Background: Recent advances in natural language processing (NLP) have heightened the interest of the medical community in its application to health care in general, in particular to stroke, a medical emergency of great impact. In this rapidly evolving context, it is necessary to learn and understand the experience already accumulated by the medical and scientific community. Objective: The aim of this scoping review was to explore the studies conducted in the last 10 years using NLP to assist the management of stroke emergencies so as to gain insight on the state of the art, its main contexts of application, and the software tools that are used. Methods: Data were extracted from Scopus and Medline through PubMed, using the keywords “natural language processing” and “stroke.” Primary research questions were related to the phases, contexts, and types of textual data used in the studies. Secondary research questions were related to the numerical and statistical methods and the software used to process the data. The extracted data were structured in tables and their relative frequencies were calculated. The relationships between categories were analyzed through multiple correspondence analysis. Results: Twenty-nine papers were included in the review, with the majority being cohort studies of ischemic stroke published in the last 2 years. The majority of papers focused on the use of NLP to assist in the diagnostic phase, followed by the outcome prognosis, using text data from diagnostic reports and in many cases annotations on medical images. The most frequent approach was based on general machine learning techniques applied to the results of relatively simple NLP methods with the support of ontologies and standard vocabularies. Although smaller in number, there has been an increasing body of studies using deep learning techniques on numerical and vectorized representations of the texts obtained with more sophisticated NLP tools. Conclusions: Studies focused on NLP applied to stroke show specific trends that can be compared to the more general application of artificial intelligence to stroke. The purpose of using NLP is often to improve processes in a clinical context rather than to assist in the rehabilitation process. The state of the art in NLP is represented by deep learning architectures, among which Bidirectional Encoder Representations from Transformers has been found to be especially widely used in the medical field in general, and for stroke in particular, with an increasing focus on the processing of annotations on medical images.

Artificial intelligence (AI) is substantially changing the world of business. The growth of AI and its impact on business and society are explored in this chapter, including dilemmas and emerging challenges, highlighting the existing gap in the adoption of ethical principles. The elements of human-centered AI (HCAI), such as privacy, explainability, equity, and fairness, are presented, analyzing its relevance in business. and how they need to be addressed to guarantee profitable investments in AI technologies. The aim of this book chapter is to present the essential knowledge needed by business about how to behave more ethically in AI development and deployment. In this regard, we discuss how to increase user confidence and usage of AI devices by presenting a best-practice guide to underscore biases and ensure fairness in AI-based products, exemplified in the financial and healthcare sector. Improving people’s understanding of how AI models operate and having a clear HCAI strategy that evaluates negative potential biases of AI systems will increase user trust, spread, and usage of AI devices, thus ensuring the full acceptance of AI in society, thus promoting human, economic, and institutional growth.

Background: Incorporating Artificial Intelligence (AI) into mental health practice represents a profound transformation of psychological care, enabling novel forms of assessment, intervention, and support. However, this shift also introduces substantial ethical and psychological risks, including erosion of autonomy, algorithmic bias, threats to digital rights, and strain on clinical judgment and therapeutic relationships. This article proposes PRISM, a multilevel framework designed to guide the ethical integration of AI in mental health across individual, technological, and institutional dimensions. Method: An integrative conceptual review was conducted drawing on Self-Determination Theory, information ethics, data justice, and international guidelines from the APA, WHO, EU, and UNESCO. The framework introduces the concept of psychological regulation of AI, extending ethical oversight to the subjective and relational impacts of algorithmic mediation. Results: PRISM articulates four operational principles (digital autonomy, affective transparency, algorithmic justice, and relational care) along with practical tools, including psychological impact indicators and a Psychological Model Card, to support ethical auditing and clinical digital wellbeing. Conclusions: A psychology-based regulatory approach is essential to ensure dignity, autonomy, and ethical sustainability in digital mental health. PRISM provides an operational bridge between technological governance and psychological practice, supporting responsible, person-centred AI use in clinical contexts.

The successful integration of autonomous vehicles (AVs) into society hinges on public acceptance, which is closely linked to trust. This study investigates the factors influencing initial trust and specific trust requirements for the acceptance of AVs among Spanish population. A national survey was conducted with 400 participants, selected to represent the demographic diversity of Spain. The survey assessed participants' prior experience with AVs, demographic characteristics, ethical concerns, and trust levels. The findings indicate that individuals with prior direct experience with AVs exhibit higher initial trust levels. Demographic variables such as age, gender, and education significantly influence trust requirements; notably, younger and higher-educated individuals demonstrate lower trust thresholds. Ethical concerns, including data privacy and algorithmic transparency, emerge as significant predictors of trust levels. When contextualized with international studies, these findings highlight unique cultural and regulatory influences on trust in AVs within Spain. These insights are crucial for policymakers and manufacturers aiming to enhance public trust promote the ethical development and public acceptance of AVs to facilitate the widespread adoption of AVs.