Thematische Bibliographien / AI for health

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Auswahl der wissenschaftlichen Literatur zum Thema „AI for health“

Autor: Grafiati

Veröffentlicht am 1. Februar 2025

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Zeitschriftenartikel zum Thema "AI for health"

Nelson R Saranya, Sharon. „Revolutionizing Health Records: The AI Way“. International Journal of Science and Research (IJSR) 13, Nr. 4 (05.04.2024): 1310–13. http://dx.doi.org/10.21275/sr24417190214.

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Barassi, Veronica, und Rahi Patra. „AI Errors in Health?“ Morals & Machines 2, Nr. 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, Nr. 12 (13.12.2020): 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 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 (Dezember 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, Nr. 1 (Dezember 2023): e100931. http://dx.doi.org/10.1136/bmjhci-2023-100931.

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A, Arif, und Srivastava P. „Revolutionizing Eye Health: AI-Powered Diagnosis and Screening“. Open Access Journal of Ophthalmology 9, Nr. 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 und Eric J. Topol. „AI in health and medicine“. Nature Medicine 28, Nr. 1 (Januar 2022): 31–38. http://dx.doi.org/10.1038/s41591-021-01614-0.

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Mehta, Mehul C., Ingrid T. Katz und Ashish K. Jha. „Transforming Global Health with AI“. New England Journal of Medicine 382, Nr. 9 (27.02.2020): 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, Nr. 05 (16.05.2024): 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 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, und Afifa Shaikh. „AI Assisting in Mental Health“. International Journal for Research in Applied Science and Engineering Technology 12, Nr. 2 (29.02.2024): 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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Dissertationen zum Thema "AI for health"

GLORIOSO, VALERIA PAOLA. „L'universalismo incompiuto.Disuguaglianze socioeconomiche e inquità nell'accesso ai servizi sanitari in Italia“. Doctoral thesis, Università degli Studi di Milano-Bicocca, 2013. http://hdl.handle.net/10281/46373.

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Equity has long been considered an important goal in the health sector. Yet, inequalities between different social groups persist. In particular, lower-status socioeconomic groups tend to suffer higher rates of mortality and morbidity than do higher-status socioeconomic groups. Also, disadvantaged groups often use health care services less, despite generally having higher levels of need. Scholars often argue that these inequalities reflect mainly differences in constraints, rather than differences in preferences, so that they should be considered as inequities, i.e., as an instance of social injustice. The purpose of this dissertation is to investigate whether, and to what extent, patterns of access to health care services deviate from the ideal of horizontal equity – i.e., equal access for equal need – in a country with a universal and egalitarian health care system: Italy. The Italian National Health Service (Servizio Sanitario Nazionale – Ssn) was established in 1978 through a major reform largely inspired by the British Nhs, with the declared goal of providing uniform and comprehensive care to all Italian citizens. The reform rested on the egalitarian principle that health care should be financed according to ability to pay – through general taxation – but distributed according to need, thereby setting out equity objectives in terms of both financial contribution and access to care. There is evidence, however, that these goals have not been fully achieved yet. The present work aims at analyzing this issue and bridging some of the method- ological and substantive gaps in existing research. Using large-scale survey data collected in three different years (1994, 2000, and 2005) and multilevel regression modeling, we investigate whether and how the probability of accessing a compre- hensive set of health care services varies among individuals with equal need but different socioeconomic status (Ses). Along with the standard indicators of health care utilization (GP visits, specialist visits, and inpatient hospital care), we consider two other variables generally neglected in previous research: propensity to take basic medical tests and use of diagnostic services. Moreover, in addition to the usual estimation of global horizontal inequity, we carry out a stratified analysis aimed at providing distinct estimates of inequity for each level of need, as proxied by health status. Overall, the results of this study confirm a well-established finding in the inter- national literature on health care: even countries with a universal and egalitarian public health care system, like Italy, exhibit a certain degree of Ses-related hori- zontal inequity in health services utilization. Specifically, we found a significant amount of pro-rich inequity in the utilization of specialist care, diagnostic services, and basic medical tests. Use of primary care was found inequitable, too, but in favor of the less well-off. Finally, we found that hospitalization is essentially equitable. The stratified analysis of inequity by level of health status uncovers a noteworthy pattern of heterogeneity in the phenomenon of interest: the degree of inequity in health services utilization tends to decrease as health status decreases – and, therefore, as the need for health care increases. Although this trend is neither regular nor strictly monotonic, our tests show that it is not a mere artifact of sampling error. Thus, there are good reasons to assume that as the need for health care increases, its utilization approaches equity. Finally, our analyses show no substantial difference in inequity of access to health care across regions and time. The articulation of the Italian Ssn into twenty different – and relatively autonomous – Regional Health Services was expected to generate some regional variation in inequity of health care utilization. Likewise, the reforms the Ssn underwent during the period 1994-2005 were plausible sources of variation over time. However, no systematic evidence of regional or longitudinal heterogeneity was revealed by our analyses, suggesting that socioeconomic inequity of access to health care is a stable, well-structured phenomenon.

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Schiele, Julian [Verfasser], und Jens O. [Akademischer Betreuer] Brunner. „AI-Enabled Decision Support in Health Care / Julian Schiele ; Betreuer: Jens O. Brunner“. Augsburg : Universität Augsburg, 2020. http://d-nb.info/1217194029/34.

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Gozuyilmaz, Cem. „A System Approach To Occupational Health And Safety Management“. Master's thesis, METU, 2003. http://etd.lib.metu.edu.tr/upload/1095231/index.pdf.

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In this thesis, methods used at present in occupational health and safety management are analyzed and a model safety management system is developed. History, development and recent occupational safety regulations in the United States of America and European Union are introduced to give a sight on this subject in developed countries. The suggested model is evaluated with work accident data taken from a company and hazard and risk analysis methods are used to investigate these accidents. Preventive measures to eliminate and reduce the consequences of these accidents are recommended. Finally a model safety management system which can be used in all types of industry is developed.

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Rahaman, Khan Md Atiqur. „Feasibility Analysis of AI based Wearable Data-driven Solution for Safety and Health in Sweden“. Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-239442.

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This thesis investigates the prospects of AI and IoT based wearable solution in order to enhance the occupational safety and health. Thus this study contributes to find the probable use cases that can be suitable for such a technology. Later also investigation has been done to figure out how appropriate the Swedish market will be to target on. At the beginning of the thesis, it includes an overall scenario about the occupational safety/health globally as well as in Sweden. Later to improve the workplace injuries, how AI based wearable solution can be handy has been visualized. The theoretical framework explains the technical features and working mechanism and how it can implement in a real world. The methods that can be applied for such research has been discussed afterwards. Then investigation has been done to find the probable use cases and Swedish market has been analyzed to verify how fit the solution. The result chapter includes the finding of the analysis thereafter. To conclude, it has been figured out that few of the us cases for Swedish industries can certainly be applicable for such AI based wearable solution to improve the workplace safety scenario.
Denna avhandling undersöker utsikterna för AI och IoT-baserad bärbar lösning för att förbättra arbetssäkerheten och hälsan. Således bidrar denna studie till att hitta de sannolika användningsfall som kan vara lämpliga för en sådan teknik. Senare har också undersökningar gjorts för att ta reda på hur lämpligt den svenska marknaden ska vara inriktad på. I början av avhandlingen ingår det ett övergripande scenario om arbetssäkerhet / hälsa globalt såväl som i Sverige. Senare för att förbättra arbetsplatsskadorna, hur AI-baserad bärbar lösning kan vara användbar har visualiserats. Den teoretiska ramen förklarar de tekniska funktionerna och arbetsmekanismen och hur den kan genomföras i en verklig värld. De metoder som kan tillämpas för sådan forskning har diskuterats efteråt. Sedan har undersökningen gjorts för att hitta de sannolika användningsfallen och den svenska marknaden har analyserats för att verifiera hur lämplig lösningen är. Resultatet kapitlet innehåller analysen av analysen därefter. Avslutningsvis har det visat sig att få av användningsärenden för svenska industrier säkert kan tillämpas för en sådan AI-baserad bärbar lösning för att förbättra arbetssäkerhetsscenariot.

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Dorrikhteh, Masoud. „A Deep-Learning Approach for Marker-less Stride Parameters Analysis with Two Cameras“. Thesis, Université d'Ottawa / University of Ottawa, 2021. http://hdl.handle.net/10393/42511.

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Human gait analysis is an essential indicator for physical and neuroglial health of an individual. Recent developments in deep-learning approaches to computer vision make possible new techniques for body segment and joint detection from photos and video frames. In this thesis, we propose a deep learning approach for non-invasive video-based gait analysis using two RGB cameras that would be suitable for routine gait monitoring in senior care and rehabilitation centers. Due to modularity and the low cost of implementation, it is considered an affordable solution for such centers. Furthermore, since the solution does not require any markers or sensors to be worn, it is a pervasive and easy method for daily usage. Our proposed deep-learning approach starts by calibrating both the intrinsic and extrinsic parameters of the cameras. Next, video streams captured from two RGB cameras are used as input, and OpenPose and HyperPose deep-learning frameworks are used to localize the main body key points, including the joints and skeleton based on Body 25 and COCO models, respectively. The 2D parameter outputs from the frameworks are triangulated into 3D vector spaces for further analysis. In order to reduce the noises in our data, we applied median and dual pass butter worth filters to the data. Finally gait parameters has been extracted measured and compared to the manually evaluated ground truth data which has been capture via manual measurement of a domain expert. The approach was evaluated in a laboratory setting similar to an institutional hallway in five types of trials: walking back and forth in a straight line while turning out of frame, walking back and forth in a straight line while turning in frame, circular walking, walking with a cane and a walker. The method brings promising results compared to more expensive and restrictive approaches that use up to 16 cameras and require markers or sensors.

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Bonarrigo, Manuel. „Panoramica dell'approccio architetturale orientato ai microservizi e analisi dell'applicabilità nel contesto TraumaTracker“. Bachelor's thesis, Alma Mater Studiorum - Università di Bologna, 2018. http://amslaurea.unibo.it/16812/.

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Lo scopo di questa tesi consiste in uno studio di fattibilità dell'eventuale migrazione del progetto TraumaTracker, uno strumento informatico realizzato in collaborazione con il TraumaCenter dell'ospedale Maurizio Bufalini di Cesena, verso un approccio architetturale orientato ai microservizi. Le motivazioni che hanno portato alla considerazione di questa recente concretizzazione della service orientation derivano dalla natura di strumento medico-ospedaliero assunta dal TraumaTracker, dal quale ci si aspetta di ottenere gli stessi livelli di resilienza rispetto ai fallimenti di rete, macchine host ed elettricità di cui gode l'infrastruttura ospedaliera; inoltre viene ricercata la possibilità di uno sviluppo agile e dinamico per rispondere alle necessità ed alla sistematica evoluzione del dominio applicativo. Dopo una parte introduttiva delle infrastrutture coinvolte, in cui vengono esplicitate sia l'infrastruttura di massima del TraumaTracker, così come i principi chiave che caratterizzano l'approccio dei microservizi, vengono delineati i cambiamenti necessari affinchè i servizi che compongono il sistema possano essere considerati effettivamente microservizi, concretizzando l'infrastruttura presente grazie al supporto della piattaforma di virtualizzazione Docker.

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Silvaggi, Fabiola. „Il wellness at work come risposta ai cambiamenti della forza lavoro“. Doctoral thesis, Università degli studi di Bergamo, 2017. http://hdl.handle.net/10446/77127.

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Il mio progetto di ricerca è stato incentrato su due aspetti del wellness at work: il primo inerente la relazione tra la work ability delle persone con malattie croniche e le variabili socio-economiche ad essa correlate (presentismo, assenteismo, produttività al lavoro, ecc) e il secondo relativo alla relazione tra la prevenzione primaria e le condizioni salutari dei lavoratori, al fine di rilevare quale organizzazione del lavoro delle singole imprese sia più idonea a gestire la presenza o il ritorno in attività di una forza-lavoro con problemi di salute o potenzialmente tale. Ai fini di questo proposito, sono stati studiati i programmi e le iniziative di wellness aziendali mirati alla prevenzione e promozione della salute nella loro validità socio-economica. Inoltre, nella prospettiva di garantire un taglio pratico ed operativo al lavoro, oltre allo studio della dottrina accademica, il presente lavoro ha adottato due diverse metodologie: il primo segue la prospettiva che si basa su analisi di tipo deduttivo attraverso questionari e interviste, mentre il secondo segue la prospettiva dei case studies. L’obiettivo che ci si pone attraverso tale integrazione di metodologie è quello di giungere ad una conoscenza più approfondita del fenomeno in oggetto.

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Franzén, Johnny. „Folkhälsovetenskap i en tid av klimatförändringar, digitalisering och orättvisa : En kartläggande studie om den artificiella intelligensens betydelse i hanteringen av klimatrelaterade folkhälsohot“. Thesis, Högskolan i Skövde, Institutionen för hälsovetenskaper, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:his:diva-19855.

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Introduktion: Varje år dör 300 000 människor i världen på grund av klimatförändringarna och 325 miljoner påverkas allvarligt. De som i huvudsak drabbas är människor som redan är utsatta och de finns både i rika och fattiga länder. Dessa konsekvenser leder till stora sociala orättvisor mellan utvecklade länder och outvecklade men även inom nationer, därför kommer en global hälsa i en vidare mening beträffande naturkapital, humankapital och socialt kapital att krävas. Där bördorna delas och skador undviks genom mitigation, anpassning och hälsoprevention. Den pågående digitaliseringen i samhället genom bland annat AI innebär stora möjligheter ur ett folkhälsoperspektiv att arbeta preventivt. Syfte: Syftet är att generera kunskap om hur artificiell intelligens används i ett hälsopreventivt arbete för att begränsa konsekvenserna av klimatförändringar. Metod: En kvalitativ kartläggande litteraturgranskning med en induktiv innehållsanalys. Resultat: Uteslutande så används maskininlärning som AI i artiklarna för att arbeta preventivt och försöka förutse olika hot mot folkhälsan som ett förändrat klimat bidrar till. Klimatkonsekvenserna som förekommer är vektorburna sjukdomar, zoonoser, värmeböljor och luftföroreningar. Artiklarna sträcker sig från år 2016 till 2020 och genomförs i huvudsak i och av utvecklade länder. Slutsats: AI kan användas i hälsopreventivt arbete för att begränsa konsekvenserna av klimatförändringar baserat på teknikens förmåga att förutse hälsohot. Detta är ett viktigt inslag i hälsopreventivt arbete. Forskningsområdet är i sin linda och behöver ta mer hänsyn till etik, rättvisa och hälsans bestämningsfaktorer.
Introduction: Every year, 300,000 people in the world die due to climate change and 325 million are severely affected. Those who are mainly affected are people who are already vulnerable and they are found in both rich and poor countries. This leads to great social injustices between developed countries and undeveloped but also within nations, therefore global health in a broader sense regarding natural capital, human capital and social capital will be required. Where burdens are shared and injuries are avoided through mitigation, adaptation and health prevention. Gradually, digitalization through, among other things, AI permeates the global society more and more and research believes that for public health work, this means great opportunities to work preventively and that health systems become more efficient, safer and cheaper. Aim: The aim is to generate knowledge about how artificial intelligence is used in health prevention work to limit the consequences of climate change. Method: A qualitative scoping review with an inductive content analysis. Results: Machine learning is used in the articles to work preventively and try to predict various threats to public health that climate change contributes to. The climate consequences that occur are vector-borne diseases, zoonoses, heat waves and air pollution. The articles extend from 2016 to 2020 and are mainly implemented in and by developed countries. Conclusion: AI can be used in health prevention work to limit the consequences of climate change based on the technology's ability to predict health threats. This is an important element in health prevention work. The research area is in its infancy and needs to take more account of ethics, justice and health determinants.

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Karlsson, Jennica, und Ann Charlotte Lindberg. „Hur påverkas folkhälsan av Artificiell Intelligens? : En litteraturstudie av hur hälsans sociala bestämningsfaktorer påverkas av AI inom området avsiktliga skador“. Thesis, Högskolan i Skövde, Institutionen för hälsa och lärande, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:his:diva-17128.

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Introduktion: Befolkningens hälsa påverkas i hög grad av hur väl samhället vi lever i fungerar. Samhällen präglade av våld, krig och terror får negativa konsekvenser för människors hälsa. Artificiell Intelligens (AI) är en kraftfull teknik med stora möjligheter att lösa samhällsproblem, men forskare varnar för att AI också kan innebära nya risker. Syfte: Syftet med litteraturstudien är att belysa vilka utmaningar och möjligheter som en ökande utveckling och implementering av AI kan ha på hälsans sociala bestämningsfaktorer inom området avsiktliga skador. Metod: Enligt Denyer och Tranfields (2011) metod genomfördes en systematisk litteraturstudie på 25 tekniska och humanistiska artiklar, som analyserades och syntetiserades med på förhand tydligt uppställda studiefrågor. Resultat: Artiklarna behandlande AI som används inom områden relaterade till avsiktliga skador och kategoriserades som tekniska eller humanistiska. De tekniska artiklarnas syfte var att utveckla AI för problemlösning medan de humanistiska artiklarna analyserade effekterna av implementering av AI. Möjligheterna med AI inkluderade förmågan att göra förutsägelser, fylla kompetensbehov, utnyttja big data och ta fram modeller som kan ge generella lösningar. Utmaningarna bestod av krav på en teoretisk grund, risk för bias, skapa eller välja lämpliga modeller och algoritmer, säkerställa kvalitet på data och databearbetning, validera lösningar, förhindra oavsiktlig användning, tydliggöra ansvar och få acceptans för lösningarna. Utvecklingen och implementeringen av AI påverkar främst tre av hälsans sociala bestämningsfaktorer: stödjande miljöer och friska människor, marknadens ansvar, samt god global styrning. Slutsats: Gemensam global reglering och större samhälleligt engagemang behövs för att säkerställa att konsekvenser för folkhälsan beaktas vid utvecklingen av AI.
Introduction: Public health is affected by the society in which people live. Communities characterized by violence, war and terror have a negative impact on human wellbeing. Artificial Intelligence (AI) is a powerful tool with a capability to solve problems in the society but scientists also warn that AI can induce new types of problems. Aim: The purpose of this literature study is to review which challenges and possibilities an increase in the development and implementation of AI can have on the social determinants of health within the area of deliberate injury. Methods: A systematic literature review was performed according to the method by Denyer and Tranfield (2011) and 25 technical and humanistic articles were analyzed and synthesized according to predefined research questions. Results: The articles about AI in the area of deliberate injuries were categorized as technical or humanistic. The purpose of the technical articles was to develop AI for problem solving while the humanistic articles analyzed the effects of AI implementation. The possibilities with AI included predictions, competence need, big data and models for general purposes. The challenges included theoretical grounds, bias, suitable models and algorithms, data quality and processing, validation, wrongful usage, responsibility and acceptance of the solutions. The development and implementation of AI especially affect three of the social determinants of health: healthy places healthy people, market responsibility and good global governance. Conclusion: A common global control and societal commitment is needed to secure that consequences for the public health is considered while developing AI.

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Johnson, Beverly Elaine. „Attitudes and Perceptions of Mental Health Treatment for Native American Clients“. ScholarWorks, 2017. https://scholarworks.waldenu.edu/dissertations/4524.

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The need for mental health service is increasing in American Indian/Alaska Native (AI/AN) communities. While research has examined the availability, access, and effectiveness of provided services to the AI/AN, very little is known about the influence of the attitude and perceptions of both clinicians and clients in their therapeutic relationship in the treatment process. Using the frameworks of liberation, oppression, and trauma theory, this qualitative phenomenological study explored mental health service delivery and utilization issues within an AI/AN community. Data were collected through semistructured interviews with 14 clinician and client participants. The data were sorted into themes and subthemes and analyzed using the NVivo 11 computer software. Intergenerational struggle represented the primary theme and other subthemes such as assimilation, acculturation, and communication were among some of the secondary themes gathered from the data. Analysis of the themes provided greater insights into the dynamics of the participant's lived experience in various organizational structures within the larger community as well as a better understanding of mental health service delivery and utilization in maintaining sobriety in their daily struggles. The results indicated that intergenerational struggle along with other environmental factors were the chief causes of their cyclical journey through the penal and other systems; thus reducing their ability in maintaining longer sobriety and in improving their mental health. The implications for positive social change in this study include the reduction of stigma associated with these health issues through the education of the community and in training clinicians in factor-specific issues impacting life altering critical events in AI/AN struggles.

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Bücher zum Thema "AI for health"

Lawry, Tom. AI in Health. Boca Raton: Taylor & Francis, 2020.: HIMSS Publishing, 2020. http://dx.doi.org/10.4324/9780429321214.

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Aluvalu, Rajanikanth, Mayuri Mehta und Patrick Siarry, Hrsg. Explainable AI in Health Informatics. Singapore: Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-97-3705-5.

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Yang, Hui, Robin Qiu und Weiwei Chen, Hrsg. AI and Analytics for Public Health. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-75166-1.

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Shaban-Nejad, Arash, Martin Michalowski und Simone Bianco, Hrsg. AI for Health Equity and Fairness. Cham: Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-63592-2.

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xuan, Chen nian. Ai ℗ʺSPA. Bei jing: Ke xue chu ban she, 2004.

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Little, Jean. Ai quan yang hu da quan. Xianggang: Wan li ji gou, wan li shu dian, 1994.

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shi, Liling shi ai guo wei sheng yun dong wei yuan hui Ban gong. Liling ai guo wei sheng zhi. [Liling shi: s.n., 1991.

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Aloulou, Hamdi, Bessam Abdulrazak, Antoine de Marassé-Enouf und Mounir Mokhtari, Hrsg. Participative Urban Health and Healthy Aging in the Age of AI. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-09593-1.

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Pagán, José, Mounir Mokhtari, Hamdi Aloulou, Bessam Abdulrazak und María Fernanda Cabrera, Hrsg. How AI Impacts Urban Living and Public Health. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-32785-9.

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Malik, Hasmat, Nuzhat Fatema und Jafar A. Alzubi, Hrsg. AI and Machine Learning Paradigms for Health Monitoring System. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-33-4412-9.

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Buchteile zum Thema "AI for health"

Wickramasinghe, Nilmini. „Exploring the Potential of ChatGPT, Responsible AI, Explainable AI and Generative AI“. In Digital Health, 138–60. Boca Raton: Chapman and Hall/CRC, 2024. http://dx.doi.org/10.1201/9781003318538-10.

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Lawry, Tom. „When AI Meets HR“. In AI in Health, 91–104. Boca Raton: Taylor & Francis, 2020.: HIMSS Publishing, 2020. http://dx.doi.org/10.4324/9780429321214-10.

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Lawry, Tom. „Engaging Your Clinicians“. In AI in Health, 105–14. Boca Raton: Taylor & Francis, 2020.: HIMSS Publishing, 2020. http://dx.doi.org/10.4324/9780429321214-11.

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Lawry, Tom. „Making Radiologists More Rad“. In AI in Health, 115–21. Boca Raton: Taylor & Francis, 2020.: HIMSS Publishing, 2020. http://dx.doi.org/10.4324/9780429321214-12.

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Lawry, Tom. „Understanding and Managing the Ethics of AI“. In AI in Health, 123–35. Boca Raton: Taylor & Francis, 2020.: HIMSS Publishing, 2020. http://dx.doi.org/10.4324/9780429321214-13.

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Lawry, Tom. „The Role of the Cloud in AI“. In AI in Health, 137–46. Boca Raton: Taylor & Francis, 2020.: HIMSS Publishing, 2020. http://dx.doi.org/10.4324/9780429321214-14.

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Lawry, Tom. „Manage Your Data Estate Like Your Finances“. In AI in Health, 147–56. Boca Raton: Taylor & Francis, 2020.: HIMSS Publishing, 2020. http://dx.doi.org/10.4324/9780429321214-15.

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Lawry, Tom. „The Importance of Intelligent HIT Vendors“. In AI in Health, 157–68. Boca Raton: Taylor & Francis, 2020.: HIMSS Publishing, 2020. http://dx.doi.org/10.4324/9780429321214-16.

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Lawry, Tom. „From Aspiration to Execution“. In AI in Health, 169–78. Boca Raton: Taylor & Francis, 2020.: HIMSS Publishing, 2020. http://dx.doi.org/10.4324/9780429321214-17.

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Lawry, Tom. „The Road Ahead for AI“. In AI in Health, 179–94. Boca Raton: Taylor & Francis, 2020.: HIMSS Publishing, 2020. http://dx.doi.org/10.4324/9780429321214-18.

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Konferenzberichte zum Thema "AI for health"

Siraskar, Warsha Prashant, Parul Dubey, Niharika Das, Pushpa Chutel, Prachi Jain und Rahul Vinayak Bambodkar. „Enhancing Cardiovascular Health through AI-Driven Heart Disease Detection“. In 2024 International Conference on Electrical Electronics and Computing Technologies (ICEECT), 1–6. IEEE, 2024. http://dx.doi.org/10.1109/iceect61758.2024.10738991.

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R, Jayabhaduri, Aadesh Vijayaraghavan, Ajay Karthik R, Ceralaathan G und Sai Sailesh S. „AI Powered Chatbot For Mental Health Treatment“. In 2024 First International Conference on Technological Innovations and Advance Computing (TIACOMP), 168–72. IEEE, 2024. http://dx.doi.org/10.1109/tiacomp64125.2024.00037.

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Prathaban, Banu Priya, R. Subash, Ashwini A und Lakshmi G. „AI based Mental Health Assisted Chatbot System“. In 2024 International Conference on Power, Energy, Control and Transmission Systems (ICPECTS), 1–6. IEEE, 2024. https://doi.org/10.1109/icpects62210.2024.10780017.

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Manoharan, Geetha, Lalit Mohan Pant, Ruchi Tewari, Swapnil Vichare, Prithu Sarkar und Joshuva Arockia Dhanraj. „AI-Powered Chatbots for Mental Health Support“. In 2024 7th International Conference on Contemporary Computing and Informatics (IC3I), 436–40. IEEE, 2024. https://doi.org/10.1109/ic3i61595.2024.10829185.

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Viignesh, M. Ragul, A. Josphin Selsia, M. Bhuvaneswari, Sudanshu Pathak, Kanchan Yadav und G. Amirthayogam. „The Role of AI Techniques in Diagnosing Health Conditions with Integration of AI“. In 2024 4th International Conference on Advance Computing and Innovative Technologies in Engineering (ICACITE), 167–72. IEEE, 2024. http://dx.doi.org/10.1109/icacite60783.2024.10617043.

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Forlesi, Mattia, Alfonso Esposito, Leonardo Ciabattini, Luca Sciullo, Marco Di Felice, Soroosh Kamali, Emma Ghini, Stefano Silvestri und Alessandro Marzani. „An IoT-AI Toolchain for Structural Health Monitoring“. In 2024 IEEE International Symposium on Measurements & Networking (M&N), 1–6. IEEE, 2024. http://dx.doi.org/10.1109/mn60932.2024.10615472.

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Shubham, Shubham, Deepak Banerjee, Deepak Upadhyay, Ramesh Singh Rawat und Suraj. „Mental Health Mapper: AI Strategies for Understanding Disorders“. In 2024 Asia Pacific Conference on Innovation in Technology (APCIT), 1–6. IEEE, 2024. http://dx.doi.org/10.1109/apcit62007.2024.10673674.

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Jiang, Zhanxu. „AI in Personalized Health Management: Practices and Challenges“. In International Conference on Engineering Management, Information Technology and Intelligence, 46–51. SCITEPRESS - Science and Technology Publications, 2024. http://dx.doi.org/10.5220/0012897900004508.

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Wang, Xiaofei, Yi Zhao, Tao Yu, Jie Ren, Huafeng Yan und Jian Song. „Research of AI Driven Light Health and Therapy“. In 2024 IEEE 48th Annual Computers, Software, and Applications Conference (COMPSAC), 2131–34. IEEE, 2024. http://dx.doi.org/10.1109/compsac61105.2024.00341.

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Agliata, Antonio, Antonio Pilato, Sorrentino Mariacarmen, Salvatore Bottiglieri, Emanuel Di Nardo und Angelo Ciaramella. „Generative AI and Emotional Health: Innovations with Haystack“. In 2024 IEEE Symposium on Computers and Communications (ISCC), 1–4. IEEE, 2024. http://dx.doi.org/10.1109/iscc61673.2024.10733569.

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Berichte der Organisationen zum Thema "AI for health"

Smeaton, John, und Lorna Christie. AI and healthcare. Parliamentary Office of Science and Technology, Januar 2021. http://dx.doi.org/10.58248/pn637.

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There are various applications of Artificial Intelligence (AI) in healthcare, such as helping clinicians to make decisions, monitoring patient health, and automating routine administrative tasks. This POSTnote gives an overview of these uses, and their potential impacts on the cost and quality of healthcare, and on the workforce. It summarises the challenges to wider adoption of AI in healthcare, including those relating to safety, privacy, data-sharing, trust, accountability and health inequalities. It also outlines some of the regulations relevant to AI, and how these may change. As healthcare is a devolved issue, policies on healthcare AI differ across the UK. This POSTnote focusses on regulations and policies relevant to England.

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Juanita, Safitri, Mauridhi Hery Purnomo und Diana Purwitasari. AI shows its worth in making sense of online health. Herausgegeben von Ria Ernunsari und Sara Phillips. Monash University, August 2022. http://dx.doi.org/10.54377/3ff3-6cf7.

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Lampkin, Cheryl L. AI in Health Care: Thoughts and Opinions Among the 50+. Washington, DC: AARP Research, August 2024. http://dx.doi.org/10.26419/res.00802.001.

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Bagchi, Devarati, Vidya Prabhu und Divya Sussana Patil. AI and big data analytics in health care: Opportunity or threat? World Evidence-based Healthcare Day, Oktober 2024. http://dx.doi.org/10.70253/vnhr9587.

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Rudd, Ian. Leveraging Artificial Intelligence and Robotics to Improve Mental Health. Intellectual Archive, Juli 2022. http://dx.doi.org/10.32370/iaj.2710.

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Artificial Intelligence (AI) is one of the oldest fields of computer science used in building structures that look like human beings in terms of thinking, learning, solving problems, and decision making (Jovanovic et al., 2021). AI technologies and techniques have been in application in various aspects to aid in solving problems and performing tasks more reliably, efficiently, and effectively than what would happen without their use. These technologies have also been reshaping the health sector's field, particularly digital tools and medical robotics (Dantas & Nogaroli, 2021). The new reality has been feasible since there has been exponential growth in the patient health data collected globally. The different technological approaches are revolutionizing medical sciences into dataintensive sciences (Dantas & Nogaroli, 2021). Notably, with digitizing medical records supported the increasing cloud storage, the health sector created a vast and potentially immeasurable volume of biomedical data necessary for implementing robotics and AI. Despite the notable use of AI in healthcare sectors such as dermatology and radiology, its use in psychological healthcare has neem models. Considering the increased mortality and morbidity levels among patients with psychiatric illnesses and the debilitating shortage of psychological healthcare workers, there is a vital requirement for AI and robotics to help in identifying high-risk persons and providing measures that avert and treat mental disorders (Lee et al., 2021). This discussion is focused on understanding how AI and robotics could be employed in improving mental health in the human community. The continued success of this technology in other healthcare fields demonstrates that it could also be used in redefining mental sicknesses objectively, identifying them at a prodromal phase, personalizing the treatments, and empowering patients in their care programs.

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Strange, Michael. AI and the everyday political-economy of global health : a research protocol. Malmö University, 2020. http://dx.doi.org/10.24834/isbn.9789178775330.

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Maerz, Seraphine. Using AI for Text Analysis in R. Instats Inc., 2024. http://dx.doi.org/10.61700/ti5uexui5ilrd1663.

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This one-day workshop provides a basic introduction to using artificial intelligence for text analysis. Tailored for researchers across a variety of social and health science fields, participants will gain practical skills in building text corpuses, topic modeling, and using ChatGPT, Copilot, and other Large Language Models (LLMs) in R, while addressing ethical considerations and validation techniques for AI-driven research.

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Gelles, Rebecca, Zachary Arnold, Ngor Luong und Jennifer Melot. PARAT – Tracking the Activity of AI Companies. Center for Security and Emerging Technology, Juni 2021. http://dx.doi.org/10.51593/20200100.

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CSET’s Private-sector AI-Related Activity Tracker (PARAT) collects data related to companies’ AI research and development to inform analysis of the global AI sector. The global AI market is already expanding rapidly and is likely to continue growing in the coming years. Identifying “AI companies” helps illustrate the size and health of the AI industry in which they participate as well as the most sought-after skills and experience in the AI workforce.

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Cerulli, Giovanni. Deep Learning and AI for Research in Python. Instats Inc., 2023. http://dx.doi.org/10.61700/g6nxp3uxsvu3l469.

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This seminar is an introduction to Deep Learning and Artificial Intelligence methods for the social, economic, and health sciences using Python. After introducing the subject, the seminar will cover the following methods: (i) Feedforward Neural Networks (FNNs) (ii) Convolutional Neural Networks (CNNs); and (iii) Recursive Neural Networks (RNNs). The course will offer various instructional examples using real datasets in Python. An Instats certificate of completion is provided at the end of the seminar, and 2 ECTS equivalent points are offered.

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Cerulli, Giovanni. Machine Learning and AI for Research in Python. Instats Inc., 2023. http://dx.doi.org/10.61700/b7qz5fpva9dar469.

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This seminar is an introduction to Machine Learning and Artificial Intelligence methods for the social, economic, and health sciences using Python. After introducing the subject, the seminar will cover the following methods: (i) model selection and regularization (Lasso, Ridge, Elastic-net); (ii) discriminant analysis and nearest-neighbor classification; and (iii) artificial neural networks. The course will offer various instructional examples using real datasets in Python. An Instats certificate of completion is provided at the end of the seminar, and 2 ECTS equivalent points are offered.

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