Digital Transformation and the Reconstruction of Nursing Professional Identity: Moving Beyond the Intangible Professional Project.

Artificial intelligence (AI) is recognised as a key element in the digital transformation of society, offering economic and societal benefits across the entire spectrum of industries (European Union ), and its potential in the context of healthcare is also evident. According to the World Health Organization's (WHO) State of the World's Nursing Report 2025, the global nursing workforce faced an estimated shortage of 5.8 million nurses in 2023 (WHO ). It is projected that by 2030, the global shortage of healthcare workers will reach 10 million (Boniol et al. ). Additionally, almost half of healthcare professionals' working time is currently spent interacting with various electronic health record systems (Budd ; Toscano et al. ). Therefore, it is crucial to develop digital solutions that enable healthcare professionals to have more time on direct patient care and meaningful interaction with patients. In recent years, the development of AI technologies has progressed rapidly, and their adoption in healthcare has become more feasible (Chhibber et al. ). Applications have expanded from diagnostic and decision support tools to administrative workflows, making the integration of AI into everyday clinical practice increasingly common. AI is a multifaceted concept that encompasses a wide range of methods, applications, technologies and research directions (Boucher ). According to the European Union, AI systems refer to machine‐based systems designed to operate with varying levels of autonomy. The AI system infers from the input it receives how to generate outputs such as content, recommendations, predictions or decisions that can influence physical or virtual environments (European Union ). Machine learning (ML) can be considered as a subfield of AI where computers learn patterns and form associations based on data (Rudner and Toner ). Other areas of the practical application of AI include: (1) computer vision, which relates to the automation of image recognition, analysis and interpretation of visual information, (2) natural language processing (NLP) which considers the automation of reading, analysis and generation of human language, (3) speech recognition concerned with the detection, analysis and interpretation of spoken human language and (4) robotics, which brings together different types of AI with the addition of manipulating physical objects (Sheikh et al. ). Due to the broad nature of the definition of AI and the lack of precise boundaries, this study does not exclude any area or type of AI. According to previous studies, AI and its applications are seen to offer several opportunities in healthcare. It can support the automation of repetitive tasks, improve medication accuracy and facilitate clinical decision‐making among other applications (Wubineh et al. ). The use of computer vision has improved the accuracy and reduced the time spent on diagnostics in radiology and pathology, streamlining and improving workflow efficiencies (Jeong et al. ). Speech recognition and NLP techniques have been used in automating clinical documentation to reduce administrative burden and clinician burnout with promising results (Olson et al. ; van Buchem et al. ). Care robots have been reported to free nursing time for direct care, especially in logistics‐related and routine tasks (Adeyemo et al. ). The implementation of AI systems also raises several challenges, such as ethical concerns and issues related to data protection, unreliability of technology (Wubineh et al. ), patient safety (Adeyemo et al. ) and data quality (Silcox et al. ). Moreover, there is still limited evidence about the effects of AI on the clinical roles, human expertise and skills (upskilling vs. deskilling), especially from the perspective of healthcare professionals outside medicine (Miller and Mutha ). Addressing these challenges is essential for the successful and meaningful implementation of AI, especially considering healthcare professionals' direct and indirect exposure to AI in their daily work. Healthcare professionals' perceptions are crucial in the development of AI‐driven solutions in healthcare, as they offer valuable insights into practical experiences, patient safety and educational needs related to AI. Moreover, the perceptions and attitudes also influence how effectively AI can be integrated into healthcare in the future. Therefore, it is essential to explore and understand the views of healthcare professionals on AI.