Background: As artificial intelligence (AI) continues to advance in healthcare, limited research has explored how nursing professionals perceive its integration into clinical practice and education—particularly among those directly involved in AI-driven initiatives. This qualitative study aimed to investigate the perceptions, experiences, and expectations of nursing educators and clinical practitioners regarding the application of AI in nursing and to provide insights for the advancement of AI-driven nursing science. Methods: A descriptive qualitative design was employed. Between September and December 2024, semi-structured interviews were conducted with 12 nursing professionals from universities and hospitals in Shanghai, Suzhou, and Chengdu, China. Participants were selected using maximum variation sampling, and data were analyzed using content analysis. Results: Three major themes and eleven sub-themes were identified: (1) The potential of multi-perspective development of AI-driven nursing science and practice, including aiding in decision-making, assisting with writing nursing documents, helping in care practices with high exposure risks and heavy physical exertion, and supporting the development of nursing activities. (2) A multi-dimensional response to the wave of intelligent nursing research and practice: education and scientific research come first, then we fully explore the application scenarios, and then conduct deep interdisciplinary integration. (3) Obstacles for intelligent nursing research and practice: interaction factors of “human–technology–machine” for application, transformation, and promotion; financial support and continuous investment; the controversy behind the intelligent maturity level; and application risk and fault tolerance. Conclusions: Participants emphasized the importance of evidence-based, cautious, and context-sensitive application of AI technologies to ensure that intelligent nursing evolves in alignment with clinical realities. The findings suggest a need for strengthened policy, education, and resource allocation to support the sustainable integration of AI in nursing.

Introduction: Early detection of lung cancer is one way to improve outcomes. Improving the detection of nodules on chest CT scans is important. Previous artificial intelligence (AI) modules show rapid advantages, which improves the performance of detecting lung nodules in some datasets. However, they have a high false-positive (FP) rate. Its effectiveness in clinical practice has not yet been fully proven. We aimed to use AI assistance in CT scans to decrease FP.

Objective: This study explored the perceptions, psychological changes and needs for home self-management in people with hard-to-heal wounds. Method: Participants were recruited from a wound care outpatient clinic of The First Affiliated Hospital of Soochow University, China, and qualitative, semi-structured interviews were conducted. A phenomenological approach of qualitative research was used, and the data were collected and analysed using Colaizzi's method. Results: The research cohort comprised 15 patients and three themes were extracted: (1) symptom management burden (symptom distress and lack of knowledge about wound care); (2) inadequate resources related to self-management (insufficient medical resources in primary hospitals, tortuosity of the treatment-seeking process, expectation of access to wound expertise); and (3) self-managing pressures coexisting with motivation (high expectations for and low level of attention to wound healing, varied treatment adherence, financial burden, negative emotions, the contradiction between work and rest, and social communication impact). Conclusion: There was variation in the perception of self-management among patients with hard-to-heal wounds, with only some patients knowing how to perform wound self-management at home. Overall, patients with hard-to-heal wounds had poor perceptions of self-management. Healthcare workers should encourage and guide patients with hard-to-heal wounds to self-manage, which will help to increase the patient's motivation for self-management and reduce the burden of symptom management. Professional staff in community and secondary hospitals should be trained to improve their guidance skills for self-management for patients with hard-to-heal wounds.