Citations

Abstract Generative AI (Gen AI), Foundation Models (FMs), and Large Language Models (LLMs) are powerful emerging technologies that demonstrate exceptional capabilities in processing vast amounts of unstructured and structured data, including text, voice, images, video and other formats, and adapting to a wide range of specific tasks. Their immense potential to drive meaningful improvements in treatment outcomes is increasingly evident. The advent of these technologies has marked a transformative era in healthcare, including the fields of radiation oncology and medical physics. Specifically, these powerful technologies offer unprecedented opportunities to analyze domain‐specific data, process and synthesize medical images, automate routine tasks, support clinical decision‐making, optimize and streamline clinical workflows, and enhance the quality of clinical trials. While these emerging technologies present new opportunities to revolutionize radiation therapy practice, their implementation also raises important educational, ethical, and regulatory considerations. This scoping review highlights benefits, promises, risks, and challenges, such as interpretability, data privacy, regulatory compliance, reproducibility, hallucination, and integration into existing clinical workflow. Finally, emerging opportunities are outlined to guide future research directions. This review paper provides a timely overview of Gen AI, FMs and LLMs, aiming to inform medical physicists, clinicians, and researchers of the evolving role of these disruptive technologies in shaping the future of radiation therapy.

Abstract Background Lupus nephritis remains a major cause of chronic kidney disease and kidney failure in systemic lupus erythematosus. Glucocorticoids are central to treatment but are limited by systemic toxicity. We evaluated whether a previously characterized collagen IV α3-targeted liposomal nanoparticle formulation carrying low-dose prednisolone could attenuate established lupus nephritis in MRL/lpr mice. Methods Female MRL/lpr mice with disease present at treatment initiation and C57BL/6J control mice received saline or collagen IV α3-targeted prednisolone-loaded nanoparticles (Col4-α3-Pred-NPs). Renal outcomes were assessed by longitudinal proteinuria, glomerular filtration rate (GFR), survival, kidney histopathology, renal IgG and C3d deposition, dUTP/TUNEL-associated injury staining, and renal cytokine/chemokine profiling. Body weight, food and water intake, and blood glucose were monitored as measures of general condition and preliminary tolerability. Results Col4-α3-Pred-NPs improved survival in MRL/lpr mice, reduced cumulative proteinuria burden, and attenuated terminal GFR decline compared with saline-treated MRL/lpr controls. Treatment reduced glomerular and tubulointerstitial injury, lowered composite EGTI histopathology scores, decreased terminal kidney enlargement, reduced glomerular IgG deposition and renal dUTP-positive injury signals, and reduced renal signals for IL-28A/B, IL-7, PD-ECGF, IL-11, CCL6/C10, and IL-15. C3d deposition was not significantly altered. Nanoparticle treatment was not associated with sustained treatment-related increases in blood glucose or body-weight loss during the measured study period. Conclusions Collagen IV α3-targeted liposomal delivery of low-dose prednisolone attenuated established lupus nephritis in MRL/lpr mice and improved renal structural, functional, inflammatory, and survival outcomes. These findings support further evaluation of glomerulus-targeted nanotherapy as a potential strategy to improve the precision and therapeutic index of glucocorticoid treatment in lupus nephritis.

Purpose: Depression is prevalent yet frequently underdiagnosed. Although speech-based detection methods show promise, most prior studies have emphasized binary classification in nontonal languages. This study examined whether acoustic features of spontaneous Mandarin speech reflect depression as a categorical condition or a continuous, dimensional construct. Method: A validated emotional Mandarin speech corpus paired with self-reported depression severity scores was analyzed. Acoustic features were extracted using the extended Geneva Minimalistic Acoustic Parameter Set. A multistage analytic framework was applied, including random forest classification to distinguish depressed from nondepressed clips, linear mixed-effects modeling to examine group and severity effects, and unsupervised clustering to examine latent structure in the acoustic feature space. Results: The random forest classifier achieved an accuracy of 72.3% and an area under the receiver operating characteristic curve of 0.826 on the held-out test set. Model performance favored sensitivity over precision in identifying depressed clips. SHapley Additive exPlanations analysis identified features across multiple acoustic domains, including spectral, pitch-related, voice quality, cepstral, and intensity measures, as important contributors to model predictions. Among the Top 10 features, no acoustic features showed statistically significant group differences. In contrast, median and mean fundamental frequencies were significantly positively associated with depression severity. Clustering analysis revealed overlapping group structures and a continuous distribution of severity scores across clusters. Conclusions: These findings provide preliminary support for a dimensional conceptualization of depression in spontaneous Mandarin speech. While classification models can distinguish depressed from nondepressed speech with moderate accuracy, acoustic features appear to vary more consistently with symptom severity than with diagnostic group. Speech-based measures may therefore have potential for continuous monitoring of depression, although this possibility requires further longitudinal verification.

ABSTRACT The integration of Internet of Things devices in medicine has paved the way for novel diagnostic and therapeutic solutions that offer ‘smart’ capabilities, especially for at‐home wound monitoring. Such capabilities include increasing portability, diminished gaps in care through continuous analysis, and real‐time programmable manipulation of device parameters to intervene more rapidly. This scoping review explores the landscape of Internet of Things devices, including smart bandages, wearable and skin‐interfaced sensors, and mobile app‐connected devices, specifically designed for chronic wound monitoring in a remote, at‐home setting. We reviewed literature from PubMed, IEEE Xplore, Scopus, and Web of Science, using keywords related to Internet of Things and chronic wound care. The evaluation criteria focused on biomarkers analysed, Internet of Things connectivity technology, experimental models used for device validation, and unique device strengths or weaknesses. Our review identified 52 unique studies comprising 77 data entries across five biomarker categories: gas and oxygen saturation, pH, metabolic markers, biophysical parameters of the wound microenvironment, and molecular markers of inflammation or infection. These devices span a wide landscape of biomarkers analysed to predict and monitor wound recovery, with the potential to improve patient outcomes through personalised treatment and timely interventions. However, many devices were never validated clinically on humans and faced limitations in power delivery, accuracy, stability, and reliability that challenge their potential usability in humans for wound monitoring. This review provides a comprehensive understanding of the potential and limitations of Internet of Things devices in at‐home wound care, highlighting areas for future research and development.

Abstract Lupus nephritis (LN) requires long-term immunosuppressive therapy, which is often associated with severe systemic side effects. Therefore, new therapeutic strategies that maintain high efficacy while minimizing adverse effects are essential. Although nanomedicine has advanced systemic and kidney-targeted drug delivery, a reliable method for glomerulus-specific delivery is lacking. Collagen IV (Col4)-alpha 3, located in the glomerular basement membrane at the blood–tissue interface through fenestrated capillary endothelium, represents an ideal target for glomeruli delivery. Herein, we developed a novel liposomal nanoparticle conjugated with a Col4-alpha 3-binding peptide (Col4-α3-NPs) for selective glomerular targeting. Prednisolone-loaded Col4-α3-NPs were administered to lupus-prone mice twice weekly for 8 weeks. Kidney injury and function were evaluated biweekly, and renal immune cell populations were analyzed by flow cytometry at study completion. The results show that rhodamine-labeled NPs predominantly accumulate in kidney glomeruli 48 h after intravenous injection. The Col4-NP system demonstrated stable and prolonged release of the encapsulated drug for over 48 h. Lupus-prone mice treated with prednisolone-loaded Col4-NPs showed significantly improved renal function and histology, including a 30% increase in glomerular filtration rate, a 56% reduction in proteinuria, and decreased IgG deposition and fibrosis. Notably, treatment also enhanced renal regulatory T cell populations. These findings suggest that glomerulus-targeted Col4-α3-NPs hold significant translational promise. This platform may offer an effective, site-specific treatment for LN while minimizing systemic side effects and could be adapted for other glomerular diseases requiring targeted therapy.