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Artykuły w czasopismach na temat „AI for health”

Autor: Grafiati

Data publikacji: 1 lutego 2025

Data aktualizacji: 31 lipca 2025

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1

Nelson R Saranya, Sharon. "Revolutionizing Health Records: The AI Way." International Journal of Science and Research (IJSR) 13, no. 4 (2024): 1310–13. http://dx.doi.org/10.21275/sr24417190214.

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Barassi, Veronica, and Rahi Patra. "AI Errors in Health?" Morals & Machines 2, no. 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 anal

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Sumit. "AI Health Care Chatbot." International Journal for Modern Trends in Science and Technology 6, no. 12 (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 c

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D’Alfonso, Simon. "AI in mental health." Current Opinion in Psychology 36 (December 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, no. 1 (2023): e100931. http://dx.doi.org/10.1136/bmjhci-2023-100931.

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Annapurna, P. V. B. "Medicare - AI Health Diagnosis." International Journal for Research in Applied Science and Engineering Technology 13, no. 5 (2025): 19–24. https://doi.org/10.22214/ijraset.2025.69010.

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Abstract: The Medicare AI System is an intelligent healthcare assistant that utilizes React, Flask, MongoDB, Random Forest Classifier, and Raspberry Pi to provide accurate and real-time disease prediction and medical recommendations. The system is designed to assist users by allowing them to input symptoms, which are then analysed using machine learning models to predict potential diseases and suggest medications, diet plans, and preventive measures. The frontend is built using React, ensuring a responsive and user-friendly interface where users can enter symptoms and receive health insights.

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A, Arif, and Srivastava P. "Revolutionizing Eye Health: AI-Powered Diagnosis and Screening." Open Access Journal of Ophthalmology 9, no. 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

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Rajpurkar, Pranav, Emma Chen, Oishi Banerjee, and Eric J. Topol. "AI in health and medicine." Nature Medicine 28, no. 1 (2022): 31–38. http://dx.doi.org/10.1038/s41591-021-01614-0.

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Mehta, Mehul C., Ingrid T. Katz, and Ashish K. Jha. "Transforming Global Health with AI." New England Journal of Medicine 382, no. 9 (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, no. 05 (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 comm

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Khan, Ulfat Yunus, and Afifa Shaikh. "AI Assisting in Mental Health." International Journal for Research in Applied Science and Engineering Technology 12, no. 2 (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 revie

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Joshi, Kalpesh. "AI Mental Health Therapist Chatbot." International Journal for Research in Applied Science and Engineering Technology 11, no. 11 (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

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Kokate, Prof Rohan B. "AI Health Task Management System." International Journal for Research in Applied Science and Engineering Technology 13, no. 5 (2025): 6416–22. https://doi.org/10.22214/ijraset.2025.71717.

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Abstract: The "challenge control machine with AI" is an revolutionary answer designed to streamline and decorate the performance of undertaking management for people and companies. In brand new fast-paced world, powerful time management isvital, and this device targets to empower customers to prioritize responsibilities intelligently. The machine incorporates an AI-primarily based mechanism that takes into account different factors, including closing dates, significance, and person-described urgency tiers. Leveraging system mastering, the AI model learns from ancient undertaking statistics to

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Deshpande, Juhi, and Chanchal Kumar Singh. "AI-Powered Menstrual Health Tracking." Greenfort International Journal of Applied Medical Science 3, no. 2 (2025): 40–42. https://doi.org/10.62046/gijams.2025.v03i02.002.

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Artificial intelligence (AI) and machine learning (ML) have improved menstrual health tracking. AI-powered menstrual health tracking systems provide personalized predictions of menstrual cycles, detect irregularities, and offer tailored recommendations. This commentary discusses physiological and psychological correlates of menstrual health and machine learning algorithms for menstrual health tracking. We highlight future research directions, including integration with wearable devices and development of personalized models. AI-powered menstrual health tracking can enhance women's health.

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Purkar, Ayesha Shehbaz. "MENTAL HEALTH AI CARE CHATBOT." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 01 (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

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Birur, N. Praveen. "AI in Health Care Landscape." Journal of Indian Academy of Oral Medicine and Radiology 36, no. 3 (2024): 197–98. https://doi.org/10.4103/jiaomr.jiaomr_229_24.

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Cross, Shane, Imogen Bell, Jennifer Nicholas, et al. "Use of AI in Mental Health Care: Community and Mental Health Professionals Survey." JMIR Mental Health 11 (October 11, 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, sp

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Roopnarinesingh, Urmala, Alan Whiteman, Carly Blum, and Nicole Peterson. "The Impact of Artificial Intelligence on Graduate Health Administration and Public Health Education." Healthcare Administration Leadership & Management Journal 3, no. 4 (2025): 189–92. https://doi.org/10.55834/halmj.5035523444.

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In 2025, AI is expected to transform healthcare operations, education, and public health. AI is being implemented to streamline operations to improve patient outcomes by automating administrative tasks, enhancing diagnostic accuracy through image analysis, and creating refined personalized treatment plans. Furthermore, AI-powered virtual assistants are also being used by healthcare providers to increase the quality of patient care and improve patient outcomes. With these advancements, healthcare leaders can shift their focus to strategic decision-making and tackling workforce shortages. Simult

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Lai-Ling Lam, Maria. "Increasing Adoptive Capacities of Innovative Health Technologies in the Global Health Care System." DARNIOS APLINKOS VYSTYMAS 22, no. 1 (2025): 143–51. https://doi.org/10.52320/dav.v22i1.381.

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AI usage in healthcare is still in its infancy and has not yet reached its potential to make the global healthcare system more equitable and safe. There is a substantial gap between AI promises and its actual delivery in healthcare settings. AI is a social-technical system and AI technology alone cannot solve our health equity issue. This is the research question: What social, political, and economic elements in the global health system must be addressed such that the capacities of AI can be optimized? The paper employs qualitative research to seek expert opinions, investigate the success case

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Zhang, Melody, Jillian Scandiffio, Sarah Younus, et al. "The Adoption of AI in Mental Health Care–Perspectives From Mental Health Professionals: Qualitative Descriptive Study." JMIR Formative Research 7 (December 7, 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 an

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Wahl, Brian, Aline Cossy-Gantner, Stefan Germann, and Nina R. Schwalbe. "Artificial intelligence (AI) and global health: how can AI contribute to health in resource-poor settings?" BMJ Global Health 3, no. 4 (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 ge

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Uğraş Tiryaki, Ebru, and Erhan Şimşek. "Artificial Intelligence Applications in Health." Arşiv Kaynak Tarama Dergisi 33, no. 2 (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 t

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Balcombe, Luke. "AI Chatbots in Digital Mental Health." Informatics 10, no. 4 (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, comm

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BIRADAR, HARSHA S. "HEALTH NEXUS." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 01 (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´

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Wagner, Jennifer K., Megan Doerr, and Cason D. Schmit. "AI Governance: A Challenge for Public Health." JMIR Public Health and Surveillance 10 (September 30, 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 concep

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Parija, Subhash C., and Padmavathi Shanmuganathan. "Artificial Intelligence in Health Care." Annals of SBV 10, no. 2 (2022): 1. https://doi.org/10.5005/jp-journals-10085-9112.

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Currently, plenty of everyday applications are available that are integrated with artificial intelligence (AI) to enhance the lifestyle of people. A few of the classical examples of AI-integrated applications include the Google Assistant, Tesla self-driving cars, and devices used for home automation. These, in general, attempt to reduce the workload and help to complete complex tasks in an easy way. The healthcare system is not an exception in utilizing such AI and enhancing the overall functioning of the system. There are many applications of AI in certain potential areas of drug discovery, c

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Razumowsky, Alexey I. "Distance education: A ruin of health." Revista Amazonia Investiga 11, no. 50 (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

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Sateesh Kumar Rongali and Durga Bramarambika Sailaja Varri. "AI in health care threat detection." World Journal of Advanced Research and Reviews 25, no. 3 (2025): 1784–89. https://doi.org/10.30574/wjarr.2025.25.3.0552.

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Medical detection and management through Artificial Intelligence (AI) constitutes a transformative healthcare force which identifies and handles health threats including infectious diseases combined with chronic conditions and new worldwide health challenges. Worldwide healthcare systems reveal extensive problems that the fast-evolving AI technologies encompassing ML, DL and NLP demonstrate ability to resolve. AI stands as a promising solution to minimize both health threats' mortality rates and morbidity through diagnostic process automation as well as surveillance capabilities improvement an

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Geneva Tamunobarafiri Igwama, Ejike Innocent Nwankwo, Ebube Victor Emeihe, and 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, no. 1 (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 v

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Srivastav, Saumya, and Meenu Kaushik. "Enhancing Mental Health Support through Human-AI Collaboration: Toward Secure and Empathetic AI-enabled Chatbots." International Journal of Research Publication and Reviews 6, no. 5 (2025): 11749–55. https://doi.org/10.55248/gengpi.6.0525.18130.

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Arunkumar, Paramasivan. "AI-Powered Population Health Management: Addressing Public Health Challenges with Predictive Insights." International Journal on Science and Technology 14, no. 1 (2023): 1–18. https://doi.org/10.5281/zenodo.14551607.

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PHM, in essence, therefore, forms one of the key aspects of contemporary health care systems with goals of enhancing the health status of entire communities. Applying AI for PHM has been identified as a new generation thatausess insight and data fordecision-makingg to solve multifaceted problems in public health. In this paper, the author is trying to understand how the use of AI technologies and methods assists in the early detection of threats to health, efficient utilization of available resources and encouragement of prevention measures. Through applying big data, analytics, artificial int

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Figueiredo, Mayara Costa, Elizabeth Ankrah, Jacquelyn E. Powell, Daniel A. Epstein, and Yunan Chen. "Powered by AI." Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 7, no. 4 (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

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Sarkar, Arindam, Titli Nath, Debanjaly Bhattacharjee, et al. "Arogya Sahayak: AI-Powered Health Navigator App." International Journal of Enhanced Research in Science, Technology & Engineering 14, no. 06 (2025): 233–47. https://doi.org/10.55948/ijerste.2025.0634.

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In today's busy world, staying healthy can be tough with so many health apps around. Our research takes on this challenge by introducing an innovative solution—the AI-powered Health Navigator App. This cutting-edge app comprehensively tracks a wide range of health factors, from daily physical activity to dietary habits, menstrual cycles, and past health records, offering users valuable insights and analytics. What sets it apart are its Disease Detection Module, Symptoms Analyzer, and AI-powered Medical Adviser, which harness users' historical data, medical records, and tracked information to d

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Howell, Michael D., Greg S. Corrado, and Karen B. DeSalvo. "Three Epochs of Artificial Intelligence in Health Care." JAMA 331, no. 3 (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 wit

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Baik, Jaiwook. "AI Techniques for Prognostics and Health Management." Journal of Applied Reliability 19, no. 3 (2019): 243–55. http://dx.doi.org/10.33162/jar.2019.09.19.3.243.

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Chakraverty, Anita. "AI offers window on heart health." Inside Precision Medicine 9, no. 1 (2022): 12–14. http://dx.doi.org/10.1089/ipm.09.01.03.

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Ramalingam, Ganesh. "Leaveraging AI for Public Health Management." International Journal of Scientific Research and Engineering Trends 10, no. 4 (2024): 1224–30. http://dx.doi.org/10.61137/ijsret.vol.10.issue4.215.

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The Lancet Regional Health – Europe. "Embracing generative AI in health care." Lancet Regional Health - Europe 30 (July 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, and Akash Solunke. "AI Based Mental Health Prediction System." International Journal for Research in Applied Science and Engineering Technology 11, no. 3 (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 handlin

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Webster, Paul. "Tech companies criticise health AI regulations." Lancet 402, no. 10401 (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, no. 1 (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, and Curt L. "Health Care AI Systems Are Biased." Scientific American 3, no. 1 (2021): None. http://dx.doi.org/10.1038/scientificamerican022021-7i562qnmh6t0dduwu1denh.

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Erlin, Azie, Helma Putri, Ratia Andesfi, and Sabarrudin. "AI Benefits in Mental Health Counseling." BICC Proceedings 2 (June 10, 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.

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Varsha, Varsha. "Integrating AI into Pediatric Health Management." International Journal of Scientific Research and Engineering Trends 11, no. 2 (2025): 1981–85. https://doi.org/10.61137/ijsret.vol.11.issue2.377.

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Mr. N. Manimozhiyan, Mr. U. Arulkumar, Mr. D. Pon Ezhilan, Mr. R. Vinoth Kumar, and Mrs. J. Veerendeswari. "AI Chatbot for Enhancing Mental Health." International Research Journal on Advanced Engineering Hub (IRJAEH) 3, no. 04 (2025): 1900–1903. https://doi.org/10.47392/irjaeh.2025.0276.

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This project introduces a healthcare chatbot designed to improve mental well-being through mood-responsive interactions. The system integrates a Large Language Model (LLM)-powered contextual understanding, enabling adaptive and personalized responses to user input. By analyzing users' language patterns, the chatbot adapts its responses to provide context-aware, empathetic support that aligns with the user's current emotional state. Built on the Rasa framework with Natural Language Understanding (NLU) capabilities, this chatbot offers personalized, therapeutic conversations aimed at alleviating

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Webster, Paul. "US AI regulatory rollback and health." Lancet 405, no. 10490 (2025): 1654–56. https://doi.org/10.1016/s0140-6736(25)00980-8.

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Kanagalakshmi, Murugan. "AI in Health Care Claim Processing." International Journal of Leading Research Publication 3, no. 11 (2022): 1–5. https://doi.org/10.5281/zenodo.14673227.

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McCarthy, Claudine. "Using AI to address mental health." Disability Compliance for Higher Education 31, no. 1 (2025): 1–4. https://doi.org/10.1002/dhe.32014.

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NEW ORLEANS — Imagine you’re pouring out your struggles about your eating disorder to Tessa, your therapist, and all seems to be going well — until your virtual appointment is sharply punctuated by a response that's insensitive, unethical, or wildly inappropriate, like dieting advice. It may seem like a highly unlikely hypothetical scenario, but it's actually a real‐life example of what happened with a pilot chatbot called Tessa, which has since been discontinued (thankfully!).

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Kracht, Chelsea L., Susan B. Sisson, Kelly Kerr, et al. "Health Care Provider’s Role in Obesity Prevention and Healthy Development of Young American Indian Children." Journal of Transcultural Nursing 30, no. 3 (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,

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Babkova, Nadiia, Dina Huliieva, Zoia Kochuieva, and Nataliia Ugolnikova. "ANALYSIS OF MENTAL HEALTH RESEARCH." Grail of Science, no. 36 (February 24, 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

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