Exponential Technology.

The lack of artificial intelligence applications in nursing education and the nursing profession in Turkey and the need for strategies for integrating artificial intelligence into the nursing profession continues. At this point, there is a need to transform the negative attitudes and anxiety that may occur in nurses. It was aimed to reorganize the professional transformation in this parallel by analyzing the effect of digital leadership perception, which is explained as how nurses approach digital technologies and innovations and their awareness of how and with which methods they can use these technologies on artificial intelligence anxiety and attitude in the nursing profession. The study was designed as descriptive, correlational, and cross-sectional. The research was conducted by reaching 439 nurses working in hospitals operating in three different regions of Turkey by simple random sampling method. In the first part of the data collection tool used in this study, digital leadership scale, artificial intelligence use anxiety, and artificial intelligence attitude scales were used, including questions determining the demographic information of nurses, their relationship with technology, artificial intelligence usage status and its importance in the profession. It was determined that 29.8% of the nurses had a good relationship with technology, 66.3% knew about using artificial intelligence in health, and 27.3% wanted it to be more involved in their lives. It was determined that nurses' perceptions of digital leadership were at a medium level of 46.9% and a high level of 41.7%, 82.7% had a positive attitude towards artificial intelligence, and 82.7% had low or medium level anxiety when their artificial intelligence anxiety status was examined. There was a significant and negative relationship between digital leadership and AI anxiety (r = -0.434; p < 0.01), a significant and positive relationship between digital leadership and AI attitude (r = 0.468; p < 0.01), and a significant and negative relationship between AI attitude and AI anxiety (r = -0.629; p < 0.01). Finally, it was determined that nurses' perception of digital leadership indirectly affected AI anxiety through AI attitude (β = -0.230, 95% CI [-0.298, -0.165]). It is suggested that the anxiety and attitude towards artificial intelligence can be transformed positively with the effect of digital leadership, and in this parallel, the digital leadership phenomenon should be evaluated as a practical implementation strategy in integrating artificial intelligence into the nursing profession. Our study showed that artificial intelligence attitude has a mediating role in the indirect effect of the perception of digital leadership in nursing on AI anxiety. It was determined that nurses' digital leadership perception, artificial intelligence anxiety, and artificial intelligence attitude differed significantly with demographic variables.

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PurposeDigital transformations are altering service models and care delivery methods in healthcare. Artificial Intelligence (AI) represents the next wave of transformation in healthcare. This study aims to understand patient perceptions of AI and its impact on value co-creation.Design/methodology/approachA conceptual model was developed to investigate how value co-creation operant resources (digital self-efficacy and relational service quality) impact value co-creation engagement (shared decision-making) and value co-creation outcomes (anticipatory AI value co-creation and intention to adopt AI). Data were collected from 332 respondents and analyzed using structural equation modeling.FindingsThe results indicate that the value co-creation process for AI technologies is a function of inputs, experiences and AI outputs. Operant resources were found to be positively associated with shared decision-making. However, not all operant resources directly and positively impacted AI outcomes. The indirect and positive mediated relationships through shared decision-making to AI outcomes suggest an interactive AI value co-creation process.Research limitations/implicationsAI technologies are still in early stages of consumer adoption in healthcare. Future research is warranted that investigates the validity of the model through maturing service life cycles.Practical implicationsCustomer perceptions of new digital innovations are formed in the context of previous digital experiences. Marketers need to understand how customers view their current non-AI technologies. Strong engagement and perceived value of current technologies will help ease customers into the usage of AI technologies.Originality/valueThis study investigates the unique stages of the value co-creation process for AI technologies in healthcare. The results demonstrate that the value co-creation process is a function of inputs, tech-enabled experiences and AI outputs.

Advancing AI in healthcare: A comprehensive review of best practices

Purpose This study aims to investigate how embedding Artificial Intelligence (AI) within Knowledge Management Systems (KMS) affects operational resilience in health care. It examines how AI-KMS integration influences disruption mitigation and dynamic resource reconfiguration, and how these effects are shaped by trust in AI and technical complexity. Design/methodology/approach A structured survey was conducted across 193 public and private hospitals in India. The study uses a three-equation structural modeling approach, estimated via 3SLS, to capture interdependent relationships among AI integration, operational disruption and resource flexibility, while accounting for moderating effects of system complexity and trust. Findings Results indicate that AI-KMS integration significantly reduces operational disruptions and enhances the organization’s ability to reconfigure resources dynamically. However, this disruption-mitigating effect is weakened when technical complexity is high. Conversely, trust in AI strengthens the positive effect of AI-KMS on resource adaptability. Resource reconfiguration, in turn, plays a mediating role in reducing disruptions, reinforcing its strategic value in digital health systems. Practical implications The study offers hospital managers the following actionable strategies: piloting low-complexity AI, embedding AI into protocols, enhancing explainability and monitoring usability. From a society perspective, instead, the findings support patient-centred innovation, more resilient health-care delivery and data-informed policy design in digital health ecosystems. Originality/value This paper bridges the literature on AI-enabled operations and knowledge management by modelling AI as a dynamic, trust-contingent capability. It also contributes a socio-technical contingency perspective to digital transformation in health care, extending existing dynamic capabilities theory to account for the role of trust and complexity in mediating technology outcomes.

The rapid integration of artificial intelligence (AI) into healthcare demands that nurses not only develop technical competencies but also uphold strong professional values to maintain effective and ethical practice. While AI readiness is gaining attention, its relationship with nursing professionalism and self-efficacy remains underexplored. To examine the relationship between nurses’ AI readiness and their self-efficacy, with a specific focus on the moderating role of professionalism within clinical practice settings. A descriptive cross-sectional design was employed involving 278 staff nurses from three tertiary healthcare hospitals: one hospital in Kafr Elsheikh Governorate and two hospitals in Alexandria Governorate, Egypt. Data were collected using three validated instruments: the Nursing Profession Self-Efficacy Scale, the Artificial Intelligence Readiness Scale, and the Nurse Professionalism Scale. Pearson correlation, multiple regression, and path analysis were performed using SPSS v25 to examine the relationships among the study variables and to test the proposed hypotheses. The findings revealed that both AI readiness (β = 0.143, p = 0.017) and professionalism (β = 0.147, p = 0.014) were significant predictors of nurses’ self-efficacy. Path analysis confirmed direct effects of both variables on self-efficacy and supported the moderating role of professionalism. AI readiness and professionalism collectively explained 4.7% of the variance in self-efficacy. Nurses reported high levels of self-efficacy and professionalism, but moderate AI readiness, especially in ethical and visionary subdomains. Nurses’ self-efficacy is shaped by both their preparedness to use AI technologies and their professional values. Professionalism enhances the psychological impact of AI readiness, enabling nurses to adapt confidently in evolving healthcare environments. Nursing leadership and education should integrate AI literacy with professional development to build a confident, ethically grounded workforce capable of leveraging AI for improved care outcomes. Not applicable.

Enhancing the quality of goods and services through innovation and the integration of electronic applications is a fundamental aspect of digital transformation. This topic has increasingly captivated the attention of management and organizational scholars. This focus is especially relevant in improving organizational performance by digitalizing core operations. The global and Sri Lankan economies have faced severe disruptions due to the COVID-19 pandemic, which has compelled commercial banks to adopt innovative digital strategies as a means of mitigating the resultant risks and maintaining stability. This study explores the impact of digital transformation on the financial performance of 10 Licensed Commercial Banks in Sri Lanka, comparing outcomes before and after the pandemic’s onset. By employing a robust methodological framework that includes descriptive statistics, correlation analysis, and T-test analysis, the research investigates key variables to discern trends and patterns across different timeframes. The assessment of digital transformation is conducted through a multi-dimensional approach, focusing on metrics such as the volume of digital transactions, income from fees and commissions, and the proliferation of Automated Teller Machines (ATMs) and Cash Deposit Machines (CDMs). Financial performance, on the other hand, is gauged using critical indicators like Return on Assets (ROA) and Return on Equity (ROE). The analysis reveals nuanced insights: while the increase in ATMs and CDMs correlates positively with financial performance, this relationship lacks statistical significance. In contrast, a substantial and positive effect on financial performance is observed from the volume of digital transactions and the income generated from fees and commissions. Significantly, the findings indicate that digital transformation initiatives have enabled commercial banks to not only weather the pandemic but also enhance their financial performance during this period of unprecedented challenge. These results emphasize the imperative for financial institutions to strategically integrate advanced technologies, such as artificial intelligence, into their digital transformation agendas. By doing so, they can secure and potentially elevate financial performance, even amidst unforeseen global crises like a pandemic.

Artificial intelligence (AI) is transforming health care and holds immense potential for various fields, including the medical field, but it also brings its share of concerns and issues. This article introduces AI and its core concepts, such as deep learning, machine learning, and large language models (LLMs). It also provides examples that were generated using ChatGPT to highlight the use of prompts and the limitations of LLMs in AI. AI is a powerful paradigm for scientific research, and it can and does reduce work effort and enhance results. However, is it the right tool for the selected task? In addition, no matter how powerful, AI is meant to complement humans and does not replace them.

Artificial intelligence (AI) is transforming health care across diagnostic imaging, clinical decision support, and personalized medicine. However, despite rapid technical advances, trust remains the principal bottleneck limiting clinical adoption. This report synthesizes insights from the second IEEE EMBS Workshop on AI and Healthcare, which centered on the multidimensional nature of trust in medical AI systems. Through keynote demonstrations of AI-enabled ultrasound and an interdisciplinary panel discussion spanning medicine, computational oncology, defense research, and scientific publishing, the workshop examined trust as a construct grounded not only in accuracy, but also in transparency, accountability, and interpretability. Audience polling and case-based discussions revealed that public and clinical confidence in AI is conditional and context-dependent, particularly in high-stakes decision environments. The workshop highlighted key tensions between performance and explainability, emphasized the need for explicit accountability frameworks, and underscored the importance of lifecycle oversight from data provenance to post-deployment validation. As AI becomes increasingly embedded in clinical workflows, its impact will depend less on algorithmic capability alone and more on its ability to earn and sustain trust. The discussions reinforce a critical conclusion: trustworthy healthcare AI requires validation, human-centered design, interdisciplinary collaboration, and sustained governance to evolve from promising technology to a reliable clinical partner.

The COVID-19 pandemic has had a massive impact on societies and their healthcare systems all over the world. Despite all the disruption the pandemic caused, it also created the opportunity and necessity to accelerate the digital transformation in healthcare. Digital technologies were employed in various ways. First, they were used for treating both COVID-19 and non-COVID-19 patients under social distancing measures via telemedicine. Second, digital technologies such as artificial intelligence or mobile applications were utilized as tools for improving contact tracing, diagnosis, and treatment of COVID-19. In addition, regulations were adapted or suspended in order to facilitate the use of these digital technologies. This chapter not only summarizes the impact of the COVID-19 pandemic on the digital transformation in healthcare, but also aims to provide fresh perspectives on how digitalization can outlast the pandemic and improve healthcare provision in the future.

The integration of artificial intelligence (AI) into medical imaging has guided in an era of transformation in healthcare. This literature review explores the latest innovations and applications of AI in the field, highlighting its profound impact on medical diagnosis and patient care. The innovation segment explores cutting-edge developments in AI, such as deep learning algorithms, convolutional neural networks, and generative adversarial networks, which have significantly improved the accuracy and efficiency of medical image analysis. These innovations have enabled rapid and accurate detection of abnormalities, from identifying tumors during radiological examinations to detecting early signs of eye disease in retinal images. The article also highlights various applications of AI in medical imaging, including radiology, pathology, cardiology, and more. AI-based diagnostic tools not only speed up the interpretation of complex images but also improve early detection of disease, ultimately delivering better outcomes for patients. Additionally, AI-based image processing facilitates personalized treatment plans, thereby optimizing healthcare delivery. This literature review highlights the paradigm shift that AI has brought to medical imaging, highlighting its role in revolutionizing diagnosis and patient care. By combining cutting-edge AI techniques and their practical applications, it is clear that AI will continue shaping the future of healthcare in profound and positive ways.

Artificial intelligence (AI) is transforming the mental health care environment. AI tools are increasingly accessed by clients and service users. Mental health professionals must be prepared not only to use AI but also to have conversations about it when delivering care. Despite the potential for AI to enable more efficient and reliable and higher-quality care delivery, there is a persistent gap among mental health professionals in the adoption of AI. A needs assessment was conducted among mental health professionals to (1) understand the learning needs of the workforce and their attitudes toward AI and (2) inform the development of AI education curricula and knowledge translation products. A qualitative descriptive approach was taken to explore the needs of mental health professionals regarding their adoption of AI through semistructured interviews. To reach maximum variation sampling, mental health professionals (eg, psychiatrists, mental health nurses, educators, scientists, and social workers) in various settings across Ontario (eg, urban and rural, public and private sector, and clinical and research) were recruited. A total of 20 individuals were recruited. Participants included practitioners (9/20, 45% social workers and 1/20, 5% mental health nurses), educator scientists (5/20, 25% with dual roles as professors/lecturers and researchers), and practitioner scientists (3/20, 15% with dual roles as researchers and psychiatrists and 2/20, 10% with dual roles as researchers and mental health nurses). Four major themes emerged: (1) fostering practice change and building self-efficacy to integrate AI into patient care; (2) promoting system-level change to accelerate the adoption of AI in mental health; (3) addressing the importance of organizational readiness as a catalyst for AI adoption; and (4) ensuring that mental health professionals have the education, knowledge, and skills to harness AI in optimizing patient care. AI technologies are starting to emerge in mental health care. Although many digital tools, web-based services, and mobile apps are designed using AI algorithms, mental health professionals have generally been slower in the adoption of AI. As indicated by this study's findings, the implications are 3-fold. At the individual level, digital professionals must see the value in digitally compassionate tools that retain a humanistic approach to care. For mental health professionals, resistance toward AI adoption must be acknowledged through educational initiatives to raise awareness about the relevance, practicality, and benefits of AI. At the organizational level, digital professionals and leaders must collaborate on governance and funding structures to promote employee buy-in. At the societal level, digital and mental health professionals should collaborate in the creation of formal AI training programs specific to mental health to address knowledge gaps. This study promotes the design of relevant and sustainable education programs to support the adoption of AI within the mental health care sphere.

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.

With the rapid changes taking place in the country, including the education system in general and nursing education in particular, the role of professional nurses as reflective clinical learning facilitators need to be re-visited in order to meet the changing health needs of the community and to facilitate outcome-based nursing education and evidence-based quality nursing care. The purpose of this paper is to explore and describe the perceptions of professional nurses as reflective clinical learning facilitators in the clinical learning units, within the context of a specific health-care service in Gauteng. A phenomenological method using descriptive naïve sketches was used to collect data from twenty professional nurses complying with certain inclusion criteria. A content analysis was performed and eight categories (main concepts) were identified in order of priority as follows: communication/collaboration; role-modelling; continuous assessment and evaluation; up-to-date knowledge; scientific approach; clinical teaching; management and professionalism. After a literature control was conducted, these main concepts were confirmed. It is recommended that a model to facilitate reflective thinking in clinical nursing education be developed, using these concepts as basis for the provisional conceptual framework.

Artificial intelligence (AI) has already revolutionized the analysis of image, text, and tabular data, bringing significant advances across many medical sectors. Now, by combining with wearable inertial measurement units (IMUs), AI could transform health care again by opening new opportunities in patient care and medical research. This systematic review aims to evaluate the integration of AI models with wearable IMUs in health care, identifying current applications, challenges, and future opportunities. The focus will be on the types of models used, the characteristics of the datasets, and the potential for expanding and enhancing the use of this technology to improve patient care and advance medical research. This study examines this synergy of AI models and IMU data by using a systematic methodology, following PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines, to explore 3 core questions: (1) Which medical fields are most actively researching AI and IMU data? (2) Which models are being used in the analysis of IMU data within these medical fields? (3) What are the characteristics of the datasets used for in this fields? The median dataset size is of 50 participants, which poses significant limitations for AI models given their dependency on large datasets for effective training and generalization. Furthermore, our analysis reveals the current dominance of machine learning models in 76% on the surveyed studies, suggesting a preference for traditional models like linear regression, support vector machine, and random forest, but also indicating significant growth potential for deep learning models in this area. Impressively, 93% of the studies used supervised learning, revealing an underuse of unsupervised learning, and indicating an important area for future exploration on discovering hidden patterns and insights without predefined labels or outcomes. In addition, there was a preference for conducting studies in clinical settings (77%), rather than in real-life scenarios, a choice that, along with the underapplication of the full potential of wearable IMUs, is recognized as a limitation in terms of practical applicability. Furthermore, the focus of 65% of the studies on neurological issues suggests an opportunity to broaden research scope to other clinical areas such as musculoskeletal applications, where AI could have significant impacts. In conclusion, the review calls for a collaborative effort to address the highlighted challenges, including improvements in data collection, increasing dataset sizes, a move that inherently pushes the field toward the adoption of more complex deep learning models, and the expansion of the application of AI models on IMU data methodologies across various medical fields. This approach aims to enhance the reliability, generalizability, and clinical applicability of research findings, ultimately improving patient outcomes and advancing medical research.