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Implementing artificial intelligence (AI) in healthcare organizations involves the entire organization. This groundbreaking technology is becoming central to achieve the goals of the new healthcare through the ongoing commitment to sustainability despite the severe lack of resources. Decision-makers in healthcare need knowledge and skills to prepare for the changes in many professional activities in the years ahead. Furthermore, chief medical officers and clinical leaders need to act on the opportunities that AI can bring, starting from its integration into the reality of healthcare settings while working with those responsible for managing and implementing AI in compliance with current legislation in Europe and the United States. Finally, stakeholders need to know how to leverage AI capabilities and how to recognize its limitations and its opportunities in administrative applications (admin AI) to optimize day-to-day operations and clinical applications (non-admin AI). In this view, clinical leaders and health care decision-makers may appreciate AI as a new way to provide sustainable social and healthcare services.

Common challenges for healthcare systems worldwide are population ageing, rising therapy spending and reduced economic resources. In response, AI can play a crucial role in facilitating managerial and economic objectives within a holistic vision of care and improve the experience of patients and professionals. AI may change the delivery of services and the demand for them as well. This raises questions of how to balance the supply and demand sides of healthcare services, how to leverage competitive positioning and how to differentiate strategies specific to the public and to the private sector.

Several primary challenges can be identified in the strategic application of AI for organizational optimization within the operational framework of diverse healthcare settings. To develop future clinical leaders, essential skills and competencies need to be cultivated within teams through training and support. The overarching aims are to foster skills for health management and leadership, as well as promote organizational behaviour for change and health system facilitators (i.e. payment systems and health information systems) to incentivize adoption. This chapter provides readers with a comprehensive roadmap for the implementation of AI in healthcare settings.

The complex challenges facing the healthcare sector call for a revision of the ways it can provide high-quality services with economic sustainability. Revision can proceed along different pathways. Among the new paradigms of healthcare is the shift from a silo approach by hospitals towards an integrated, multidisciplinary approach. This entails restructuring hospitals in disease centres and exploring how AI can aid in the integration of hospital services and community care. Reorganization is vital to the development of patient-centred healthcare and the holistic approach. To achieve these goals, healthcare and policy decision-makers need to consider both the administrative and the clinical aspects of everyday issues. AI can play a key role in helping balance this duality. The overarching objective is to create interdisciplinary therapeutic and diagnostic pathways within care networks shared between the hospital and the community. This involves the analysis of huge amounts of data and interdisciplinary knowledge beyond the grasp of an individual. Therefore, knowing how AI can help in the development and reorganization of community healthcare is essential for clinical leaders to take advantage of this enormous opportunity in larger settings.

Artificial intelligence (AI) applications are increasingly used for day-to-day operations in healthcare. Each has a relatively limited scope or task, and several find application in managerial and organizational processes. More and more, AI and machine learning (ML) devices have received US FDA approval in the last decade. This chapter covers the main AI applications in healthcare, with a focus on organizational AI solutions (administrative AI), the main AI developers, their investment and real-world data and case studies in healthcare and other sectors. AI can be applied in resource management and procurement, resource allocation, clinical case management, staff work shift scheduling and handling of emergencies. AI applications are becoming ubiquitous in hospital (e.g. emergency room and operating theatre) and outpatient settings (e.g. ambulatory care and dentistry clinics). Their implementation is expected to bring direct benefits for patient care and satisfaction. This chapter gives a broad definition of AI in healthcare settings, with a focus on administrative applications and their use in case study data.

This study aims to carry out a critical analysis of the methods used to deal with the regulatory impact assessment while proposing an alternative method to overcome some of the drawbacks of the aforementioned methods.

To achieve the objective of this work, the methods currently used in regulatory impact analysis are presented by highlighting their scope and the problems they may pose during their applications. After that, the adjusted variant of radial measure is suggested as an alternative method to the aforementioned methods while showing its relevance with regard to other methods using pertinent criteria. Finally, for concretization, a case study related to the sanctions against Russia after its invasion of Ukraine is presented.

The findings show that regulations related to the sanctions against Russia are good enough, with a score of 0.846. However, this score is less good in several countries like Germany (0.671), Italy (0.677) and France (0.745) and in the poorest countries.

The originality of this work resides in using a novel method in the regulatory impact analysis field, which is adjusted variant of radial measure. This method increases the effectiveness of the regulatory impact assessment.

This paper aims to present the application and critical reflection on the effects of a intervention for children with autism spectrum disorder (ASD): the Soundbeam Imitation Intervention (SII). The intervention is based on the imitation of meaningless body gestures supported by a musical feedback. The rationale underlying SII is that mirror neurons deficit may represent the cause for the incomplete development of social and motor functioning in children with ASD. Following this assumption, it is possible to hypothesise that a systematic activation of this a system through the simultaneous observation-execution of meaningless body gestures may affect functional changes of mirror-related functions.

A sample of 14 children, who were between 5 and 9 years of age, with a diagnosis of ASD were involved in a six weeks’ SII programme. The programme is designed as a three-step progression, where each step includes exercises that focus on an activity: synchronous/one arm imitation, synchronous/two arms imitation and delayed imitation. Exercises are based on repeated movements-melodies associations of increasing difficulty. Motor imitation and social attention were assessed using a synchronous video-modelling task pre and post intervention.

Data highlight significant improvements in imitation accuracy and duration of social sustained attention were achieved.

Data reported in this paper provide preliminary and promising evidence that imitation and social attention skills acquired through SII can be generalised to a video-modelling imitation setting. The SII ordinal execution has included meaningless gestures, usually excluded from previous interventions, and this adds further validity to the training.