TENCON 2021 - 2021 IEEE Region 10 Conference (TENCON), Год журнала: 2024, Номер unknown, С. 563 - 566
Опубликована: Дек. 1, 2024
Язык: Английский
Medicine, Год журнала: 2024, Номер 103(22), С. e38352 - e38352
Опубликована: Май 31, 2024
This study aimed to evaluate the readability, reliability, and quality of responses by 4 selected artificial intelligence (AI)-based large language model (LLM) chatbots questions related cardiopulmonary resuscitation (CPR). was a cross-sectional study. Responses 100 most frequently asked about CPR (ChatGPT-3.5 [Open AI], Google Bard [Google Gemini Perplexity [Perplexity AI]) were analyzed for quality. The following question: “What are cardio pulmonary resuscitation?” in English. Each queries derived from individually posed chatbots. 400 or patient education materials (PEM) assessed reliability using modified DISCERN Questionnaire, Journal American Medical Association Global Quality Score. Readability assessment utilized 2 different calculators, which computed readability scores independently metrics such as Flesch Reading Ease Score, Flesch-Kincaid Grade Level, Simple Measure Gobbledygook, Gunning Fog Automated Index. Analyzed each When values median results obtained Calculators 1 compared with 6th-grade reading level, there highly significant difference between groups ( P < .001). Compared all formulas, level above 6 th grade. It can be seen that order easy difficult is Bard, Perplexity, Gemini, ChatGPT-3.5. text content provided found level. We believe enhancing quality, PEMs will lead easier understanding readers more accurate performance CPR. So, patients who receive bystander may experience an increased likelihood survival.
Язык: Английский
Процитировано
8
Journal of Medical Internet Research, Год журнала: 2025, Номер 27, С. e66032 - e66032
Опубликована: Март 6, 2025
Health communication and promotion on the internet have evolved over time, driven by development of new technologies, including generative artificial intelligence (GenAI). These technological tools offer opportunities for both public professionals. However, these advancements also pose risks exacerbating health disparities. Limited research has focused combining mediums, particularly those enabled technologies like GenAI, their applications Therefore, this viewpoint, adopting a conceptual approach, provides an updated overview mediums role in understanding disparities GenAI era. Additionally, associated with are briefly discussed through lens Technology Acceptance Model 2, uses gratifications theory, knowledge gap hypothesis. This viewpoint discusses limitations barriers previous internet-based regarding real-time responses, personalized advice, follow-up inquiries, highlighting potential technology promotion. It caused such as individuals' inability to evaluate information, restricted access services, lack skill development. Overall, study lays groundwork future how could be leveraged its challenges may exacerbate inequities. underscores need more empirical studies, well importance enhancing digital literacy increasing socially disadvantaged populations.
Язык: Английский
Процитировано
1
Medicine, Год журнала: 2025, Номер 104(11), С. e41780 - e41780
Опубликована: Март 14, 2025
It is clear that artificial intelligence-based chatbots will be popular applications in the field of healthcare near future. known more than 30% world's population suffers from chronic pain and individuals try to access health information they need through online platforms before applying hospital. This study aimed examine readability, reliability quality responses given by 3 different intelligence (ChatGPT, Gemini Perplexity) frequently asked questions about pain. In this study, 25 most used keywords related were determined using Google Trend every chatbots. The readability response texts was Flesch Reading Ease Score (FRES), Simple Measure Gobbledygook, Gunning Fog Flesch-Kincaid Grade Level scoring. Reliability assessment Journal American Medical Association (JAMA), DISCERN scales. Global Quality Ensuring Information for Patients (EQIP) score assessment. As a result search, first as "back pain," "stomach "chest pain." answers all higher recommended 6th grade level (P < .001). evaluation, order easy difficult Gemini, ChatGPT Perplexity. Higher GQS scores = .008) detected compared other Perplexity had JAMA, EQIP chatbots, respectively .001, P .05). has been ChatGPT, pain-related are read their low. can stated these cannot replace comprehensive medical consultation. applications, it may facilitate text content, create containing reliable references, control them supervisory expert team.
Язык: Английский
Процитировано
1
Journal of Computing Theories and Applications, Год журнала: 2024, Номер 1(4), С. 421 - 445
Опубликована: Апрель 13, 2024
Artificial Intelligence (AI) has been applied to many human endeavors, and epidemiology is no exception. The AI community recently seen a renewed interest in applying methods approaches epidemiological problems. However, number of challenges are impeding the growth field. This work reviews uses applications from 1994 2023. following themes were uncovered: epidemic outbreak tracking surveillance, Geo-location visualization epidemics data, Tele-Health, vaccine resistance hesitancy sentiment analysis, diagnosis, predicting monitoring recovery mortality, decision support systems. Disease detection received most during time under review. Furthermore, found be used epidemiology: prediction, geographic information systems (GIS), knowledge representation, analytics, contagion warning systems, classification. Finally, makes findings: absence benchmark datasets for purposes, need develop ethical guidelines regulate development as this major issue it’s growth, concerted continuous collaboration between Epidemiology experts grow field, explainable privacy retaining more secured understandable solutions.
Язык: Английский
Процитировано
3
Professional Discourse & Communication, Год журнала: 2025, Номер 7(1), С. 70 - 88
Опубликована: Март 17, 2025
The article aims to explore the potential of generative artificial intelligence (AI) for assessing written work and providing feedback on it. goal this research is determine possibilities limitations AI when used evaluating students’ production feedback. To accomplish aim, a systematic review twenty-two original studies was conducted. selected were carried out in both Russian international contexts, with results published between 2022 2025. It found that criteria-based assessments made by models align those instructors, surpasses human evaluators its ability assess language argumentation. However, reliability evaluation negatively affected instability sequential assessments, hallucinations models, their limited account contextual nuances. Despite detailisation constructive nature from AI, it often insufficiently specific overly verbose, which can hinder student comprehension. Feedback primarily targets local deficiencies, while pay attention global issues, such as incomplete alignment content assigned topic. Unlike provides template-based feedback, avoiding indirect phrasing leading questions contributing development self-regulation skills. Nevertheless, these shortcomings be addressed through subsequent queries model. also students are open receiving AI; however, they prefer receive instructors peers. discussed context using formulating foreign instructors. conclusion emphasises necessity critical approach assessment importance training effective interaction technologies.
Язык: Английский
Процитировано
0
ACM Transactions on Computing for Healthcare, Год журнала: 2025, Номер unknown
Опубликована: Март 19, 2025
Post-Traumatic Stress Disorder (PTSD) is a prevalent disorder triggered by life-threatening trauma, and exposure therapy, which involves confronting traumatic stimuli, has been proven to be highly effective for treating PTSD. However, therapy not widely adopted. Virtual Reality (VR) shown comparable effectiveness that of traditional methods, therefore advancing. this broadly implemented, partly because the time required create VR experiences tailored patient’s specific trauma. To address problem, study proposes system generates auditory using Large Language Model (LLM) natural language interaction. This system, built on LLM an audio dataset, sounds matching user-provided themes corresponding scenarios coordinates. An experiment with clinicians generate stimuli was conducted assess usability therapeutic potential generated audio. The results indicated high quality, requiring minimal adjustments applications. Notably, within duration standard clinical session. challenges remain, particularly complex themes, highlighting need further research enhance verify system’s feasibility efficacy.
Язык: Английский
Процитировано
0
Journal of Computer Information Systems, Год журнала: 2025, Номер unknown, С. 1 - 34
Опубликована: Апрель 1, 2025
Язык: Английский
Процитировано
0
Applied Sciences, Год журнала: 2025, Номер 15(8), С. 4090 - 4090
Опубликована: Апрель 8, 2025
The increasing reliance on artificial intelligence in cybersecurity has broadened the role of generative tasks such as text generation and translation. This study assesses effectiveness conventional translation tools translating early vulnerability alerts from English to Spanish—a critical process for ensuring timely dissemination information. Utilizing a dataset provided by Spanish National Cybersecurity Institute, translations were generated using various systems evaluated through linguistic assessment metrics, including methods measuring lexical similarity others capturing semantic meaning beyond direct word matching. Additionally, embeddings employed enhance accuracy analysis. results indicate that generally exhibit greater structural fidelity, whereas produces more natural-sounding translations. However, this flexibility variability quality. findings suggest while may serve valuable complement traditional tools, its inconsistencies limit suitability highly technical content demands precision. underscores importance integrating both approaches improve accessibility across different languages.
Язык: Английский
Процитировано
0
European Archives of Oto-Rhino-Laryngology, Год журнала: 2024, Номер 281(11), С. 6093 - 6097
Опубликована: Авг. 22, 2024
Язык: Английский
Процитировано
3
Advances in web technologies and engineering book series, Год журнала: 2024, Номер unknown, С. 430 - 469
Опубликована: Авг. 22, 2024
This study explores the transformational potential of generative artificial intelligence (GAI) in commerce fraud detection and prevention within e-commerce, highlighting growing risk fraudulent activities due to rise online transactions data-driven various industries, including finance, healthcare. Conventional rule-based systems often fail keep up with evolving strategies, whereas GAI, employing tools like GANs variational autoencoders, can generate synthetic yet realistic data uncover sophisticated schemes. The chapter presents successful real-world examples GAI applications, emphasizing need for ethical considerations, such as privacy bias prevention, ensure responsible AI implementation. concludes that offers a potent, adaptive, strategy combat fraud, promising safer digital environment if implications are carefully managed.
Язык: Английский
Процитировано
2