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Nelson R Saranya, Sharon. "Revolutionizing Health Records: The AI Way". International Journal of Science and Research (IJSR) 13, n.º 4 (5 de abril de 2024): 1310–13. http://dx.doi.org/10.21275/sr24417190214.
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Barassi, Veronica y Rahi Patra. "AI Errors in Health?" Morals & Machines 2, n.º 1 (2022): 34–43. http://dx.doi.org/10.5771/2747-5174-2022-1-34.
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The ever-greater use of AI-driven technologies in the health sector begs moral questions regarding what it means for algorithms to mis-understand and mis-measure human health and how as a society we are understanding AI errors in health. This article argues that AI errors in health are putting us in front of the problem that our AI technologies do not grasp the full pluriverse of human experience, and rely on data and measures that have a long history of scientific bias. However, as we shall see in this paper, contemporary public debate on the issue is very limited. Drawing on a discourse analysis of 520 European news media articles reporting on AI-errors the article will argue that the ‘media frame’ on AI errors in health is often defined by a techno-solutionist perspective, and only rarely it sheds light on the relationship between AI technologies and scientific bias. Yet public awareness on the issue is of central importance because it shows us that rather than ‚fixing‘ or ‚finding solutions‘ for AI errors we need to learn how to coexist with the fact that technlogies – because they are human made, are always going to be inevitably biased.
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Sumit. "AI Health Care Chatbot". International Journal for Modern Trends in Science and Technology 6, n.º 12 (13 de diciembre de 2020): 219–24. http://dx.doi.org/10.46501/ijmtst061241.
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Healthcare bot is a technology that makes interaction between man and machine possible by using Artificial Intelligence with the support of dialog flow. Now a day people tend to seek knowledge or information from internet that concern with health through online healthcare services. To lead a good life healthcare is very much important. But it is very difficult to obtain the consultation with the doctor in case of any health issues. The basic aim of this system is to bridge the vocabulary gap between the doctors by giving self-diagnosis from the comfort of one’s place. The proposed idea is to create a medical chatbot using Artificial Intelligence that can diagnose the disease and provide basic details about the disease before consulting a doctor. To reduce the healthcare costs and improve accessibility to medical knowledge the medical bot is built. Certain bots act as a medical reference books, which helps the patient know more about their disease and helps to improve their health. The user can achieve the real benefit of a bot only when it can diagnose all kind of disease and provide necessary information. Hence, people will have an idea about their health and have the right protection.
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D’Alfonso, Simon. "AI in mental health". Current Opinion in Psychology 36 (diciembre de 2020): 112–17. http://dx.doi.org/10.1016/j.copsyc.2020.04.005.
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Oppermann, Ian. "Regulating AI for health". BMJ Health & Care Informatics Online 30, n.º 1 (diciembre de 2023): e100931. http://dx.doi.org/10.1136/bmjhci-2023-100931.
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A, Arif y Srivastava P. "Revolutionizing Eye Health: AI-Powered Diagnosis and Screening". Open Access Journal of Ophthalmology 9, n.º 2 (2024): 1–6. https://doi.org/10.23880/oajo-16000328.
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Artificial intelligence (AI) has contributed to healthcare, particularly in the field of ophthalmology. This article focuses on how AI has improved detection and diagnosis of common eye diseases such as diabetic retinopathy (DR) and age-related macular degeneration (AMD). With AI-based systems, healthcare professionals can now receive more accurate diagnoses faster and create personalized treatment plans. The article also explains how AI is used to detect eye diseases. This includes collecting data, selecting components, preparing data, training models, analyzing data, developing, and refining models, and making diagnoses. It highlights the advantages of using AI to detect subtle changes in retina, such as high accuracy, early detection, predictive abilities, personalised treatment plans, and remote monitoring. However, there are still challenges to employ AI in healthcare. These include ensuring that data utilized is of good quality, refining algorithms, ensuring that the models are easy to understand, and integrating AI with clinical practice. Everyone involved in healthcare must collaborate to ensure that AI can be utilized to help more people with vision problems globally
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Rajpurkar, Pranav, Emma Chen, Oishi Banerjee y Eric J. Topol. "AI in health and medicine". Nature Medicine 28, n.º 1 (enero de 2022): 31–38. http://dx.doi.org/10.1038/s41591-021-01614-0.
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Mehta, Mehul C., Ingrid T. Katz y Ashish K. Jha. "Transforming Global Health with AI". New England Journal of Medicine 382, n.º 9 (27 de febrero de 2020): 791–93. http://dx.doi.org/10.1056/nejmp1912079.
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KR, Prahlad. "AI Health Chatbot using ML". INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, n.º 05 (16 de mayo de 2024): 1–5. http://dx.doi.org/10.55041/ijsrem33761.
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This project aims to develop a personalized and interactive healthcare chatbot leveraging natural language processing and machine learning. It offers tailored advice based on user symptoms, medical history, and preferences. Integrated with healthcare databases, it provides reliable information and services like symptom analysis, triage recommendations, medication details, and personalized health tips. Seamlessly accessing patient records and appointment schedules within existing healthcare systems ensures a cohesive user experience. The AI healthcare chatbot optimizes services by reducing communication burdens, improving information accessibility, and enhancing patient engagement. Preliminary evaluations demonstrate promising results in user satisfaction and healthcare administration efficiency gains. Keywords: Healthcare chatbot, Symptom analysis, Disease Prediction, Medication information and Machine learning.
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Khan, Ulfat Yunus y Afifa Shaikh. "AI Assisting in Mental Health". International Journal for Research in Applied Science and Engineering Technology 12, n.º 2 (29 de febrero de 2024): 217–23. http://dx.doi.org/10.22214/ijraset.2024.58308.
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Abstract: This research paper explores the transformative impact of artificial intelligence (AI) in the field of mental health counselling, aiming to enhance the effectiveness and accessibility of support services. As the demand for mental health care continues to rise globally, there is a growing need for innovative solutions to bridge the gap between the increasing demand for counselling and the limited availability of human counsellors. Our study focuses on the integration of AI technologies to assist mental health counsellors in various aspects of their practice. Through an extensive review of existing literature, we analyse the potential benefits and challenges associated with implementing AI in counselling settings. The paper highlights AI's ability to augment traditional counselling approaches by offering timely and personalised interventions, improving the overall efficiency of therapeutic processes. Drawing on case studies and pilot programs, this research presents empirical evidence supporting the positive outcomes and user acceptance of AI-assisted counselling interventions. Additionally, the paper discusses potential limitations, such as the need for continuous refinement of algorithms and the importance of addressing concerns related to data security and privacy.
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Joshi, Kalpesh. "AI Mental Health Therapist Chatbot". International Journal for Research in Applied Science and Engineering Technology 11, n.º 11 (30 de noviembre de 2023): 308–11. http://dx.doi.org/10.22214/ijraset.2023.56393.
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Chatbots have become very popular these days as the technology is growing with a very high rate. Due to advancements in the technology chatbots have made our lives easier as we can get to know about many things at our finger tips. So, there are many chatbots available which do the work related to particular things. One such chatbot is ChatGPT, Bard etc. AI chatbots provide a more human like experience with the help of natural language processing and leverage semantics to understand the context of what a person says. Thinking of it we have created a AI Mental Health Therapist Chatbot to provide a medical recommendations according to the problem the user might be facing. It will be able to provide medical support in minimal cost and also recommend the treatment required to the user. This can be a type of advancement in the field of AI which can gain popularity among people. The best AI chatbots can unlock incredible efficiency and also the breadth of AI chatbots available today is incredible.
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Birur, N. Praveen. "AI in Health Care Landscape". Journal of Indian Academy of Oral Medicine and Radiology 36, n.º 3 (julio de 2024): 197–98. https://doi.org/10.4103/jiaomr.jiaomr_229_24.
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Purkar, Ayesha Shehbaz. "MENTAL HEALTH AI CARE CHATBOT". INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, n.º 01 (19 de enero de 2025): 1–9. https://doi.org/10.55041/ijsrem40887.
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Always Mental disability and Mental health care have been overlooked. This is puzzling considering that 8% of the world's population suffers from mental impairments, which are widespread. A scalable option that offers an interactive way to engage consumers in behavioral health interventions powered by artificial intelligence is a chatbots. Anxiety, stress, etc. provides a critical first step in enhancing chatbot design and revealing the advantages and disadvantages of the chatbots. In this report, a customized chatbot framework is proposed with a blended neural network design. The recommended chatbot is a virtual health assistant & it is cost-effective and less time-consuming. It includes chat features, many languages voice input & a recommendation tool to improve users’ mood. To improve the performance of the proposed system, the chatbot is further integrated with
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Cross, Shane, Imogen Bell, Jennifer Nicholas, Lee Valentine, Shaminka Mangelsdorf, Simon Baker, Nick Titov y Mario Alvarez-Jimenez. "Use of AI in Mental Health Care: Community and Mental Health Professionals Survey". JMIR Mental Health 11 (11 de octubre de 2024): e60589-e60589. http://dx.doi.org/10.2196/60589.
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Abstract Background 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. Objective 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). Methods 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. Results 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. Conclusions 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.
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Zhang, Melody, Jillian Scandiffio, Sarah Younus, Tharshini Jeyakumar, Inaara Karsan, Rebecca Charow, Mohammad Salhia y David Wiljer. "The Adoption of AI in Mental Health Care–Perspectives From Mental Health Professionals: Qualitative Descriptive Study". JMIR Formative Research 7 (7 de diciembre de 2023): e47847. http://dx.doi.org/10.2196/47847.
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Background Artificial intelligence (AI) is transforming the mental health care environment. AI tools are increasingly accessed by clients and service users. Mental health professionals must be prepared not only to use AI but also to have conversations about it when delivering care. Despite the potential for AI to enable more efficient and reliable and higher-quality care delivery, there is a persistent gap among mental health professionals in the adoption of AI. Objective A needs assessment was conducted among mental health professionals to (1) understand the learning needs of the workforce and their attitudes toward AI and (2) inform the development of AI education curricula and knowledge translation products. Methods A qualitative descriptive approach was taken to explore the needs of mental health professionals regarding their adoption of AI through semistructured interviews. To reach maximum variation sampling, mental health professionals (eg, psychiatrists, mental health nurses, educators, scientists, and social workers) in various settings across Ontario (eg, urban and rural, public and private sector, and clinical and research) were recruited. Results A total of 20 individuals were recruited. Participants included practitioners (9/20, 45% social workers and 1/20, 5% mental health nurses), educator scientists (5/20, 25% with dual roles as professors/lecturers and researchers), and practitioner scientists (3/20, 15% with dual roles as researchers and psychiatrists and 2/20, 10% with dual roles as researchers and mental health nurses). Four major themes emerged: (1) fostering practice change and building self-efficacy to integrate AI into patient care; (2) promoting system-level change to accelerate the adoption of AI in mental health; (3) addressing the importance of organizational readiness as a catalyst for AI adoption; and (4) ensuring that mental health professionals have the education, knowledge, and skills to harness AI in optimizing patient care. Conclusions AI technologies are starting to emerge in mental health care. Although many digital tools, web-based services, and mobile apps are designed using AI algorithms, mental health professionals have generally been slower in the adoption of AI. As indicated by this study’s findings, the implications are 3-fold. At the individual level, digital professionals must see the value in digitally compassionate tools that retain a humanistic approach to care. For mental health professionals, resistance toward AI adoption must be acknowledged through educational initiatives to raise awareness about the relevance, practicality, and benefits of AI. At the organizational level, digital professionals and leaders must collaborate on governance and funding structures to promote employee buy-in. At the societal level, digital and mental health professionals should collaborate in the creation of formal AI training programs specific to mental health to address knowledge gaps. This study promotes the design of relevant and sustainable education programs to support the adoption of AI within the mental health care sphere.
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Wahl, Brian, Aline Cossy-Gantner, Stefan Germann y Nina R. Schwalbe. "Artificial intelligence (AI) and global health: how can AI contribute to health in resource-poor settings?" BMJ Global Health 3, n.º 4 (agosto de 2018): e000798. http://dx.doi.org/10.1136/bmjgh-2018-000798.
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The field of artificial intelligence (AI) has evolved considerably in the last 60 years. While there are now many AI applications that have been deployed in high-income country contexts, use in resource-poor settings remains relatively nascent. With a few notable exceptions, there are limited examples of AI being used in such settings. However, there are signs that this is changing. Several high-profile meetings have been convened in recent years to discuss the development and deployment of AI applications to reduce poverty and deliver a broad range of critical public services. We provide a general overview of AI and how it can be used to improve health outcomes in resource-poor settings. We also describe some of the current ethical debates around patient safety and privacy. Despite current challenges, AI holds tremendous promise for transforming the provision of healthcare services in resource-poor settings. Many health system hurdles in such settings could be overcome with the use of AI and other complementary emerging technologies. Further research and investments in the development of AI tools tailored to resource-poor settings will accelerate realising of the full potential of AI for improving global health.
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Uğraş Tiryaki, Ebru y Erhan Şimşek. "Artificial Intelligence Applications in Health". Arşiv Kaynak Tarama Dergisi 33, n.º 2 (30 de junio de 2024): 98–105. http://dx.doi.org/10.17827/aktd.1439689.
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General practices (GPs), called family physicians in certain countries, are the cornerstone of primary health care. The increase in average lifespan and, thereby, the number of chronic diseases has recently increased the workload of GPs and decreased the time spent on the patient. Implementations of Artificial intelligence (AI)-powered systems are essential in GPs to facilitate the jobs of health professionals. Implementing AI-driven systems is expected to help health professionals diagnose and treat. AI involves the machine simulation of human cognitive capabilities, encompassing a range of technologies, including deep learning and machine learning. AI is currently being used across various applications in medicine and continues to evolve, and its role in medicine is expected to become increasingly prominent. AI-enhance sensor systems can continuously monitor physiological parameters and generate personalized medicinal therapy. However, the employment of AI in GPs is still in the very early phase. AI is a tool to aid healthcare professionals in improving the accuracy and speed of diagnosis rather than a replacement for their expertise. This review will focus on applying artificial intelligence in general practices (GPs).
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Balcombe, Luke. "AI Chatbots in Digital Mental Health". Informatics 10, n.º 4 (27 de octubre de 2023): 82. http://dx.doi.org/10.3390/informatics10040082.
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Artificial intelligence (AI) chatbots have gained prominence since 2022. Powered by big data, natural language processing (NLP) and machine learning (ML) algorithms, they offer the potential to expand capabilities, improve productivity and provide guidance and support in various domains. Human–Artificial Intelligence (HAI) is proposed to help with the integration of human values, empathy and ethical considerations into AI in order to address the limitations of AI chatbots and enhance their effectiveness. Mental health is a critical global concern, with a substantial impact on individuals, communities and economies. Digital mental health solutions, leveraging AI and ML, have emerged to address the challenges of access, stigma and cost in mental health care. Despite their potential, ethical and legal implications surrounding these technologies remain uncertain. This narrative literature review explores the potential of AI chatbots to revolutionize digital mental health while emphasizing the need for ethical, responsible and trustworthy AI algorithms. The review is guided by three key research questions: the impact of AI chatbots on technology integration, the balance between benefits and harms, and the mitigation of bias and prejudice in AI applications. Methodologically, the review involves extensive database and search engine searches, utilizing keywords related to AI chatbots and digital mental health. Peer-reviewed journal articles and media sources were purposively selected to address the research questions, resulting in a comprehensive analysis of the current state of knowledge on this evolving topic. In conclusion, AI chatbots hold promise in transforming digital mental health but must navigate complex ethical and practical challenges. The integration of HAI principles, responsible regulation and scoping reviews are crucial to maximizing their benefits while minimizing potential risks. Collaborative approaches and modern educational solutions may enhance responsible use and mitigate biases in AI applications, ensuring a more inclusive and effective digital mental health landscape.
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Wagner, Jennifer K., Megan Doerr y Cason D. Schmit. "AI Governance: A Challenge for Public Health". JMIR Public Health and Surveillance 10 (30 de septiembre de 2024): e58358-e58358. http://dx.doi.org/10.2196/58358.
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Abstract The rapid evolution of artificial intelligence (AI) is structuralizing social, political, and economic determinants of health into the invisible algorithms that shape all facets of modern life. Nevertheless, AI holds immense potential as a public health tool, enabling beneficial objectives such as precision public health and medicine. Developing an AI governance framework that can maximize the benefits and minimize the risks of AI is a significant challenge. The benefits of public health engagement in AI governance could be extensive. Here, we describe how several public health concepts can enhance AI governance. Specifically, we explain how (1) harm reduction can provide a framework for navigating the governance debate between traditional regulation and “soft law” approaches; (2) a public health understanding of social determinants of health is crucial to optimally weigh the potential risks and benefits of AI; (3) public health ethics provides a toolset for guiding governance decisions where individual interests intersect with collective interests; and (4) a One Health approach can improve AI governance effectiveness while advancing public health outcomes. Public health theories, perspectives, and innovations could substantially enrich and improve AI governance, creating a more equitable and socially beneficial path for AI development.
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BIRADAR, HARSHA S. "HEALTH NEXUS". INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, n.º 01 (19 de enero de 2025): 1–9. https://doi.org/10.55041/ijsrem40894.
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This feasibility study entitles us to identifying the type of health-related analytics that we can deliver through the application of both AI-based analytics in healthcare as well as other health areas of HMOs. AI is employed in this study for facilitating in diagnosis of different diseases through the analysis of the records of the patients. The implementation of AI in the healthcare sector is called health analytics. For instance, one can compare the records of patients and find characteristics that tie the two together. This is a case study for a pilot implementation of the City of Chicago´s hepatitis screening program. It differentiates the people who have been vaccinated from the people who need to be vaccinated but are not yet vaccinated. In this way, the environment in Chicago would be extremely clean according to our plan. This research would also allow the clinics to notify the respective patients on time for the success of the prevention, treatment, and other medical interventions. Keywords - Healthcare Analytics, AI-Driven Insights, Role-Based Access, Real-Time Predictive Analytics, Web Application Development, Flask Framework, Machine Learning Models
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Geneva Tamunobarafiri Igwama, Ejike Innocent Nwankwo, Ebube Victor Emeihe y Mojeed Dayo Ajegbile. "The role of community health workers in implementing AI-based health solutions in rural areas". International Journal of Biology and Pharmacy Research Updates 4, n.º 1 (30 de agosto de 2024): 001–7. http://dx.doi.org/10.53430/ijbpru.2024.4.1.0026.
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The integration of artificial intelligence (AI) in healthcare holds substantial promise for improving health outcomes, particularly in rural areas where access to medical resources is often limited. Community health workers (CHWs) play a pivotal role in bridging the gap between advanced health technologies and underserved populations. This paper explores the crucial role of CHWs in implementing AI-based health solutions in rural settings, focusing on their contributions to facilitating technology adoption, enhancing healthcare delivery, and addressing local health challenges. CHWs serve as a vital link between healthcare systems and rural communities, often providing essential services in areas with scarce medical professionals. Their involvement in AI-based health solutions is transformative, as they help integrate these technologies into everyday healthcare practices, making advanced tools accessible to those who need them most. AI applications, such as predictive analytics for disease outbreaks, remote monitoring systems, and diagnostic tools, can greatly benefit from the on-the-ground insights and support provided by CHWs. The implementation of AI-based health solutions by CHWs involves several key activities: educating community members about new technologies, assisting with the operation and maintenance of AI tools, and collecting and reporting health data for analysis. CHWs also play a critical role in ensuring that AI solutions are tailored to the specific needs and contexts of rural populations, thereby enhancing their effectiveness and acceptance. Despite the potential benefits, challenges such as technological literacy, resource constraints, and the need for continuous training must be addressed to optimize the impact of AI solutions. This review underscores the importance of equipping CHWs with the necessary skills and resources to leverage AI effectively, thereby improving health outcomes and operational efficiency in rural healthcare settings. In conclusion, the role of community health workers in implementing AI-based health solutions is crucial for extending the benefits of advanced technologies to rural areas. By supporting and integrating AI tools, CHWs can help bridge the healthcare divide, improve health outcomes, and promote more equitable access to healthcare resources.
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Baik, Jaiwook. "AI Techniques for Prognostics and Health Management". Journal of Applied Reliability 19, n.º 3 (30 de septiembre de 2019): 243–55. http://dx.doi.org/10.33162/jar.2019.09.19.3.243.
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Howell, Michael D., Greg S. Corrado y Karen B. DeSalvo. "Three Epochs of Artificial Intelligence in Health Care". JAMA 331, n.º 3 (16 de enero de 2024): 242. http://dx.doi.org/10.1001/jama.2023.25057.
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ImportanceInterest in artificial intelligence (AI) has reached an all-time high, and health care leaders across the ecosystem are faced with questions about where, when, and how to deploy AI and how to understand its risks, problems, and possibilities.ObservationsWhile AI as a concept has existed since the 1950s, all AI is not the same. Capabilities and risks of various kinds of AI differ markedly, and on examination 3 epochs of AI emerge. AI 1.0 includes symbolic AI, which attempts to encode human knowledge into computational rules, as well as probabilistic models. The era of AI 2.0 began with deep learning, in which models learn from examples labeled with ground truth. This era brought about many advances both in people’s daily lives and in health care. Deep learning models are task-specific, meaning they do one thing at a time, and they primarily focus on classification and prediction. AI 3.0 is the era of foundation models and generative AI. Models in AI 3.0 have fundamentally new (and potentially transformative) capabilities, as well as new kinds of risks, such as hallucinations. These models can do many different kinds of tasks without being retrained on a new dataset. For example, a simple text instruction will change the model’s behavior. Prompts such as “Write this note for a specialist consultant” and “Write this note for the patient’s mother” will produce markedly different content.Conclusions and RelevanceFoundation models and generative AI represent a major revolution in AI’s capabilities, ffering tremendous potential to improve care. Health care leaders are making decisions about AI today. While any heuristic omits details and loses nuance, the framework of AI 1.0, 2.0, and 3.0 may be helpful to decision-makers because each epoch has fundamentally different capabilities and risks.
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Chakraverty, Anita. "AI offers window on heart health". Inside Precision Medicine 9, n.º 1 (1 de febrero de 2022): 12–14. http://dx.doi.org/10.1089/ipm.09.01.03.
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The Lancet Regional Health – Europe. "Embracing generative AI in health care". Lancet Regional Health - Europe 30 (julio de 2023): 100677. http://dx.doi.org/10.1016/j.lanepe.2023.100677.
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Desai, Prof M. P., Swarnima Nagapurkar, Nia Gajbhiye, Vedantika Patil y Akash Solunke. "AI Based Mental Health Prediction System". International Journal for Research in Applied Science and Engineering Technology 11, n.º 3 (31 de marzo de 2023): 1559–61. http://dx.doi.org/10.22214/ijraset.2023.49704.
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Abstract: The most common mood disorder in the world, depression has a considerable negative influence on health and functionality as well as profound personal, familial, and society implications. The correct and timely identification of depressionrelated symptoms may have numerous advantages for both doctors and those who are affected. The current work aimed to develop and clinically test a system capable of identifying visual signs of melancholy and supporting physician decisions. Programmable suffering assessment based on visible signals is a rapidly expanding research area. Picture handling and AI computations are the focus of the current thorough evaluation of existing approaches as described in more than sixty distributions during the last 10 years. The current datasets, various information-gathering methods, and visual cues of misery are compiled. The survey depicts estimates for visual element extraction, dimensionality reduction, layout and relapse choosing options, as well as numerous combination techniques. Incorporating a quantitative meta-analysis of announced results based on execution metrics risk-tolerant, it identifies general trends and significant irksome issues to be taken into account in ongoing investigations of programmed sadness appraisal using visible signs either alone or in combination with obvious signals. Additionally, the proposed work used deep learning to predict the level of the downturn as shown by the contribution of current face photographs.
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Webster, Paul. "Tech companies criticise health AI regulations". Lancet 402, n.º 10401 (agosto de 2023): 517–18. http://dx.doi.org/10.1016/s0140-6736(23)01667-7.
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Temple, Victoria. "AI Health Projects Win BCS Prizes". ITNOW 65, n.º 1 (22 de febrero de 2023): 49. http://dx.doi.org/10.1093/combul/bwad025.
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Abstract Two innovative technology projects have been honoured with awards from the BCS Primary Healthcare Specialist Group. Victoria Temple, BCS’ Senior Press Officer, explores how each uses AI to improve patient outcomes and save GPs’ time.
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K, Amit, Russ A y Curt L. "Health Care AI Systems Are Biased". Scientific American 3, n.º 1 (febrero de 2021): None. http://dx.doi.org/10.1038/scientificamerican022021-7i562qnmh6t0dduwu1denh.
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Erlin, Azie, Helma Putri, Ratia Andesfi y Sabarrudin. "AI Benefits in Mental Health Counseling". BICC Proceedings 2 (10 de junio de 2024): 170–75. http://dx.doi.org/10.30983/bicc.v1i1.92.
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The development of artificial intelligence (AI) is increasingly widespread and indispensable in human life such as education, services, and so on. This development is demonstrated by the large number of devices using artificial intelligence or AI-based technology. This device greatly simplifies human work. This study aims to find out the benefits of AI in mental health counseling The research method that is conducted is a literature review that uses data collection sources relevant to this study can be in books, magazines, and other print media, or can be obtained from photographs and videos. In the digital age as it is today, the internet and social media have become an important part of human life. In 2019, about 71% of the global population accessed the internet and 45% of the global population used social media (Kemp, 2019). The development of AI technology in social media, such as content personalization, can affect human behavior and strengthen biases that can affect mental health. For example, personalization of content on social media can make humans exposed to the same view constantly, which can strengthen bias and affect mental health (Fornell & Kocovski, 2018)
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Ramalingam, Ganesh. "Leaveraging AI for Public Health Management". International Journal of Scientific Research and Engineering Trends 10, n.º 4 (15 de julio de 2024): 1224–30. http://dx.doi.org/10.61137/ijsret.vol.10.issue4.215.
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Figueiredo, Mayara Costa, Elizabeth Ankrah, Jacquelyn E. Powell, Daniel A. Epstein y Yunan Chen. "Powered by AI". Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 7, n.º 4 (19 de diciembre de 2023): 1–24. http://dx.doi.org/10.1145/3631414.
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Recently, there has been a proliferation of personal health applications describing to use Artificial Intelligence (AI) to assist health consumers in making health decisions based on their data and algorithmic outputs. However, it is still unclear how such descriptions influence individuals' perceptions of such apps and their recommendations. We therefore investigate how current AI descriptions influence individuals' attitudes towards algorithmic recommendations in fertility self-tracking through a simulated study using three versions of a fertility app. We found that participants preferred AI descriptions with explanation, which they perceived as more accurate and trustworthy. Nevertheless, they were unwilling to rely on these apps for high-stakes goals because of the potential consequences of a failure. We then discuss the importance of health goals for AI acceptance, how literacy and assumptions influence perceptions of AI descriptions and explanations, and the limitations of transparency in the context of algorithmic decision-making for personal health.
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Kracht, Chelsea L., Susan B. Sisson, Kelly Kerr, Devon Walker, Lancer Stephens, Julie Seward, Amber Anderson et al. "Health Care Provider’s Role in Obesity Prevention and Healthy Development of Young American Indian Children". Journal of Transcultural Nursing 30, n.º 3 (3 de agosto de 2018): 231–41. http://dx.doi.org/10.1177/1043659618792605.
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Introduction: Health care providers (HCPs) serving American Indian (AI) populations are critical stakeholders in promoting healthy weight-related behaviors of young AI children. The purpose of this study is to develop an understanding of how HCP perceive their role in the healthy development of young AI children, and how they envision working with early care and education teachers and parents to enhance children’s health. Method: Twenty HCP that serve young AI children in Oklahoma participated in individual interviews. Thematic analysis was conducted on coded transcripts and three main themes, each with two to four subthemes were identified. Results: HCP had limited contact with teachers, felt family health was equal or more important than child health, and parental empowerment and gradual change was essential for success. Conclusion: Creating ways to involve HCP, early care and education teachers, and parents together in multilevel and multisector interventions has the potential to improve the health of young AI children.
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Babkova, Nadiia, Dina Huliieva, Zoia Kochuieva y Nataliia Ugolnikova. "ANALYSIS OF MENTAL HEALTH RESEARCH". Grail of Science, n.º 36 (24 de febrero de 2024): 229–36. http://dx.doi.org/10.36074/grail-of-science.16.02.2024.037.
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In 2022 there are more than 150 million people only in Europe who have mental health problems. The availability of services for population has decreased, deteriorating economic conditions, stress, military conflict make our mental health vulnerable. At the same time, the use of artificial intelligence (AI) makes possible revolutionary breakthroughs in healthcare and medicine. AI technologies are being considered as a new tool for planning, monitoring and identifying health services at level of populations and individuals. AI-powered tools could be used like digitized healthcare data, including electronic records, images and handwritten notes, to automate tasks, make clinicians' jobs easier, and understand the causes of complex diseases. At the same time, the use of AI-based solutions often involves the use of complex statistical and mathematical tools and multi-dimensional data, which raises the possibility of errors and misinterpretation of results: researchers sometimes tend to overtrust AI. There is also concern about the lack of open reporting of the use of AI models, which limits the ability to replicate results. The study showed that data and models used are most often not publicly available, and there is little collaboration between researchers.
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Inamdar, Harshada Joshi. "Artificial Intelligence and Mental Health". International Journal of Nursing Research 10, n.º 03 (2024): 29–31. http://dx.doi.org/10.31690/ijnr.2024.v010i03.007.
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In addition to identifying challenges and prospects for such application, this paper intends to present significant implementation science ideas that may be pertinent to comprehending and aiding artificial intelligence (AI) deployment in mental health care. This paper provides an overview of AI technology in modern mental health care and evaluates recent research developments, with a focus on the developing field of digital psychiatry. AI application in psychiatry to improve our knowledge, diagnosis, and treatment of mental diseases; to address concerns with availability, appeal, and accessibility of mental health-care services. Nursing staff members and other medical professionals, who operate in clinical settings, such as mental health facilities, stand to gain significantly from AI’s potential to increase workplace productivity and efficiency. Using the phrases “AI in mental health,” “diagnosis of mental health disorders,” “artificial intelligence,” and “deep learning,” we searched PubMed psychological testing and psychotherapy are being impacted by cutting-edge technologies such as artificial and Web of science. The research covered in this overview of the literature discusses how intelligence. This is a survey article, hence not applicable. The advantages and moral dilemmas associated with the use of AI in psychiatry are outweighed by its potential uses. More research and advancement are needed to get past these obstacles and guarantee the ethical and safe integration of AI in the psychiatric sector. By doing this, AI can significantly advance both long-term outcomes and the organization of mental health services.
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Van Teijlingen, Alexander, Tell Tuttle, Hamid Bouchachia, Brijesh Sathian y Edwin Van Teijlingen. "Artificial Intelligence and Health in Nepal". Nepal Journal of Epidemiology 10, n.º 3 (30 de septiembre de 2020): 915–18. http://dx.doi.org/10.3126/nje.v10i3.31649.
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The growth in information technology and computer capacity has opened up opportunities to deal with much and much larger data sets than even a decade ago. There has been a technological revolution of big data and Artificial Intelligence (AI). Perhaps many readers would immediately think about robotic surgery or self-driving cars, but there is much more to AI. This Short Communication starts with an overview of the key terms, including AI, machine learning, deep learning and Big Data.
This Short Communication highlights so developments of AI in health that could benefit a low-income country like Nepal and stresses the need for Nepal’s health and education systems to track such developments and apply them locally. Moreover, Nepal needs to start growing its own AI expertise to help develop national or South Asian solutions. This would require investing in local resources such as access to computer power/capacity as well as training young Nepali to work in AI.
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Hodgson, Christine, Dylan Decker, Teresia M. O’Connor, Melanie Hingle y Francine C. Gachupin. "A Qualitative Study on Parenting Practices to Sustain Adolescent Health Behaviors in American Indian Families". International Journal of Environmental Research and Public Health 20, n.º 21 (3 de noviembre de 2023): 7015. http://dx.doi.org/10.3390/ijerph20217015.
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American Indian (AI) adolescents who practice healthy behaviors of sleep, nutrition, physical activity, and limited screen time can lower their lifetime risk of diet-sensitive disease. Little is known about how AI parenting practices influence the health behaviors of youth. The objective of this qualitative study was to explore how a group of AI parents of youths at risk of disease influenced their youth’s health behaviors after a family intervention. A secondary objective was to understand the role of AI parents in supporting and sustaining health behavior change in their youths following the intervention. Semi-structured in-depth interviews were conducted with AI parents (n = 11) and their young adolescents, 10–15 years old (n = 6). Parents reported facilitators to how they enacted healthy lifestyle behaviors, including family togetherness, routines, youth inclusion in cooking, and motivation due to a health condition in the family. Barriers to enacting healthy behaviors included a lack of time, a lack of access to health resources, negative role modeling, and the pervasiveness of screen media. Three major themes about the role of AI parenting emerged inductively from the interview data: “Parenting in nontraditional families”, “Living in the American grab-and-go culture”, and “Being there and teaching responsibility”. The importance of culture in raising youths was emphasized. These findings inform strategies to promote long-term adherence to behavior changes within the intervention. This study contributes to public health conversations regarding approaches for AI youths and families, who are not well represented in previous health behavior research.
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Lavigne, Maxime, Fatima Mussa, Maria I. Creatore, Steven J. Hoffman y David L. Buckeridge. "A population health perspective on artificial intelligence". Healthcare Management Forum 32, n.º 4 (19 de mayo de 2019): 173–77. http://dx.doi.org/10.1177/0840470419848428.
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The burgeoning field of Artificial Intelligence (AI) has the potential to profoundly impact the public’s health. Yet, to make the most of this opportunity, decision-makers must understand AI concepts. In this article, we describe approaches and fields within AI and illustrate through examples how they can contribute to informed decisions, with a focus on population health applications. We first introduce core concepts needed to understand modern uses of AI and then describe its sub-fields. Finally, we examine four sub-fields of AI most relevant to population health along with examples of available tools and frameworks. Artificial intelligence is a broad and complex field, but the tools that enable the use of AI techniques are becoming more accessible, less expensive, and easier to use than ever before. Applications of AI have the potential to assist clinicians, health system managers, policy-makers, and public health practitioners in making more precise, and potentially more effective, decisions.
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Obe Destiny Balogun, Oluwatoyin Ayo-Farai, Oluwatosin Ogundairo, Chinedu Paschal Maduka, Chiamaka Chinaemelum Okongwu, Abdulraheem Olaide Babarinde y Olamide Tolulope Sodamade. "INTEGRATING AI INTO HEALTH INFORMATICS FOR ENHANCED PUBLIC HEALTH IN AFRICA: A COMPREHENSIVE REVIEW". International Medical Science Research Journal 3, n.º 3 (13 de diciembre de 2023): 127–44. http://dx.doi.org/10.51594/imsrj.v3i3.643.
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This study delves into the integration of Artificial Intelligence (AI) within the field of health informatics and its transformative effect on public health outcomes in Africa. It will cover how AI-driven solutions are being implemented to overcome challenges in disease surveillance, healthcare delivery, and public health policy. The paper aims to provide an in-depth analysis of current innovations, the effectiveness of these technological interventions, and their broader implications for health policy and management across the African continent. The integration of Artificial Intelligence (AI) into health informatics holds transformative potential for enhancing public health in Africa. This comprehensive review explores the multifaceted applications, challenges, and opportunities associated with the convergence of AI and health informatics on the African continent. The review encompasses various domains, including disease surveillance, diagnostics, treatment optimization, and public health management. Key themes addressed in the review include the adoption of AI-driven technologies in healthcare, the impact on disease detection and monitoring, and the potential for improving healthcare accessibility in resource-constrained settings. Moreover, the ethical considerations, regulatory challenges, and disparities in technology adoption across diverse African regions are examined, providing insights into the complexities of implementing AI in the African public health landscape. Through an in-depth analysis of current initiatives, case studies, and emerging trends, this review aims to contribute a comprehensive understanding of the opportunities and challenges associated with integrating AI into health informatics for the advancement of public health in Africa. Ultimately, this exploration seeks to inform policymakers, healthcare professionals, and researchers on the critical role AI can play in addressing public health challenges on the continent and fostering sustainable healthcare solutions.
Keywords: Artificial Intelligence, Health Informatics, Health Management, Africa, Review, Disease Surveillance.
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Paik, Kenneth Eugene, Rachel Hicklen, Fred Kaggwa, Corinna Victoria Puyat, Luis Filipe Nakayama, Bradley Ashley Ong, Jeremey N. I. Shropshire y Cleva Villanueva. "Digital Determinants of Health: Health data poverty amplifies existing health disparities—A scoping review". PLOS Digital Health 2, n.º 10 (12 de octubre de 2023): e0000313. http://dx.doi.org/10.1371/journal.pdig.0000313.
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Artificial intelligence (AI) and machine learning (ML) have an immense potential to transform healthcare as already demonstrated in various medical specialties. This scoping review focuses on the factors that influence health data poverty, by conducting a literature review, analysis, and appraisal of results. Health data poverty is often an unseen factor which leads to perpetuating or exacerbating health disparities. Improvements or failures in addressing health data poverty will directly impact the effectiveness of AI/ML systems. The potential causes are complex and may enter anywhere along the development process. The initial results highlighted studies with common themes of health disparities (72%), AL/ML bias (28%) and biases in input data (18%). To properly evaluate disparities that exist we recommend a strengthened effort to generate unbiased equitable data, improved understanding of the limitations of AI/ML tools, and rigorous regulation with continuous monitoring of the clinical outcomes of deployed tools.
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Razumowsky, Alexey I. "Distance education: A ruin of health". Revista Amazonia Investiga 11, n.º 50 (10 de marzo de 2022): 290–98. http://dx.doi.org/10.34069/ai/2022.50.02.27.
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This review essay addresses the problem of interaction between humans and technologies within the educational context. To what extent are the problems and consequences of the measures taken with regard to the introduction of distance learning realized today? The issue of integral combination of many elements of the educational environment is being investigated. On the basis of a variety of literature on educational, social, psychological, and brain sciences, using the methodology of reasoning, the conditions of quality life of the educational process are determined, including first of all the problems of formation of morality, responsibility and initiative of the student, as well as mental and physical health. The actual end result was the establishment of fact that it is fundamentally impossible to replace direct or personal education (upbringing) by its distant, irresponsible, remote form. Transformation of the educational environment through the separation of individuals from each other does not only lead to the exhaustion of opportunities to obtain quality knowledge, but also to chamber loneliness with disastrous consequences for moral, mental and physical health.
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Hedayet, Hridi y Fariha Haseen. "Artificial Intelligence in Public Health: A Review Article". Bangladesh Journal of Bioethics 15, n.º 2 (1 de julio de 2024): 15–19. http://dx.doi.org/10.62865/bjbio.v15i2.108.
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Background: Artificial intelligence (AI) is the simulation of human intelligence processes by machines, such as learning, reasoning, and problem-solving. AI has the potential to transform the field of public health, which is concerned with promoting and protecting the health of populations and preventing diseases. AI can help public health organizations perform their essential functions more efficiently, effectively, and equitably; AI can transform the public health field, but it also poses some challenges and risks that must be addressed carefully and responsibly. Objectives: This paper reviews the current and potential applications of AI in public health, discusses the opportunities and challenges of AI for public health, and provides recommendations for the ethical and responsible use of AI in public health. Results: AI can improve the speed and accuracy of diagnosis, screening, and treatment of various diseases and support disease surveillance, outbreak response, and health systems management. However, AI poses significant challenges and risks, such as ethical, legal, and social implications, data quality and security, algorithmic bias and fairness, and environmental impact. Conclusion: AI has the potential to revolutionize public health, but it comes with risks that must be addressed. Promoting digital literacy, establishing modern data governance frameworks, and investing in advanced data infrastructure and procedures are essential. Public health organizations must also train their workforce to collaborate with AI. By doing so, they can improve health outcomes, reduce health disparities, and advance public health science and practice.
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Sathiyavani, V. y Santhosh B Panjagal. "IoT and AI Based Remote Patient Health Monitoring System". International Journal of Science and Research (IJSR) 13, n.º 7 (5 de julio de 2024): 1526–30. http://dx.doi.org/10.21275/sr24726154606.
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Gejjegondanahalli Yogeshappa, Vedamurthy. "Generative AI and Personalized Health Coaching: Empowering Medicare Beneficiaries". International Journal of Science and Research (IJSR) 13, n.º 10 (5 de octubre de 2024): 1348–58. http://dx.doi.org/10.21275/sr241018111904.
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Pendy, Bimalendu. "Artificial Intelligence in Health Sector of USA". Jurnal Indonesia Sosial Sains 4, n.º 03 (20 de marzo de 2023): 200–208. http://dx.doi.org/10.59141/jiss.v4i03.791.
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The article describes how AI has already made a significant impact in the health sector in the US, with many promising future applications. In medical imaging, for example, AI algorithms have been developed that can detect and classify anomalies with high accuracy rates, potentially reducing the need for unnecessary biopsies or surgeries. Personalized medicine is another area where AI is being used to analyze large amounts of patient data and provide tailored treatments. AI is also being used to improve the accuracy and efficiency of electronic health records (EHRs) by automatically detecting errors and inconsistencies. In drug discovery, AI algorithms can analyze large data sets and predict which compounds are likely to be effective in treating a particular disease, potentially reducing the time and cost of drug development. In the future, AI is expected to play an even greater role in precision medicine, enabling clinicians to develop customized treatment plans based on a patient's individual genetic and medical history. AI is also being developed for use in virtual assistants, which can provide patients with personalized health advice and reminders. Clinical decision support systems are another area where AI is expected to have a significant impact, helping clinicians to make more informed decisions by analyzing patient data and providing recommendations based on the latest research and best practices. Overall, the article suggests that AI has the potential to transform healthcare by improving patient outcomes, reducing costs, and increasing access to care. However, the article also acknowledges that there are challenges to be addressed, such as ensuring the accuracy and fairness of AI algorithms and addressing privacy concerns.
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Singh, Jerome Amir. "Artificial Intelligence and global health: opportunities and challenges". Emerging Topics in Life Sciences 3, n.º 6 (14 de noviembre de 2019): 741–46. http://dx.doi.org/10.1042/etls20190106.
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Artificial Intelligence (AI) offers unprecedented opportunities and challenges for humanity. If AI can be positioned and leveraged correctly, it can rapidly accelerate progress on achieving the United Nations’ Sustainable Development Goals (SDGs), including SDG #3: ‘Ensure healthy lives and promote wellbeing for all at all ages’. Achieving this goal could have a transformative impact on global health. An ethical, transparent and responsible approach to AI development will result in AI translating data into contextually relevant knowledge, conclusions, and impactful actions.
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Yankov, Gergana. "Artificial Intelligence in Health PR". Vocational Education 25, n.º 3 (22 de junio de 2023): 292–300. http://dx.doi.org/10.53656/voc23-513izku.
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What is the role of artificial intelligence (AI) in healthcare public relations? How is it reshaping the world of health PR? Is AI a threat to healthcare PR or becoming their indispensable assistant? The purpose of the scientific report is to answer these and other questions regarding the application of artificial intelligence in health public communications. The methods used for the purpose of this report are the analysis and synthesis of data from bibliographic sources, as well as the analysis of the results of conducting a scientific experiment in a digital environment with artificial intelligence of the latest generation. Results. The role of AI in health PR is increasingly tangible and fully imposed in the system of international health care – the chatbot of the WHO in the Viber communication platform is a universal bridge between the Organization and its audiences and provides multi-layered opportunities for awareness on all possible health issues. An identical result is also on Facebook‘s Messenger platform. AI is gradually changing the business environment in every field, including healthcare. AI in the face of ChatGPT is becoming both an assistant to health PRs and a major rival. Discussion. Recent breakthroughs in AI technologies could significantly and permanently transform health PR and how it is implemented. The PR profession is not a disappearing species, as it is associated with a deep thought process, creativity, contacts, and connections with „living” people. These activities are so far beyond the reach of artificial intelligence, yet the best professionals will stand out and remain. The results of the scientific experiment set a direction for striving for an increasingly accurate representation of health PR specialists in their professional field. Conclusion. ChatGPT can compose relevant texts, slogans, even messages that are highly effective when applied in the practice of health PR. In the foreseeable future, this technological innovation will be further refined. Chatbots as part of the idea of communication through AI is an area where the role of PRs is to carefully consider each message and the logical connection between the messages. The following recommendations emerge for future research on the topic: to monitor the technological progress of AI and to find new niches in health PR activities in which AI finds application.
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Jana, Sunit, Deepshikha Chatterjee, Nikita Pal, Koushik Pal, Kaushik Roy y Surajit Bashak. "AI in Soil Health Monitoring: A Data-Driven Approach". International Journal for Research in Applied Science and Engineering Technology 12, n.º 10 (31 de octubre de 2024): 1327–35. http://dx.doi.org/10.22214/ijraset.2024.64871.
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Abstract: The sustainability of ecosystems and agricultural productivity depend on healthy soil. However, conventional techniques for evaluating soil health are sometimes time-consuming, labor-intensive, and restricted in their geographic reach. New opportunities for scalable, real-time soil health monitoring have been made possible by recent developments in artificial intelligence (AI) and data-driven methodologies. This study examines a thorough AI-powered system for tracking soil health, emphasizing methods for data collection, processing, and predictive modeling. AI models can provide precise forecasts of soil characteristics, health indicators, and possible crop yields by combining data from multiple sources, such as remote sensing, soil sensors, and historical data. This study offers a comprehensive analysis of recent AI applications in soil health monitoring and suggests a reliable, scalable approach intended to incorporate diverse data sources, guaranteeing precise and effective soil health assessment.
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Kruse, Gina, Victor A. Lopez-Carmen, Anpotowin Jensen, Lakotah Hardie y Thomas D. Sequist. "The Indian Health Service and American Indian/Alaska Native Health Outcomes". Annual Review of Public Health 43, n.º 1 (5 de abril de 2022): 559–76. http://dx.doi.org/10.1146/annurev-publhealth-052620-103633.
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The Indian Health Service (IHS) has made huge strides in narrowing health disparities between American Indian and Alaska Native (AI/AN) populations and other racial and ethnic groups. Yet, health disparities experienced by AI/AN people persist, with deep historical roots combined with present-day challenges. Here we review the history of the IHS from colonization to the present-day system, highlight persistent disparities in AI/AN health and health care, and discuss six key present-day challenges: inadequate funding, limited human resources, challenges associated with transitioning services from federal to Tribal control through contracting and compacting, evolving federal and state programs, the need for culturally sensitive services, and the promise and challenges of health technology.
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Arya Pawar, Sidharth Salian, Saad Shaikh, Sarib Siddiqui y Bharati Jadhav. "Study of Web Based Mental Health Applications". International Research Journal on Advanced Engineering Hub (IRJAEH) 2, n.º 11 (15 de noviembre de 2024): 2586–92. http://dx.doi.org/10.47392/irjaeh.2024.0356.
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This paper examines the role of artificial intelligence (AI) in mental health platforms, addressing the increasing demand for innovative solutions in mental healthcare. Traditional diagnostic methods are often subjective, and early detection of mental health disorders like depression, anxiety, and ADHD remains challenging. AI, through machine learning, natural language processing, and wearable sensor data, offers new opportunities for creating digital phenotypes and enhancing diagnostic accuracy. Studies show that AI-driven platforms can analyze large datasets from electronic health records and real-time monitoring tools, providing early warnings and personalized care. AI models have demonstrated promise in identifying patterns that predict mental health conditions, improving intervention timing and outcomes. While AI holds great potential for transforming mental healthcare, ethical considerations, such as data privacy, transparency, and the need for clinically validated tools, remain essential challenges. As AI continues to advance, integrating these technologies responsibly will be crucial for improving access, reliability, and personalization in mental health interventions.