The transformative potential of artificial intelligence (AI) in medical Imaging (MI) is well recognized. Yet despite promising reports in research settings, many AI tools fail to achieve clinical adoption in practice. In fact, more generally, there is a documented 17-year average delay between evidence generation and implementation of a technology. Implementation science (IS) may provide a practical, evidence-based framework to bridge the gap between AI development and real-world clinical imaging use, to shorten this lag through systematic frameworks, strategies, and hybrid research designs. We outline challenges specific to AI adoption in MI workflows, including infrastructural, educational, and cultural barriers. We highlight the complementary roles of effectiveness research and implementation research, emphasizing hybrid study designs and the role of integrated KT (iKT), stakeholder engagement, and equity-focused co-creation in designing sustainable and generalizable solutions. We discuss integration of Human-Computer Interaction (HCI) frameworks in MI towards usable AI. Adopting IS is not only a methodological advancement; it is a strategic imperative for accelerating translation of innovation into improved patient outcomes.

With the increasing importance of data and artificial intelligence, organizations strive to become more data-driven. However, current data architectures are not necessarily designed to keep up with the scale and scope of data and analytics use cases. In fact, existing architectures often fail to deliver the promised value associated with them. Data mesh is a socio-technical, decentralized, distributed concept for enterprise data management. As the concept of data mesh is still novel, it lacks empirical insights from the field. Specifically, an understanding of the motivational factors for introducing data mesh, the associated challenges, implementation strategies, its business impact, and potential archetypes is missing. To address this gap, we conduct 15 semi-structured interviews with industry experts. Our results show, among other insights, that organizations have difficulties with the transition toward federated governance associated with the data mesh concept, the shift of responsibility for the development, provision, and maintenance of data products, and the comprehension of the overall concept. In our work, we derive multiple implementation strategies and suggest organizations introduce a cross-domain steering unit, observe the data product usage, create quick wins in the early phases, and favor small dedicated teams that prioritize data products. While we acknowledge that organizations need to apply implementation strategies according to their individual needs, we also deduct two archetypes that provide suggestions in more detail. Our findings synthesize insights from industry experts and provide researchers and professionals with preliminary guidelines for the successful adoption of data mesh.

The Scandinavian leaders of AI in breast imaging presented their research at the ScreenPoint symposium at EUSOBI 2022 in Malmo, Sweden. Dr Kristina Lang presented the MASAI trial, the first prospective randomized controlled trial on the use of AI in breast screening as an alternative for double reading. Based on her previous retrospective studies, she is convinced that AI could lead to a more efficient and more effective screening programme. In the MASAI trial at Unilabs/Skane University Hospital Malmo, women are randomly assigned to a control arm where exams are double read as usual, or to the AI-based intervention arm: Transpara triages screening exams based on risk for malignancy and assigns 90% of all screening cases to single reading, and 10% to double reading. In addition, the top 1% most suspicious cases are automatically recalled.

SEP 07 2020: Growing complexities in the communication networks today calls for an intelligent approach to network planning and optimization. With the rise of Artificial Intelligence (AI) techniques, new technology paradigms such as network virtualization, self-organizing networks (SONs), intelligent antennas, AI-powered radio-frequency (RF) front end and intelligent chipsets can be easily embedded into the communication networks. Telecom companies are therefore leveraging AI solutions to achieve hyper-automation of telecom networks and usher in an era of self-healing and self-configuring networks. Inclusion of network intelligence allows mobile network operators (MNOs) to achieve efficient network management and cross spectrum protection. This report includes a comprehensive analysis of the adoption of AI in telecom, highlighting the major technology trends and opportunities available across the ecosystem.

Artificial intelligence (AI) is "completely living up to its expected hype and hysteria," with 70% of daily digital interactions being AI-based, and businesses generating multimillion-dollar revenue streams from it. This was the word from Mike Bugembe, founder of UK-based AI consultancy, Lens.ai, delivering a keynote at the ITWeb Business Intelligence Summit 2020, in Johannesburg, today. Bugembe is a bestselling author, international speaker and executive advisor, helping organisations use data and AI to transform their businesses and grow. Discussing the importance of an AI strategy to gain business value, Bugembe noted that companies across the globe are ramping up investments in AI-related technologies and gaining multimillion-dollar-revenue streams, cutting costs, managing risk, improving operations, and finding innovative ways to develop products and strengthen customer intimacy. However, he warned that without an intelligent roadmap, companies risk focusing on the wrong opportunities, resulting in failure to tap into the true promise of AI. "Business and technology experts believe AI will be the most significant technological revolution that businesses have ever experienced," said Bugembe.

One decade passes away, another decade comes – and it makes people think. For starters, we think about what to call the decade about to end: the "twenty-teens", the "twenty-tens"? Awkward to refer to, it may prove harder to classify. The 1920s were "Roaring," but how do you categorize a decade rife with such stark contrasts: Obama and Trump, globalism and nationalism, free trade and trade wars, prosperity and income inequality, recession (at the start) and long bull market, world peace (generally) and "permanent wars." The present is too much with us to gain perspective on what, if anything in particular, this decade will come to mean – other than the mishmash of counter-trends it appears to have been.

DevOps is a software development and delivery process that helps in emphasizing communication along with cross-functional collaboration between product management, software development, and operations professionals. We've curated a list of the Top 12 DevOps Tools, along with their features, based on our decade-long experience in the IT industry, dealing with infrastructure for the significant part. We've taken great care in selecting, benchmarking, and continuously improving our tool selection. That apart, the article also covers the DevOps transformational roadmap as well as the step by step implementation guide. The popularity of DevOps, in recent years, as robust software development and delivery process has been unprecedented.

A few months back, I had the privilege of attending and speaking at the VentureBeat Summit, which gathers senior leaders across various industries to discuss the emerging technologies disrupting the global business agenda. The theme of this year's summit was "Riding the AI Wave," focusing on the explosion of AI in business, specifically how today's leaders use AI to extract meaningful ROI. While the summit touched on a diverse portfolio of use cases, spanning from transportation to data visualization to emotional language technology, one sentiment remained certain: We've raised the bar on AI, and those who don't embrace it risk lagging behind. AI alters more than strategic goals and corporate agendas -- it transforms the C-suite of organizations around the world. As AI becomes more prevalent, the time is now for senior leaders to consider its implications for their businesses' top and bottom lines.