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

Journal of Medical Systems, Год журнала: 2025, Номер 49(1)

Опубликована: Фев. 21, 2025

Язык: Английский

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

Biomedicines, Год журнала: 2025, Номер 13(3), С. 636 - 636

Опубликована: Март 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

Язык: 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, Год журнала: 2025, Номер unknown

Опубликована: Март 1, 2025

Язык: 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, Год журнала: 2025, Номер 49(1)

Опубликована: Март 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.

Язык: Английский

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Generative AI in Medicine — Evaluating Progress and Challenges

New England Journal of Medicine, Год журнала: 2025, Номер unknown

Опубликована: Апрель 10, 2025

Язык: Английский

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Generative Artificial Intelligence and Risk Appetite in Medical Decisions in Rheumatoid Arthritis

Applied Sciences, Год журнала: 2025, Номер 15(10), С. 5700 - 5700

Опубликована: Май 20, 2025

With Generative AI (GenAI) entering medicine, understanding its decision-making under uncertainty is important. It well known that human subjective risk appetite influences medical decisions. This study investigated whether the of GenAI can be evaluated and if established assessment tools are applicable for this purpose in a context. Five systems (ChatGPT 4.5, Gemini 2.0, Qwen 2.5 MAX, DeepSeek-V3, Perplexity) were using Rheumatoid Arthritis (RA) clinical scenarios. We employed two methods adapted from assessment: General Risk Propensity Scale (GRiPS) Time Trade-Off (TTO) technique. Queries involving RA cases with varying prognoses hypothetical treatment choices posed repeatedly to assess profiles response consistency. All GenAIs consistently identified same best worst prognoses. However, methodologies yielded varied results. The GRiPS showed significant differences general propensity among being least risk-averse Qwen/DeepSeek most), though these diminished specific prognostic contexts. Conversely, TTO method indicated strong aversion (unwillingness trade lifespan pain relief) across yet revealed Perplexity as significantly more risk-tolerant than Gemini. variability obtained versus raises questions about tool applicability. discrepancy suggests human-centric instruments may not adequately or capture nuances processing Artificial Intelligence. findings imply current might insufficient, highlighting need specifically tailored evaluating uncertainty.

Язык: Английский

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Critical thinking in the age of generative AI: implications for health sciences education

Frontiers in Artificial Intelligence, Год журнала: 2025, Номер 8

Опубликована: Май 21, 2025

Язык: Английский

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