Reclaiming Patient-Centered Care: How Intelligent Time is Redefining Healthcare Priorities

Journal of Medical Systems, Journal Year: 2025, Volume and Issue: 49(1)

Published: Feb. 21, 2025

Language: Английский

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Effectiveness of Generative Artificial Intelligence-Driven Responses to Patient Concerns in Long-Term Opioid Therapy: Cross-Model Assessment

Biomedicines, Journal Year: 2025, Volume and Issue: 13(3), P. 636 - 636

Published: March 5, 2025

Background: While long-term opioid therapy is a widely utilized strategy for managing chronic pain, many patients have understandable questions and concerns regarding its safety, efficacy, potential dependency addiction. Providing clear, accurate, reliable information essential fostering patient understanding acceptance. Generative artificial intelligence (AI) applications offer interesting avenues delivering education in healthcare. This study evaluates the reliability, accuracy, comprehensibility of ChatGPT’s responses to common inquiries about therapy. Methods: An expert panel selected thirteen frequently asked based on authors’ clinical experience pain targeted review materials. Questions were prioritized prevalence consultations, relevance treatment decision-making, complexity typically required address them comprehensively. We assessed by implementing multimodal generative AI Copilot (Microsoft 365 Chat). Spanning three domains—pre-therapy, during therapy, post-therapy—each question was submitted GPT-4.0 with prompt “If you physician, how would answer asking…”. Ten physicians two non-healthcare professionals independently using Likert scale rate reliability (1–6 points), accuracy (1–3 points). Results: Overall, demonstrated high (5.2 ± 0.6) good (2.8 0.2), most answers meeting or exceeding predefined thresholds. Accuracy moderate (2.7 0.3), lower performance more technical topics like tolerance management. Conclusions: exhibit significant as supplementary tool limitations addressing highly context-specific queries underscore need ongoing refinement domain-specific training. Integrating systems into practice should involve collaboration between healthcare developers ensure safe, personalized, up-to-date

Language: Английский

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Quantifying the Progressing Landscape of Infection Preventionists: A Survey-Based Analysis of Workload and Resource Needs

American Journal of Infection Control, Journal Year: 2025, Volume and Issue: unknown

Published: March 1, 2025

Language: Английский

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TQCPat: Tree Quantum Circuit Pattern-based Feature Engineering Model for Automated Arrhythmia Detection using PPG Signals

Journal of Medical Systems, Journal Year: 2025, Volume and Issue: 49(1)

Published: March 24, 2025

Abstract Background and Purpose Arrhythmia, which presents with irregular and/or fast/slow heartbeats, is associated morbidity mortality risks. Photoplethysmography (PPG) provides information on volume changes of blood flow can be used to diagnose arrhythmia. In this work, we have proposed a novel, accurate, self-organized feature engineering model for arrhythmia detection using simple, cost-effective PPG signals. Method We drawn inspiration from quantum circuits employed quantum-inspired extraction function /named the Tree Quantum Circuit Pattern (TQCPat). The system consists four main stages: (i) multilevel discrete wavelet transform (MDWT) TQCPat, (ii) selection Chi-squared (Chi2) neighborhood component analysis (NCA), (iii) classification k-nearest neighbors (kNN) support vector machine (SVM) (iv) fusion. Results Our TQCPat-based has yielded accuracy 91.30% 46,827 signals in classifying six classes ten-fold cross-validation. Conclusion results show that accurate tested large database more classes.

Language: Английский

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Generative AI in Medicine — Evaluating Progress and Challenges

New England Journal of Medicine, Journal Year: 2025, Volume and Issue: unknown

Published: April 10, 2025

Language: Английский

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