mental health care, enhancing both accessibility and patient outcomes. However, despite significant progress in the field of digital psychiatry, its implementation presents a plethora of challenges and ethical considerations. Critical problems that require careful investigation are raised by issues such as data privacy, the digital divide, legal frameworks, and the dependability of digital instruments. Furthermore, there are potential risks and several hazards associated with the integration of digital tools into psychiatric practice. A better understanding of the growing field of digital psychiatry is needed to promote the development of effective interventions and improve the accuracy of diagnosis. The overarching goal of this review paper is to provide an overview of some of the current opportunities in digital psychiatry, highlighting both its potential benefits and inherent challenges. This review paper also aims at providing guidelines for future research and for the proper integration of digital psychiatry into clinical practice.

BACKGROUND Depression is a pervasive global mental health challenge, affecting approximately 5.7% of the global population, with heightened prevalence among vulnerable groups such as children (5-10%), elderly individuals (15-20%), postpartum women (10-20%), and married couples (17-18%). Traditional therapeutic approaches, while effective for some, face limitations in accessibility, scalability, and personalization. Emerging digital technologies, particularly mobile applications and machine learning (ML), offer promising avenues to address these gaps by enabling non-invasive, tailored interventions. However, systematic integration of evidence-based psychological frameworks with advanced technologies remains underexplored, especially for demographic-specific needs. This study addresses these challenges by developing a mobile application, MOODZ, designed to deliver personalized, scalable mental health support across four high-risk groups. OBJECTIVE The primary objective of this study is to design, develop, and evaluate MOODZ—a cross-platform mobile application that integrates standardized diagnostic tools, machine learning models, and image processing to: Accurately classify depression levels in children, elderly individuals, postpartum women, and married couples. Provide personalized therapeutic recommendations (e.g., yoga, meditation, community activities) based on user-specific needs. Monitor longitudinal progress and improve mental health outcomes through accessible, technology-driven interventions. METHODS Study Design: A mixed-methods approach combining quantitative analysis (ML-driven predictions) and qualitative feedback (user/caregiver evaluations). Participants: Targeted demographics included children, elderly individuals, postpartum women, and married couples. Tools and Frameworks: Standardized Scales: CES-DC (children), GDS (elderly), EPDS (postpartum women), DAS (married couples). ML Models: Random Forest, Gradient Boosting, Logistic Regression, and k-Nearest Neighbors for depression classification. Technical Implementation: React Native (frontend), Firebase (backend), Google Colab (data analysis), and image processing for facial recognition in children. Data Collection: Quantitative data from validated scales and ML predictions. Qualitative insights from user engagement surveys and expert evaluations (psychiatrists, counselors). Ethical Compliance: Anonymized data storage, informed consent, and ethical approval from the Sri Lanka Institute of Information Technology. RESULTS Quantitative Outcomes: ML models achieved high accuracy in depression classification: 98% (Gradient Boosting), 97% (k-NN), and 89% (Random Forest). Image processing for children’s facial recognition enhanced CES-DC questionnaire accuracy. Personalized recommendations (e.g., yoga, digital drawing) showed measurable symptom reduction in follow-up evaluations. Qualitative Insights: User Engagement: 85% of participants reported ease of use and symptom awareness. Caregiver Feedback: Highlighted the importance of progress tracking and activity customization. Expert Validation: Psychiatrists confirmed alignment with clinical practices and therapeutic relevance. CONCLUSIONS This study demonstrates the efficacy of MOODZ, a mobile application that synergizes machine learning, image processing, and evidence-based psychological frameworks to address depression in high-risk demographics. Key findings include: High accuracy (up to 98%) in depression classification using ML models. Positive user engagement and symptom management through personalized activities. Scalability and accessibility for diverse populations via cross-platform deployment. Future work will focus on integrating multimodal data (wearables, voice analysis) and expanding collaborations with healthcare providers for real-world validation. MOODZ exemplifies the transformative potential of digital innovations in bridging gaps in mental health care. CLINICALTRIAL Note: This study is not a clinical trial but a technological intervention development project. As such, it does not require clinical trial registration. The research adheres to ethical guidelines for software-based mental health interventions, with oversight from the Sri Lanka Institute of Information Technology.

This abstract delves into the dynamic interplay between community psychology and cross-cultural psychology, highlighting their roles in promoting psychological well-being, addressing social issues, and fostering a deeper understanding of cultural diversity. Community psychology focuses on the reciprocal relationship between individuals and their communities, emphasizing the importance of context in shaping human behavior and well-being. By employing an ecological perspective, community psychologists strive to empower communities, prevent social problems, and advocate for social justice. This approach recognizes the impact of various factors, including economic disparities, social support networks, and neighborhood environments, on individual psychological experiences. Cross-cultural psychology, on the other hand, investigates the intricate connections between culture and psychology. It seeks to uncover both universal and culturally specific aspects of human behavior, cognition, and emotion. By comparing psychological phenomena across cultures, cross-cultural psychologists illuminate the diverse ways in which cultural norms, values, and traditions influence individuals’ thoughts and actions. This field plays a crucial role in challenging ethnocentric biases and enriching our understanding of the human experience. The abstract highlights the convergence of these two fields, emphasizing how they mutually enrich one another. Community psychology benefits from the insights of cross-cultural psychology by recognizing the importance of cultural context in community dynamics and interventions. Cross-cultural psychology gains depth by integrating the community perspective, recognizing that culture is not solely an individual attribute, but a collective phenomenon shaped by the communities in which people reside. Through collaborative research, interventions, and advocacy efforts, these fields contribute to building more inclusive and equitable societies. By acknowledging the importance of community and cultural influences on psychological well-being, researchers and practitioners can better address societal challenges, bridge gaps in mental health care, and create interventions that are sensitive to the diverse needs of individuals from various backgrounds. In conclusion, the synergy between community psychology and cross-cultural psychology holds great potential for advancing our understanding of human behavior and well-being within the contexts of communities and cultural diversity. This abstract encourages continued exploration and integration of these fields to create meaningful impact and positive change in diverse societies around the world.