AI-enhanced healthcare management during natural disasters: conceptual insights

Engineering Science & Technology Journal, Год журнала: 2024, Номер 5(5), С. 1794 - 1816

Опубликована: Май 21, 2024

Natural disasters often lead to significant disruptions in healthcare delivery, exacerbating the already formidable challenges faced by systems. Leveraging artificial intelligence (AI) offers a promising approach mitigate these and enhance management during after natural disasters. This conceptual paper aims propose framework for integration of AI into disaster response efforts, with focus on optimizing resource allocation, improving patient triage, enhancing overall system resilience. Through comprehensive review existing literature, this identifies gaps current practices explores potential address shortcomings. By analyzing case studies examples from previous disasters, highlights transformative impact that technologies such as predictive analytics, machine learning, robotics can have delivery crisis situations. The objectives are twofold: define strategic incorporating protocols outline expected outcomes implementing framework. Expected benefits include expedited triage processes, more accurate improved communication systems, ultimately leading better enhanced efficiency. proposed emphasizes importance interdisciplinary collaboration between professionals, technologists, policymakers, experts. It also addresses ethical considerations associated implementation settings. In conclusion, underscores critical role bolstering capabilities leveraging technologies, systems become adaptive, responsive, resilient face unforeseen challenges, saving lives minimizing communities. Keywords: AI-Enhanced Healthcare Management, Disasters, Conceptual Insights.

Язык: Английский

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Integrative analysis of AI-driven optimization in HIV treatment regimens

Computer Science & IT Research Journal, Год журнала: 2024, Номер 5(6), С. 1314 - 1334

Опубликована: Июнь 7, 2024

The integration of artificial intelligence (AI) into HIV treatment regimens has revolutionized the approach to personalized care and optimization strategies. This study presents an in-depth analysis role AI in transforming treatment, focusing on its ability tailor therapy individual patient needs enhance outcomes. AI-driven involves utilization advanced algorithms computational techniques analyze vast amounts data, including genetic information, viral load measurements, history. By harnessing power machine learning predictive analytics, can identify patterns trends data that may not be readily apparent human clinicians. One key benefits is personalize based characteristics disease progression. considering factors such as drug resistance profiles, comorbidities, lifestyle factors, recommend most effective well-tolerated options for each patient, leading improved adherence clinical Furthermore, enables continuous monitoring adjustment real time, allowing healthcare providers respond rapidly changes status evolving dynamics. proactive management help prevent failure development resistance, ultimately better long-term outcomes patients. Despite transformative potential, without challenges. Ethical considerations, privacy concerns, need robust validation regulatory oversight are all important must addressed ensure safe implementation practice. In conclusion, integrative presented this underscores significant impact personalization regimens. leveraging technologies, approaches needs, quality life people living with HIV. Keywords: Integrative Analysis, AI- Driven, Optimization, Treatment, Regimens.

Язык: Английский

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Innovations in real-time infectious disease surveillance using AI and mobile data

International Medical Science Research Journal, Год журнала: 2024, Номер 4(6), С. 647 - 667

Опубликована: Июнь 6, 2024

The integration of artificial intelligence (AI) and mobile health data has ushered in a new era real-time infectious disease surveillance, offering unprecedented insights into dynamics enabling proactive public interventions. This paper explores the innovative applications AI transforming traditional surveillance systems for diseases. By harnessing power algorithms, coupled with vast amount generated from devices, researchers authorities can now monitor outbreaks greater accuracy efficiency. AI-driven predictive models analyze diverse datasets, including demographic information, travel patterns, social media activity, to detect early signs emergence predict potential outbreaks. use provides wealth information that was previously inaccessible methods. Mobile apps, wearables, other connected devices enable continuous monitoring individuals' indicators, allowing detection symptoms rapid response threats. Furthermore, geolocation facilitates tracking population movements identification high-risk areas transmission. However, this approach also presents challenges ethical considerations. Privacy concerns regarding collection must be carefully addressed ensure rights are protected. Additionally, issues related quality, interoperability, algorithm bias need mitigated reliability effectiveness systems. In conclusion, holds immense promise revolutionizing surveillance. leveraging these technologies, gain valuable dynamics, enhance capabilities, implement targeted interventions prevent spread it is essential address considerations associated its responsible effective implementation. Keywords: Innovations, Real-Time Infectious Disease, Surveillance, AI, Data.

Язык: Английский

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From experts’ perspective, Factors Affecting the Effectiveness of Online Educational Programs in Promoting the Health Literacy of MS Patients: A Grounded Theory Approach

Patient Education and Counseling, Год журнала: 2025, Номер 134, С. 108673 - 108673

Опубликована: Янв. 17, 2025

Язык: Английский

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Medical Misinformation in AI-Assisted Self-Diagnosis: The EvalPrompt Method for Analyzing Large Language Models (Preprint)

JMIR Formative Research, Год журнала: 2025, Номер 9, С. e66207 - e66207

Опубликована: Янв. 29, 2025

Abstract Background Rapid integration of large language models (LLMs) in health care is sparking global discussion about their potential to revolutionize quality and accessibility. At a time when improving access remains critical concern for countries worldwide, the ability these pass medical examinations often cited as reason use them training diagnosis. However, impact inevitable self-diagnostic tool role spreading misinformation has not been evaluated. Objective This study aims assess effectiveness LLMs, particularly ChatGPT, from perspective an individual self-diagnosing better understand clarity, correctness, robustness models. Methods We propose comprehensive testing methodology evaluation LLM prompts (EvalPrompt). uses multiple-choice licensing examination questions evaluate responses. Experiment 1 ChatGPT with open-ended mimic real-world self-diagnosis cases, experiment 2 performs sentence dropout on correct responses missing information. Humans then returned by both experiments ChatGPT. Results In 1, we found that ChatGPT-4.0 was deemed 31% (29/94) nonexperts experts, only 34% (32/94) agreement between groups. Similarly, 2, which assessed robustness, 61% (92/152) continued be categorized all assessors. As result, comparison passing threshold 60%, considered incorrect unclear, though robust. indicates sole reliance could increase risk individuals being misinformed. Conclusions The results highlight modest capabilities are unclear inaccurate. Any advice provided LLMs should cautiously approached due significant misinformation. evidence suggests steadily potentially play systems future. To address issue misinformation, there pressing need development dataset. dataset enhance reliability applications featuring more realistic prompt styles minimal information across broader range fields.

Язык: Английский

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Awareness of herbal medicine within the Bulgarian population—a pilot study

Pharmacia, Год журнала: 2025, Номер 72, С. 1 - 8

Опубликована: Фев. 17, 2025

Background : Herbal medicine has been an integral part of Bulgarian life and culture for centuries. However, incorrect use, resulting from a lack adequate or false understanding medicinal plants’ therapeutic properties, poses serious health risks patients. Objectives The main goal is to determine the sources information about herbal medicine. Secondary objectives include identifying most commonly used plants, ways obtain them, assessing whether herbs are correctly by respondents. Methods A pilot, observational, prospective, cross-sectional online survey was conducted 19 June 2023 31 July 2023. specific questionnaire with four sections developed. Statistical analysis performed using MedCalc software, including descriptive statistics, frequency graphical analysis, Fisher’s exact test, chi-squared tests. Results number respondents 59, predominance individuals aged 18–30 (57.6%), women (72.8%), university graduates (64.4%). About 31% suffer chronic diseases, common being related digestive system (n = 3), nervous allergies 3). considered effective therapy 81.4% (p < 0.0001). For acute preferred 22 patients, while 6. widely Mentha sp. 18) Thymus 14) Lamiaceae family Matricaria chamomilla 15) Asteraceae family. There reported be 123 vs. n 22, p Most (70.4%) buy pharmacy drugstore 0.0004). medical professionals 51) Internet 31) 0.1080). Conclusion significant Internet, which can lead misinformation. Efforts needed develop reliable source information.

Язык: Английский

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Maternal knowledge of pediatric first aid in Riyadh: Addressing gaps for improved child safety and women’s health outcomes

Medicine, Год журнала: 2025, Номер 104(7), С. e41611 - e41611

Опубликована: Фев. 14, 2025

Childhood injuries are a major cause of morbidity and mortality worldwide, with mothers often being the first responders in such emergencies. In Saudi Arabia, despite high educational attainment, maternal preparedness for pediatric aid remains underexplored. This study aims to evaluate knowledge, attitudes, practices (KAP) concerning Riyadh, focus on identifying key gaps informing interventions line Arabia’s Vision 2030. descriptive cross-sectional surveyed 385 residing Riyadh between May September 2023. Data were collected through structured validated questionnaire available Arabic English, distributed via social media platforms. The assessed socio-demographic characteristics, regarding aid. Statistical analysis was conducted using SPSS version 23, statistics non-parametric tests (Mann–Whitney U Kruskal–Wallis) employed analyze group differences. reliability instruments measured Cronbach’s alpha (α = 0.867). majority (69.2%) aged 20 40 years, 66.1% held university degree. While 97.4% respondents reported aware aid, significant knowledge observed. Although 76.8% participants knew how apply pressure bleeding wound, only 42.3% correctly identified preserve lost tooth, just 12.3% appropriate response seizures. Mothers formal training had significantly higher scores ( P < .01), education level predictor better .05). Social most frequently cited source information (37.6%), followed by courses (27.4%). Despite awareness, this identifies substantial emergencies, particularly managing specific situations as seizures dental injuries. These findings highlight urgent need programs tailored Riyadh. Incorporating into public health initiatives, 2030, could improve enhance child safety.

Язык: Английский

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From Disease Detection to Health Campaigns: The Role of Social Media Analytics in Public Health

Studies in computational intelligence, Год журнала: 2025, Номер unknown, С. 105 - 120

Опубликована: Янв. 1, 2025

Язык: Английский

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Developing predictive models for HIV Drug resistance: A genomic and AI approach

International Medical Science Research Journal, Год журнала: 2024, Номер 4(5), С. 521 - 543

Опубликована: Май 5, 2024

This paper proposes a novel approach to combating HIV drug resistance through the development of predictive models leveraging genomic data and artificial intelligence (AI). With increasing prevalence drug-resistant strains HIV, there is critical need for innovative strategies predict manage mutations, thereby optimizing treatment outcomes prolonging efficacy antiretroviral therapy (ART). Drawing on advances in genomics AI, this study outlines conceptual framework that can identify potential drug-resistance mutations genomes inform clinical decision-making. The proposed integrates from HIV-infected individuals with AI algorithms capable learning complex patterns within data. By analyzing sequences obtained HIV-positive patients, aim genetic variations associated resistance, likelihood development, guide selection appropriate regimens. holds promise personalized medicine care, enabling clinicians tailor based an individual's profile risk resistance. Key components include preprocessing extract relevant features, model training using machine techniques such as deep ensemble methods, validation performance cross-validation independent testing. Furthermore, integration data, history viral load measurements, enhances accuracy provides valuable insights into response dynamics.The represents paradigm shift offering proactive management surveillance. technologies, healthcare providers anticipate address emerging before they compromise efficacy. Ultimately, implementation improve patient outcomes, reduce transmission strains, advance global fight against HIV/AIDS. Keywords: Developing, Predictive Models, Drug Resistance, Genomic, Approach.

Язык: Английский

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Machine learning insights into HIV outbreak predictions in Sub-Saharan Africa

International Medical Science Research Journal, Год журнала: 2024, Номер 4(5), С. 558 - 578

Опубликована: Май 5, 2024

Predicting and preventing HIV outbreaks in Sub-Saharan Africa, a region disproportionately affected by the epidemic remains significant challenge. This review explores effectiveness challenges of using machine learning (ML) for forecasting spread high-risk areas. ML models have shown promise identifying patterns trends data, enabling more accurate predictions targeted interventions. insights into outbreak leverage various data sources, including demographic, epidemiological, behavioural data. By analysing these algorithms can identify populations geographical areas susceptible to transmission. information is crucial public health authorities allocate resources efficiently implement preventive measures effectively. Despite potential benefits, several exist predictions. These include quality issues, such as incomplete or inaccurate which affect reliability Additionally, complexity transmission dynamics need real-time pose models. To address challenges, researchers practitioners are exploring innovative approaches, integrating multiple sources advanced techniques. Collaborations between researchers, officials, technology experts also developing robust In conclusion, while offers valuable addressing model essential its effective use. overcoming has significantly improve prevention efforts ultimately reduce burden region. Keywords: Machine Learning, AI, Outbreaks: Predictions, Insights.

Язык: Английский

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