This study presents a comprehensive literature review using a systematic approach to explore the role of artificial intelligence (AI) in promoting sustainable development in line with the United Nations Sustainable Development Goals (SDGs). The systematic review approach was applied to collect and analyze topics, and the literature search was conducted in two stages, encompassing 57 articles that met the research requirements. Our analysis reveals that AI's contribution to sustainability is concentrated within three key areas: organizational, technical, and processing aspects. The organizational aspect focuses on the integration of AI in companies and industries, addressing barriers to implementation and the relationship between companies, partners, and customers. The technical aspect highlights the development of AI algorithms that can address global challenges and contribute to the growth of stability and development in society. The processing aspect emphasizes the internal transformation of companies, their business models, and strategies in response to AI integration. Our proposed conceptual model outlines the essential elements organizations must consider when incorporating AI into their sustainability efforts, such as strategic alignment, infrastructure development, change management, and continuous improvement. By addressing these critical aspects, organizations can harness the potential of AI to drive positive social, environmental, and economic outcomes, ultimately contributing to the achievement of the SDGs. The model serves as a comprehensive framework for organizations seeking to leverage AI for sustainable development, but it should be adapted to individual contexts to ensure its relevance and effectiveness.

Purpose: Value creation based on artificial intelligence (AI) can significantly change global healthcare. Diagnostics, therapy and drug discovery start-ups are some key forces behind this change. This article aims to study the process of start-ups' value creation within healthcare.Design/methodology/approach: A multiple case study method and a business model design approach were used to study nine European start-ups developing AI healthcare solutions. Obtained information was performed using within and cross-case analysis.Findings: Three unique design elements were established, with 16 unique frames and three unifying design themes based on business models for AI healthcare start-ups.Originality/value: Our in-depth framework focuses on the features of AI start-up business models in the healthcare industry. We contribute to the business model and business model innovation by systematically analyzing value creation, how it is delivered to customers, and communication with market participants, as well as design themes that combine start-ups and categorize them by specialization.

Purpose: Virtual and augmented reality solutions in medicine are generally applied in communication, training, simulation and therapy. However, like most new digital developments, these technologies face a large number of institutional barriers that are inherent to the medical sector.Design/methodology/approach: Following Richard Scott's view on institutions and organizations, a multiple case study is used to analyze regulatory, normative and cultural-cognitive institutional pillars in the medical industry.Findings: The results of the study demonstrate that (1) the regulatory pillar inhibits the advancement of new technologies in the approach to treatment, regulation of patient data, educational processes for medical staff, and information and financial flows; (2) the number of barriers increases based on the solution's level of disruption and the number of variable conventional procedures; (3) trust between participants in the medical industry plays an important role in introducing new technologies; (4) new participants need to address certain pillars depending on the area of application.Originality/value: The authors discuss top-down and bottom-up approaches for overcoming institutional barriers when implementing augmented and virtual reality solutions for companies focusing on the medical market.

Purpose: The purpose of this study is to examine the role of artificial intelligence (AI) in transforming the healthcare sector, with a focus on how AI contributes to entrepreneurship and value creation. This study also aims to explore the potential of combining AI with other technologies, such as cloud computing, blockchain, IoMT, additive manufacturing and 5G, in the healthcare industry.Design/methodology/approach: Exploratory qualitative methodology was chosen to analyze 22 case studies from the USA, EU, Asia and South America. The data source was public and specialized podcast platforms.Findings: The findings show that combining technologies can create a competitive advantage for technology entrepreneurs and bring about transitions from simple consumer devices to actionable healthcare applications. The results of this research identified three main entrepreneurship areas: 1. Analytics, including staff reduction, patient prediction and decision support; 2. Security, including protection against cyberattacks and detection of atypical cases; 3. Performance optimization, which, in addition to reducing the time and costs of medical procedures, includes staff training, reducing capital costs and working with new markets.Originality/value: This study demonstrates how AI can be used with other technologies to cocreate value in the healthcare industry. This study provides a conceptual framework, “AI facilitators – AI achievers,” based on the findings and offer several theoretical contributions to academic literature in technology entrepreneurship and technology management and industry recommendations for practical implication.

This article introduces a novel seven-step methodology designed to incorporate podcasts as qualitative data sources in academic research, addressing a notable gap in current literature. The purpose of this methodology is to harness the unique and timely insights offered by podcasts, thereby enriching academic studies with diverse, real-time perspectives. The design of this approach includes a meticulous selection of podcasts based on relevance and credibility, comprehensive ethical considerations, strategic sampling methods, robust data collection and analysis techniques, and recommendations for applying best research practices. Key to this methodology is the use of weighted scoring systems to mitigate biases and the establishment of ethical guidelines specifically tailored for podcast data. The results of this study indicate the high potential of the proposed methodology in employing podcasts as an additional source of data, providing dynamic and current perspectives often absent in traditional research sources. Future studies are encouraged to delve deeper into refining and standardizing the proposed methodology, focusing on enhancing the weight metrics through advanced statistical techniques and exploring new technological tools for more efficient podcast data extraction and analysis.