This study delves into the transformative potential of integrating augmented reality (AR) within imaging technologies, shedding light on this evolving landscape. Through a comprehensive narrative review, this research uncovers a wealth of literature exploring the intersection between AR and medical imaging, highlighting its growing prominence in healthcare. AR’s integration offers a host of potential opportunities to enhance surgical precision, bolster patient engagement, and customize medical interventions. Moreover, when combined with technologies like virtual reality (VR), artificial intelligence (AI), and robotics, AR opens up new avenues for innovation in clinical practice, education, and training. However, amidst these promising prospects lie numerous unanswered questions and areas ripe for exploration. This study emphasizes the need for rigorous research to elucidate the clinical efficacy of AR-integrated interventions, optimize surgical workflows, and address technological challenges. As the healthcare landscape continues to evolve, sustained research efforts are crucial to fully realizing AR’s transformative impact in medical imaging. Systematic reviews on AR in healthcare also overlook regulatory and developmental factors, particularly in regard to medical devices. These include compliance with standards, safety regulations, risk management, clinical validation, and developmental processes. Addressing these aspects will provide a comprehensive understanding of the challenges and opportunities in integrating AR into clinical settings, informing stakeholders about crucial regulatory and developmental considerations for successful implementation. Moreover, navigating the regulatory approval process requires substantial financial resources and expertise, presenting barriers to entry for smaller innovators. Collaboration across disciplines and concerted efforts to overcome barriers will be essential in navigating this frontier and harnessing the potential of AR to revolutionize healthcare delivery.

The integration of artificial intelligence in interventional radiology is an emerging field with transformative potential, aiming to make a great contribution to the health domain. This overview of reviews seeks to identify prevailing themes, opportunities, challenges, and recommendations related to the process of integration. Utilizing a standardized checklist and quality control procedures, this review examines recent advancements in, and future implications of, this domain. In total, 27 review studies were selected through the systematic process. Based on the overview, the integration of artificial intelligence (AI) in interventional radiology (IR) presents significant opportunities to enhance precision, efficiency, and personalization of procedures. AI automates tasks like catheter manipulation and needle placement, improving accuracy and reducing variability. It also integrates multiple imaging modalities, optimizing treatment planning and outcomes. AI aids intra-procedural guidance with advanced needle tracking and real-time image fusion. Robotics and automation in IR are advancing, though full autonomy in AI-guided systems has not been achieved. Despite these advancements, the integration of AI in IR is complex, involving imaging systems, robotics, and other technologies. This complexity requires a comprehensive certification and integration process. The role of regulatory bodies, scientific societies, and clinicians is essential to address these challenges. Standardized guidelines, clinician education, and careful AI assessment are necessary for safe integration. The future of AI in IR depends on developing standardized guidelines for medical devices and AI applications. Collaboration between certifying bodies, scientific societies, and legislative entities, as seen in the EU AI Act, will be crucial to tackling AI-specific challenges. Focusing on transparency, data governance, human oversight, and post-market monitoring will ensure AI integration in IR proceeds with safeguards, benefiting patient outcomes and advancing the field.

The application of chatbots and NLP in radiology is an emerging field, currently characterized by a growing body of research. An umbrella review has been proposed utilizing a standardized checklist and quality control procedure for including scientific papers. This review explores the early developments and potential future impact of these technologies in radiology. The current literature, comprising 15 systematic reviews, highlights potentialities, opportunities, areas needing improvements, and recommendations. This umbrella review offers a comprehensive overview of the current landscape of natural language processing (NLP) and natural language models (NLMs), including chatbots, in healthcare. These technologies show potential for improving clinical decision-making, patient engagement, and communication across various medical fields. However, significant challenges remain, particularly the lack of standardized protocols, which raises concerns about the reliability and consistency of these tools in different clinical contexts. Without uniform guidelines, variability in outcomes may hinder the broader adoption of NLP/NLM technologies by healthcare providers. Moreover, the limited research on how these technologies intersect with medical devices (MDs) is a notable gap in the literature. Future research must address these challenges to fully realize the potential of NLP/NLM applications in healthcare. Key future research directions include the development of standardized protocols to ensure the consistent and safe deployment of NLP/NLM tools, particularly in high-stake areas like radiology. Investigating the integration of these technologies with MD workflows will be crucial to enhance clinical decision-making and patient care. Ethical concerns, such as data privacy, informed consent, and algorithmic bias, must also be explored to ensure responsible use in clinical settings. Longitudinal studies are needed to evaluate the long-term impact of these technologies on patient outcomes, while interdisciplinary collaboration between healthcare professionals, data scientists, and ethicists is essential for driving innovation in an ethically sound manner. Addressing these areas will advance the application of NLP/NLM technologies and improve patient care in this emerging field.

Objectives: Three-dimensional (3D) planning and Patient Specific Instrumentation (PSI) can help the surgeon to obtain more predictable results in Medial Opening Wedge High Tibial Osteotomy (mOW-HTO) than the conventional techniques. We compared the accuracy of the PSI and standard techniques and measured the learning curve for surgery time and number of fluoroscopic shots. Methods: We included the first 12 consecutive cases of mOW-HTO performed with 3D planning and PSI cutting guides and the first 12 non-supervised mOW-HTO performed with the standard technique. We recorded surgery time and fluoroscopic time. We calculated the variation (Δ delta) between the planned target and the postoperative result for Hip Knee Ankle Angle (HKA), mechanical medial Proximal Tibia Angle (MPTA), Joint Line Convergence Angle (JLCA) and tibial slope (TS) and compared it both groups. We also recorded the complication rate. We then calculated the learning curves for surgery time, number of fluoroscopic shots, Δ from target in both groups. CUSUM analysis charts for learning curves were applied between the two groups. Results: Mean surgical time and mean number of fluoroscopic shots were lower in PSI group (48.58±7.87 vs. 58.75±6.86 min; p=0.034 and 10.75±3.93 vs. 18.16±4.93 shots; p<0.001). The postoperative ΔHKA was 0.42±0.51° in PSI vs. 1.25±0.87° in conventional, p=0.005. ΔMPTA was 0.50±0.67° in PSI vs. 3.75±1.48° in conventional, p<0.001; ΔTS was 1.00±0.82° in PSI vs. 3.50±1.57° in conventional, p<0.001. ΔJLCA was 1.83±1.11° in PSI vs. 4±1.41° in conventional, p<0.001. The CUSUM analysis favoured PSI group regarding surgery time (p=0.034) and number of shots (p<0.001) with no learning curve effect for ΔHKA, ΔMPTA, ΔJLCA and ΔTS. Conclusions: PSI cutting guides and 3D planning for HTO are effective in reducing the learning curves for operation time and number of fluoroscopic shots. Accuracy of the procedure has been elevated since the first cases.

Background/Objectives: Pain, anxiety, and distress are common yet frequently insufficiently managed issues for patients undergoing radiology and radiotherapy procedures. Immersive technologies, including virtual reality (VR), augmented reality (AR), and mixed reality (MR), are emerging as innovative non-pharmacological approaches to alleviate such burdens through engaging interventions. This review, combining scoping and narrative methodologies, seeks to examine the current application, efficacy, and integration of these technologies to enhance patient care and wellbeing within diagnostic and oncological environments. Methods: Employing a mixed scoping and narrative review approach, this study conducted a systematic search of PubMed, EMBASE, Scopus, and Web of Science databases (no date restrictions—search included studies up to May 2025) to identify relevant studies utilizing VR, AR, MR, or XR for mitigating pain, anxiety, or distress in patients undergoing radiology or radiotherapy. Two independent reviewers selected eligible papers, with data extracted systematically. The narrative analysis supplemented the scoping review by providing contextual insights into clinical relevance and technological challenges. Results: The screening process identified 76 articles, of which 27 were assessed for eligibility and 14 met the inclusion criteria. Most studies focused on oncology and primarily employed VR as the immersive technology. VR has shown promising effects in reducing anxiety and pain—particularly during radiotherapy sessions and invasive procedures—and in supporting patient education through engaging, immersive experiences, making it a valuable approach meriting further investigation. Patient acceptance was notably high, especially among those with elevated distress levels. However, findings in radiology were less consistent, likely due to shorter procedure durations limiting the effectiveness of VR. The variability in outcomes highlights the importance of tailoring immersive interventions to specific procedures and patient needs. The narrative component identified key barriers, such as regulatory hurdles, standardization issues, and implementation challenges, that need addressing for broader clinical adoption. Conclusions: Immersive digital therapeutics are evolving from preliminary research tools toward more structured incorporation into clinical practice. Their future success relies on harmonizing technological advancements with patient-focused design and robust clinical evidence. Achieving this will require collaborative efforts among researchers, industry stakeholders, and healthcare providers. The integration of scoping and narrative review methods in this study offers a comprehensive perspective on the current landscape and informs strategic directions for advancing immersive technologies in radiology and radiotherapy.