Artificial intelligence (AI) has become increasingly central to precision medicine by enabling the integration and interpretation of multimodal data, yet implementation in clinical settings remains limited. This paper provides a scoping review of literature from 2019-2024 on the implementation of AI in precision medicine, identifying key barriers and enablers across data quality, clinical reliability, workflow integration, and governance. Through an ecosystem-based framework, we highlight the interdependent relationships shaping real-world translation and propose future directions to support trustworthy and sustainable implementation. Traditional healthcare models have difficulty addressing the complexity of modern healthcare needs, particularly given the increasingly multimodal nature of health data spanning genetic, clinical, behavioral, environmental, and lifestyle information (Topol, 2023; Judge et al., 2024; Schouten et al., 2025). As precision medicine emerges as a promising solution for integrating multimodal data into healthcare, a new implementation strategy is necessary due to the complexity of existing healthcare structures and the extent of interdisciplinary collaboration that is now required (Tobias et al., 2023).

Untethered mobile milli/microrobots hold transformative potential for interventional medicine by enabling more precise and entirely non-invasive diagnosis and therapy. Realizing this promise requires bridging the gap between groundbreaking laboratory demonstrations and successful clinical integration. Despite remarkable technical progress over the past two decades, most millirobots and microrobots remain confined to laboratory proof-of-concept demonstrations, with limited real-world feasibility. In this Review, we identify key factors that slow translation from bench to bedside, focusing on the disconnect between technical innovation and real-world application. We argue that the long-term impact and sustainability of the field depend on aligning development with unmet medical needs, ensuring applied feasibility, and integrating seamlessly into existing clinical workflows, which are essential pillars for delivering meaningful patient outcomes. To support this shift, we introduce a strategic milli/microrobot Technology Readiness Level framework (mTRL), which maps system development from initial conceptualization to clinical adoption through clearly defined milestones and their associated stepwise activities. The mTRL model provides a structured gauge of technological maturity, a common language for cross-disciplinary collaboration and actionable guidance to accelerate translational development toward new, safer and more efficient interventions.

Pegasystems Inc., the software company that crushes business complexity, announced a series of new and updated Pega Platform features that help make it even easier for organizations to embed self-service workflows into any front-end channel. Building on Pega's open architecture, these features help businesses accelerate the development of self-service experiences that are increasingly in demand from customers and employees while requiring less time and effort from IT. According to the 2022 Gartner Customer Service and Support Priorities Poll (1), 74% of business respondents say creating a seamless customer journey across assisted and self-service channels is'important' or'very important.' But most companies rely on a mix of siloed technologies that make executing on that priority extremely complex. For example, while a self-service'change of address' sounds simple to build, any developer who's tried to integrate their maze of back-end systems with their web or mobile channels knows the harsh reality of how complex it can be.

Organizations building deep learning data pipelines may struggle with their accelerated I/O needs, and whenever I/O is the question, the usual answer is "throw flash/SSD at it." Certainly expensive all-flash storage arrays are highly beneficial for line-of-business applications (and to storage vendors' sales). But DL applications and workflows are inherently different from typical file-based workloads, and should not be architected the same way. Let's start by looking inside those servers. DL uses several hidden layers of neural networks, such as convolutional (CNN), long short-term memory (LTSM), and/or recurrent (RNN).