Revolutionizing Sleep Health: The Emergence and Impact of Personalized Sleep Medicine

Journal of Personalized Medicine, Journal Year: 2024, Volume and Issue: 14(6), P. 598 - 598

Published: June 4, 2024

Personalized sleep medicine represents a transformative shift in healthcare, emphasizing individualized approaches to optimizing health, considering the bidirectional relationship between and health. This field moves beyond conventional methods, tailoring care unique physiological psychological needs of individuals improve quality manage disorders. Key this approach is consideration diverse factors like genetic predispositions, lifestyle habits, environmental factors, underlying health conditions. enables more accurate diagnoses, targeted treatments, proactive management. Technological advancements play pivotal role field: wearable devices, mobile applications, advanced diagnostic tools collect detailed data for continuous monitoring analysis. The integration machine learning artificial intelligence enhances interpretation, offering personalized treatment plans based on individual profiles. Moreover, research circadian rhythms physiology advancing our understanding sleep’s impact overall next generation technology will integrate seamlessly with IoT smart home systems, facilitating holistic environment Telemedicine virtual healthcare platforms increase accessibility specialized care, especially remote areas. Advancements also focus integrating various sources comprehensive assessments treatments. Genomic molecular could lead breakthroughs disorders, informing highly plans. Sophisticated methods stage estimation, including techniques, are improving precision. Computational models, particularly conditions obstructive apnea, enabling patient-specific strategies. future likely involve cross-disciplinary collaborations, cognitive behavioral therapy mental interventions. Public awareness education about approaches, alongside updated regulatory frameworks security privacy, essential. Longitudinal studies provide insights into evolving patterns, further refining approaches. In conclusion, revolutionizing disorder treatment, leveraging characteristics technologies improved diagnosis, towards marks significant advancement enhancing life those

Language: Английский

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Anxiety among Medical Students Regarding Generative Artificial Intelligence Models: A Pilot Descriptive Study

Published: Aug. 16, 2024

Despite the potential benefits of generative Artificial Intelligence (genAI), concerns about its psy-chological impact on medical students, especially with regard to job displacement, are apparent. This pilot study, conducted in Jordan during July–August 2024, aimed examine specific fears, anxieties, mistrust, and ethical students could harbor towards genAI. Using a cross-sectional survey design, data were collected from 164 studying across various academic years, employing structured self-administered questionnaire an internally consistent FAME scale—representing Fear, Anxiety, Mistrust, Ethics comprising 12 items, three items for each construct. The results indicated variable levels anxiety genAI among participating students: 34.1% reported no role their future careers (n = 56), while 41.5% slightly anxious 61), 22.0% somewhat 36), 2.4% extremely 4). Among constructs, Mistrust was most agreed upon (mean: 12.35±2.78), followed by construct 10.86±2.90), Fear 9.49±3.53), Anxiety 8.91±3.68). Sex, level, Grade Point Average (GPA) did not significantly affect students’ perceptions However, there notable direct association between general elevated scores constructs scale. Prior exposure previous use modify These findings highlighted critical need refined educational strategies address integration training. demonstrated pervasive anxiety, fear, regarding deployment healthcare, indicating necessity curriculum modifi-cations that focus specifically these areas. Interventions should be tailored increase familiarity competency, which would alleviate apprehension equip physicians engage this inevitable technology effectively. study also importance incorporating discussions into courses mistrust human-centered aspects Conclusively, calls proactive evolution education prepare AI-driven healthcare practices shortly ensure well-prepared, confident, ethically informed professional interactions technologies.

Language: Английский

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Monitoring oral health remotely: ethical considerations when using AI among vulnerable populations

Frontiers in Oral Health, Journal Year: 2025, Volume and Issue: 6

Published: April 14, 2025

Technological innovations in dentistry are revolutionizing the monitoring and management of oral health. This perspective article critically examines rapid expansion remote technologies—including artificial intelligence (AI)-driven diagnostics, electronic health records (EHR), wearable devices, mobile applications, chatbots—and discusses their ethical, legal, social implications. The accelerated adoption these digital tools, particularly wake COVID-19 pandemic, has enhanced accessibility to care while simultaneously raising significant concerns regarding patient consent, data privacy, algorithmic biases. We review current applications ranging from AI-assisted detection dental pathologies blockchain-enabled transfer within EHR systems, highlighting potential for improved diagnostic accuracy risks associated with over-reliance on assessments. Furthermore, we underscore challenges posed by divide, where disparities literacy access may inadvertently exacerbate existing socio-economic inequalities. calls development rigorous implementation ethical frameworks regulatory guidelines that ensure reliability, transparency, accountability innovations. By integrating multidisciplinary insights, our discussion aims foster a balanced approach maximizes clinical benefits emerging technologies safeguarding autonomy promoting equitable healthcare delivery.

Language: Английский

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Development of a Core Outcome Set for Neurological Disorders (COS-Neuro): an AI-Assisted Thematic Framework Analysis. (Preprint)

Published: April 23, 2025

Neurological disorders affect approximately 3 billion people globally, yet clinical trial success is often hindered by poorly chosen outcome measures, impacting design, compliance, and interpretation. Core Outcome Sets (COS) have emerged over the past 25 years as standardized tools to enhance selection, ensuring comparability across studies reflecting priorities of both researchers patients. Despite COS initiatives in other fields, their development neurology remains limited, leaving many trialists without disease-specific guidance. Given common themes neurological COS, a unified framework—a ‘COS COS’—could support selection where no exists. This study (COS-Neuro) uses Artificial Intelligence (AI) analyse existing identifying shared domains develop thematic framework, streamlining creation improving design. COS-Neuro was developed using AI-assisted framework analysis, followed expert review. A modified 6-step analysis used pre-determined codes: 1. Dataset Gathering – Data from COMET database collected for diseases were coded. 2. Prompt Design & Testing LLMs (ChatGPT 3.5, Google Gemini 1.5 Flash, Meta Llama-2-70b) trialled, prompts refined based on responses. 3. Thematic Analysis categorised into core areas. 4. Human Refinement Experts reviewed LLM-generated areas selected most appropriate 5. Clinical Validation validated domains, areas, concepts. streamlined approach integrated AI with oversight standardised disorders. With assistance LLMs, particularly ChatGPT, robust conceptual 112 COS. Adapting OMERACT model, 4 concepts, 13 75 finalised following consensus clinicians. establishes recommendations project provides foundation future research reference trials lacking established It also sets precedent qualitative medicine, successful adaptation highlighting its scalability COS’ specialties.

Language: Английский

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Prompts, privacy, and personalized learning: integrating AI into nursing education—a qualitative study

BMC Nursing, Journal Year: 2025, Volume and Issue: 24(1)

Published: April 29, 2025

Generative artificial intelligence (GenAI) has emerged as a powerful tool in nursing education, offering novel ways to enhance clinical reasoning, critical thinking, and personalized learning. However, questions remain regarding the ethical use of AI-generated outputs, data privacy concerns, limitations recognizing emotional nuances. This study aims explore how students utilize GenAI tools develop care plans, with particular focus on innovative role prompt engineering. By identifying both challenges opportunities, it seeks provide actionable insights into seamlessly integrating education while safeguarding humanistic skills. A qualitative design was adopted, involving semi-structured interviews third-year undergraduate at single institution. Participants worked anonymized cases multiple tools, emphasizing iterative prompts optimize care-plan outputs. Data were analyzed thematically capture detailed perspectives AI-facilitated learning considerations. Findings indicate that enhanced efficiency conceptual clarity, allowing more higher-order thinking. Prompt engineering significantly improved accuracy contextual relevance plans. expressed concerns about incomplete or imprecise responses, GenAI's limited understanding, risks associated sensitive healthcare data. When used careful refinement evaluation, viewed valuable supplement rather than replacement for competencies. highlights transformative potential underscoring importance structured safeguards. balancing technological innovation empathy, communication, cultural sensitivity, educators can harness AI deepen reasoning prepare future AI-enhanced practice. Further research across diverse settings is needed validate these findings refine best practices curricula. Not applicable. did not involve trial.

Language: Английский

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Apprehension toward generative artificial intelligence in healthcare: a multinational study among health sciences students

Frontiers in Education, Journal Year: 2025, Volume and Issue: 10

Published: May 1, 2025

Background In the recent generative artificial intelligence (genAI) era, health sciences students (HSSs) are expected to face challenges regarding their future roles in healthcare. This multinational cross-sectional study aimed confirm validity of novel FAME scale examining themes Fear, Anxiety, Mistrust, and Ethical issues about genAI. The also explored extent apprehension among HSSs genAI integration into careers. Methods was based on a self-administered online questionnaire distributed using convenience sampling. survey instrument scale, while toward assessed through modified State-Trait Anxiety Inventory (STAI). Exploratory confirmatory factor analyses were used construct scale. Results final sample comprised 587 mostly from Jordan (31.3%), Egypt (17.9%), Iraq (17.2%), Kuwait (14.7%), Saudi Arabia (13.5%). Participants included studying medicine (35.8%), pharmacy (34.2%), nursing (10.7%), dentistry (9.5%), medical laboratory (6.3%), rehabilitation (3.4%). Factor analysis confirmed reliability Of constructs, Mistrust scored highest, followed by Ethics. participants showed generally neutral genAI, with mean score 9.23 ± 3.60. multivariate analysis, significant variations observed previous ChatGPT use, faculty, nationality, expressing highest level apprehension, Kuwaiti lowest. Previous use correlated lower levels. higher agreement Ethics constructs statistically associations apprehension. Conclusion revealed notable Arab HSSs, which highlights need for educational curricula that blend technological proficiency ethical awareness. Educational strategies tailored discipline culture needed ensure job security competitiveness an AI-driven future.

Language: Английский

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Pathology in the artificial intelligence era: Guiding innovation and implementation to preserve human insight

Academic Pathology, Journal Year: 2025, Volume and Issue: 12(1), P. 100166 - 100166

Published: Jan. 1, 2025

The integration of artificial intelligence in pathology has ignited discussions about the role technology diagnostics-whether serves as a tool for augmentation or risks replacing human expertise. This manuscript explores intelligence's evolving contributions to pathology, emphasizing its potential capacity enhance, rather than eclipse, pathologist's role. Through historical comparisons, such transition from analog digital radiology, this paper highlights how technological advancements have historically expanded professional capabilities without diminishing essential element. Current applications pathology-from diagnostic standardization workflow efficiency-demonstrate augment accuracy, expedite processes, and improve consistency across institutions. However, challenges remain algorithmic bias, regulatory oversight, maintaining interpretive skills among pathologists. discussion underscores importance comprehensive governance frameworks, educational curricula, public engagement initiatives ensure remains collaborative endeavor that empowers professionals, upholds ethical standards, enhances patient outcomes. ultimately advocates balanced approach where expertise work concert advance future medicine.

Language: Английский

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Generative AI: Current Status and Future Directions

Journal of Computer Information Systems, Journal Year: 2025, Volume and Issue: unknown, P. 1 - 34

Published: April 1, 2025

Language: Английский

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Generative Neural Networks for Addressing the Bioequivalence of Highly Variable Drugs

Algorithms, Journal Year: 2025, Volume and Issue: 18(5), P. 266 - 266

Published: May 4, 2025

Bioequivalence assessment of highly variable drugs (HVDs) remains a significant challenge, as the application scaled approaches requires replicate designs, complex statistical analyses, and varies between regulatory authorities (e.g., FDA EMA). This study introduces use artificial intelligence, specifically Wasserstein Generative Adversarial Networks (WGANs), novel approach for bioequivalence studies HVDs. Monte Carlo simulations were conducted to evaluate performance WGANs across various variability levels, population sizes, data augmentation scales (2× 3×). The generated tested acceptance using both EMA approaches. WGAN approach, even applied without scaling, consistently outperformed EMA/FDA methods by effectively reducing required sample size. Furthermore, not only minimizes size needed HVDs, but also eliminates need complex, costly, time-consuming designs that are prone high dropout rates. demonstrates with 3× can achieve rates exceeding 89% all criteria, 10 out 18 scenarios reaching 100%, highlighting method potential transform design efficiency studies. is foundational step in utilizing clear new era evaluation begin.

Language: Английский

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The Overlooked Dark Side of Generative AI in Nursing: An International Think Tank's Perspective

Journal of Nursing Scholarship, Journal Year: 2025, Volume and Issue: unknown

Published: May 5, 2025

Language: Английский

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