Generative Artificial Intelligence Use in Healthcare: Opportunities for Clinical Excellence and Administrative Efficiency

ChatGPT Applications in Nursing: Current Status and Future Perspectives

AIM

With the rapid advancement of generative artificial intelligence technologies, natural language processing tools represented by ChatGPT have been increasingly used. This narrative review explored the application, challenges, and future directions of ChatGPT in nursing.

DESIGN

Narrative review.

METHODS

The searches were conducted in PubMed, Web of Science and Google Scholar. The empirical studies of ChatGPT in nursing were selected and explored.

RESULTS

ChatGPT has been integrated into clinical nursing support, nurse education and patient service optimisation, demonstrating potential in improving efficiency and patient outcomes. However, technical limitations, ethical-legal issues and implementation barriers pose challenges to its widespread adoption. In future, technology iteration, standard development and multimodal convergence are needed to promote the construction of trusted artificial intelligence nursing systems.

PATIENT OR PUBLIC CONTRIBUTION

This narrative review is based on a secondary analysis of existing literature and does not directly involve patient or public contributions.

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

BACKGROUND AND PURPOSE

Arrhythmia, which presents with irregular and/or fast/slow heartbeats, is associated with morbidity and mortality risks. Photoplethysmography (PPG) provides information on volume changes of blood flow and 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 have drawn inspiration from quantum circuits and employed a quantum-inspired feature extraction function /named the Tree Quantum Circuit Pattern (TQCPat). The proposed system consists of four main stages: (i) multilevel feature extraction using discrete wavelet transform (MDWT) and TQCPat, (ii) feature selection using Chi-squared (Chi2) and neighborhood component analysis (NCA), (iii) classification using k-nearest neighbors (kNN) and support vector machine (SVM) and (iv) information fusion.

RESULTS

Our proposed TQCPat-based feature engineering model has yielded a classification accuracy of 91.30% using 46,827 PPG signals in classifying six classes with ten-fold cross-validation.

CONCLUSION

Our results show that the proposed TQCPat-based model is accurate for arrhythmia classification using PPG signals and can be tested with a large database and more arrhythmia classes.

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

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