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Leadership Dynamics in Driving AI Transformation in Healthcare: Insights from a Scoping Review (Preprint)

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Background The leaders of health care organizations are grappling with rising expenses and surging demands for health services. In response, they are increasingly embracing artificial intelligence (AI) technologies to improve patient care delivery, alleviate operational burdens, and efficiently improve health care safety and quality. Objective In this paper, we map the current literature and synthesize insights on the role of leadership in driving AI transformation within health care organizations. Methods We conducted a comprehensive search across several databases, including MEDLINE (via Ovid), PsycINFO (via Ovid), CINAHL (via EBSCO), Business Source Premier (via EBSCO), and Canadian Business & Current Affairs (via ProQuest), spanning articles published from 2015 to June 2023 discussing AI transformation within the health care sector. Specifically, we focused on empirical studies with a particular emphasis on leadership. We used an inductive, thematic analysis approach to qualitatively map the evidence. The findings were reported in accordance with the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analysis extension for Scoping Reviews) guidelines. Results A comprehensive review of 2813 unique abstracts led to the retrieval of 97 full-text articles, with 22 included for detailed assessment. Our literature mapping reveals that successful AI integration within healthcare organizations requires leadership engagement across technological, strategic, operational, and organizational domains. Leaders must demonstrate a blend of technical expertise, adaptive strategies, and strong interpersonal skills to navigate the dynamic healthcare landscape shaped by complex regulatory, technological, and organizational factors. Conclusions In conclusion, leading AI transformation in healthcare requires a multidimensional approach, with leadership across technological, strategic, operational, and organizational domains. Organizations should implement a comprehensive leadership development strategy, including targeted training and cross-functional collaboration, to equip leaders with the skills needed for AI integration. Additionally, when upskilling or recruiting AI talent, priority should be given to individuals with a strong mix of technical expertise, adaptive capacity, and interpersonal acumen, enabling them to navigate the unique complexities of the healthcare environment.

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At this point we start a new architecture-cycle, for which we’ve allocated eight of our ten available days: one day for each phase of that standard cycle that we set up back in Day 1. This will allow us to explore in more depth our “one idea” that things work better when they work together, on purpose. We summarized the basic structure and context of the architecture-cycle in the previous day’s overview, but we now need to flesh out a bit more of the detail. Whilst working on this, we also need to make sure that each item links back in a fully traceable way to the business-strategy and suchlike
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An increasing networking of IT systems as well as the use of cyber-physical systems in the industrial environment are raising the current amount of data. To process this enormous amount of data and derive conclusions companies use Artificial Intelligence (AI) more frequently. The increasing application and use of AI have a significant impact on socio-technical work systems. In particular, challenges and requirements for leaders and leadership can be identified. Accordingly, leaders and leadership are crucial for implementing and using AI successfully. This and the dynamic development of AI require further research on its impact on leaders and leadership for supporting companies with practice-proven guidelines and recommendations. For developing those a comprehensive analysis of existing literature has been conducted and will be the basis for further steps. The literature analysis’ results were grouped into four main clusters: Strategic Transformation Process, Qualification and Competencies, Culture and Human-AI Interaction. The results are presented in detail and an outlook on the further steps of research and development will be given.
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Artificial intelligence (AI) is poised to broadly reshape medicine, potentially improving the experiences of both clinicians and patients. We discuss key findings from a 2-year weekly effort to track and share key developments in medical AI. We cover prospective studies and advances in medical image analysis, which have reduced the gap between research and deployment. We also address several promising avenues for novel medical AI research, including non-image data sources, unconventional problem formulations and human–AI collaboration. Finally, we consider serious technical and ethical challenges in issues spanning from data scarcity to racial bias. As these challenges are addressed, AI’s potential may be realized, making healthcare more accurate, efficient and accessible for patients worldwide. AI has the potential to reshape medicine and make healthcare more accurate, efficient and accessible; this Review discusses recent progress, opportunities and challenges toward achieving this goal.
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This paper is one of the first to analyse the ethical implications of specific healthcare artificial intelligence (AI) applications, and the first to provide a detailed analysis of AI‐based systems for clinical decision support. AI is increasingly being deployed across multiple domains. In response, a plethora of ethical guidelines and principles for general AI use have been published, with some convergence about which ethical concepts are relevant to this new technology. However, few of these frameworks are healthcare‐specific, and there has been limited examination of actual AI applications in healthcare. Our ethical evaluation identifies context‐ and case‐specific healthcare ethical issues for two applications, and investigates the extent to which the general ethical principles for AI‐assisted healthcare expressed in existing frameworks capture what is most ethically relevant from the perspective of healthcare ethics. We provide a detailed description and analysis of two AI‐based systems for clinical decision support (Painchek® and IDx‐DR). Our results identify ethical challenges associated with potentially deceptive promissory claims, lack of patient and public involvement in healthcare AI development and deployment, and lack of attention to the impact of AIs on healthcare relationships. Our analysis also highlights the close connection between evaluation and technical development and reporting. Critical appraisal frameworks for healthcare AIs should include explicit ethical evaluation with benchmarks. However, each application will require scrutiny across the AI life‐cycle to identify ethical issues specific to healthcare. This level of analysis requires more attention to detail than is suggested by current ethical guidance or frameworks.