Aim. To synthesise the available evidence on the applicability of artificial intelligence in nursing care. Background. Artificial intelligence involves the replication of human cognitive abilities in machines, allowing to perform tasks that conventionally necessitate human cognition. However, its application in health sciences is a recent one, and its use is currently limited to supporting the diagnosis and prognosis of hospitalised patients, among others. Evaluation. A systematic review was conducted in the PubMed-Medline, Scopus, CINAHL, Web of Science, and Nursing & Allied Health databases until September 2022, following the PRISMA guidelines. Key Issues. A total of 21 articles were selected for the review. The different applications of artificial intelligence in nursing identified comprised (i) advances in early disease detection and clinical decision making; (ii) artificial intelligence-based support systems in nursing for patient monitoring and workflow optimisation; and (iii) artificial intelligence insights for nursing training and education. Conclusion. Artificial intelligence-based systems demonstrated increased autonomy of patients and professionals in care processes such as wound management through guided instructions, improved workflows, and efficiency in terms of time, materials, and human resources. Implications for Nursing Management. Artificial intelligence applied to nursing practice can be a very useful resource for professionals, managers, and supervisors. It has the potential to change current working flow systems and may serve as a down-to-earth resource to support nursing professionals in their decision-making process that ensures high quality and patient safety care.

Cancer affects more than 19.3 million people and has become the second leading cause of death worldwide. Chemo- and radiotherapy, the most common procedures in these patients, often produce unpleasant treatment-related side effects that have a direct impact on the quality of life of these patients. However, innovative therapeutic strategies such as probiotics are being implemented to manage these complications. Thus, this study aimed to evaluate the efficacy of probiotics supplements as a therapeutic strategy in adult oncology treatment-related side effects. A systematic review of randomized controlled trials was conducted in PubMed, Scielo, ProQuest and OVID databases up to and including January 2021, following the PRISMA guidelines. The quality of the included studies was assessed by the Jadad Scale. Twenty clinical trials published between 1988 and 2020 were included in this review. Seventeen studies (85%) revealed predominantly positive results when using probiotics to reduce the incidence of treatment-related side effects in oncology patients, while three studies (15%) reported no impact in their findings. This study sheds some light on the significance of chemotherapy and radiotherapy in altering the composition of gut microbiota, where probiotic strains may play an important role in preventing or mitigating treatment-related side effects.

The aim of this study is to explore the experiences and perceptions of final‐year nursing students on the acceptability and feasibility of using a chatbot for clinical decision‐making and patient safety. The effective and inclusive use of new technologies such as conversational agents or chatbots could support nurses in increasing evidence‐based care and decreasing low‐quality services. A descriptive qualitative study was used through focus group interviews. The data analysis was conducted using a thematic analysis. This study included 114 participants. After our data analysis, two main themes emerged: (i) experiences in the use of a chatbot service for clinical decision‐making and and (ii) integrating conversational agents into the organizational safety culture. The findings of our study provide preliminary support for the acceptability and feasibility of adopting SafeBot, a chatbot for clinical decision‐making and patient safety. Our results revealed substantial recommendations for refining navigation, layout and content, as well as useful insights to support its acceptance in real nursing practice. Leaders and managers may well see artificial intelligence‐based conversational agents like SafeBot as a potential solution in modern nursing practice for effective problem‐solving resolution, innovative staffing and nursing care delivery models at the bedside and criteria for measuring and ensure quality and patient safety.

Background: Females with autism often receive late diagnoses—especially those with average or above-average intellectual abilities—highlighting the need to explore the unique experiences of this population for better health care support. Objective: To explore the experiences and perceptions of females who received a late diagnosis on the autism spectrum in terms of coping and managing their diagnosis. Methods: A descriptive qualitative study was conducted from December 2022 to March 2023 using semistructured interviews with 14 late-diagnosed autistic female subjects. Purposive and snowball sampling were employed, and thematic analysis of the interview data was performed using ATLAS.ti v.9 software. Findings were reported following standards for reporting qualitative research guidelines. Results: Two main themes emerged: (a) getting a diagnosis: fitting into the norm, and (b) navigating the distinct significance for autistic females. The findings indicated that masking strategies contribute to delayed diagnoses, and social and health care stigma surrounding autism in females was evident. Areas for improving access to resources and support programs were identified. Discussion: The results show that masking and camouflaging are predominant strategies among autistic females, contributing to delayed diagnoses and exposing them to additional risks. Enhancing resources and training for health care professionals is necessary to address the specific needs of this population.

Aim: To explore the perceptions of nursing professionals in high‐demand healthcare services regarding the adoption of AI‐based support systems for the prevention of medication errors. Design: A qualitative descriptive study was conducted between November 2024 and March 2025. Methods: Sixteen semi‐structured interviews were held with nurses from emergency and intensive care units, guided by conceptual dimensions of the Technology Acceptance Model framework. Participants were recruited using purposive and snowball sampling. ATLAS.ti v.9 software was used for an inductive thematic analysis. Results: Two major themes emerged: (i) professional reflections on medication safety and related risks; and (ii) integrating artificial intelligence into nursing practice to reduce such risks and prevent medication errors. While artificial intelligence was recognised as a promising resource to support clinical decision‐making and reduce cognitive load, nurses identified barriers, including limited training, inadequate technological infrastructure, unreliable data sources, and ethical concerns that could compromise its safe implementation and thereby hinder its potential to prevent medication errors. Conclusion: AI‐based support systems are perceived as useful, but complex resources for addressing medication errors, which remain a critical challenge in healthcare. Its successful implementation depends not only on the availability of resources, but also on the organisational context and the ability to respond to the needs and concerns of healthcare professionals. Implications for Clinical Practice: Integrating artificial intelligence into routine workflows to support clinical decision‐making and reduce medication errors in high‐demand settings requires more than infrastructure and technical training. Effective adoption demands participatory design, clear role delineation, and context‐sensitive training aligned with medication‐management processes. Lack of alignment may result in artificial intelligence increasing complexity instead of contributing to safer and more efficient medication administration. Reporting Method: Methods and findings are reported following SRQR recommendations. Patient or Public Contribution: No patient or public contribution.