Citations

Abstract Large language models (LLMs) and vision‐language models represent a fundamentally different category of artificial intelligence (AI) compared to prior image analysis approaches in digital pathology, which have largely been based on convolutional neural network architectures. This review from the American Society of Cytopathology Clinical Practice Committee examines the current evidence for LLM and vision‐language model applications in cytopathology, including structured reporting, diagnostic assistance, quality control, education, and workflow integration. The distinction between applications with preliminary evidence and those that remain hypothetical is described. A detailed assessment of the challenges that must be addressed before clinical deployment, including hallucination risk, limited explainability, bias, data privacy, validation gaps, and infrastructure barriers is discussed. A review of the regulatory landscape in the United States and European Union as it applies to AI‐enabled software as a medical device is provided. Recommendations addressing cytopathology‐specific benchmarks, multi‐institutional validation, transparent governance, and incremental deployment beginning with low‐risk applications are suggested. In the current environment, LLMs have the potential to augment cytopathology practice, but responsible adoption requires rigorous validation and sustained collaboration among cytopathologists, AI researchers, and regulatory bodies.

ABSTRACT Background Alcohol‐associated hepatitis (AH) has high short‐term mortality, whereas current prognostic models mainly reflect liver and renal dysfunction. The Metabolic Vulnerability Index (MVX) is a nuclear magnetic resonance‐derived score comprising the Inflammation Vulnerability Index (IVX; small high‐density lipoprotein particles [S‐HDL‐P] and GlycA) and the Metabolic Malnutrition Index (MMX). We evaluated whether MVX, IVX, and IVX components improve 90‐day mortality prediction beyond Model for End‐Stage Liver Disease (MELD) in AH. Methods Serum samples from 196 patients with AH, 169 heavy‐drinking controls, and 351 healthy controls were analyzed by nuclear magnetic resonance spectroscopy. The primary outcome was 90‐day AH‐related mortality. Biomarkers were modeled continuously after standardization within the AH cohort; S‐HDL‐P was modeled per 1 SD decrease. Cox models were adjusted for MELD, age, sex, white blood cell count, and baseline antibiotic or corticosteroid use. Model performance was assessed using Akaike information criterion (AIC), likelihood‐ratio testing, and Harrell's C ‐statistic. Results Among patients with AH, 30 AH‐related deaths occurred within 90 days (15.3%). IVX and MVX were highest in AH cases and increased with AH severity, whereas MMX did not consistently differentiate severity. Lower S‐HDL‐P was independently associated with 90‐day mortality in fully adjusted models (HR, 1.84; 95% CI, 1.01–3.35; p = 0.045), whereas GlycA was not (HR, 0.86; 95% CI, 0.54–1.37; p = 0.527). Adding S‐HDL‐P improved fully adjusted MELD‐based model fit (AIC, 298.97 vs. 301.46; LR p = 0.034; C ‐statistic, 0.741 vs. 0.729). IVX showed supportive improvement in model fit and discrimination (AIC, 299.81; LR p = 0.056; C ‐statistic, 0.749). Continuous MELD interaction analyses supported risk separation for S‐HDL‐P (LR χ 2 = 6.20; p = 0.045) and IVX (LR χ 2 = 6.64; p = 0.036). Conclusions Lower S‐HDL‐P appears to be the principal short‐term prognostic component within the IVX domain in AH. S‐HDL‐P and IVX may refine MELD‐based 90‐day risk stratification, pending external validation.

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.

Importance Pyoderma gangrenosum (PG) is a rare neutrophilic dermatosis characterized by sterile ulcerative skin lesions. Multiple comorbidities and clinical features have been associated with PG, but no standardized guidelines exist for classifying PG phenotypes. Objective To develop an expert-established classification framework for PG phenotypes to inform treatment guidelines and future research endeavors. Evidence Review In this modified Delphi consensus study that included 23 board-certified dermatologists and Medical Dermatology Society members with expertise in PG, panelists completed 5 rounds of anonymous, iterative online surveys that were administered from December 2023 through July 2025. Before the beginning of the consensus exercise, a literature review was performed by nonvoting researchers to summarize existing data on PG clinical associations and comorbidities. A PubMed search from inception to September 2023 identified observational studies, narrative reviews, systematic reviews, and meta-analyses describing PG clinical associations and comorbidities that were published in English. Experts indicated their agreement with proposed PG phenotypes and disease modifiers with a consensus threshold of 70% or greater. Anonymous comments and aggregated results were presented in each subsequent round. In the final round, a framework of PG phenotypes and disease modifiers was proposed for agreement. Findings Twenty-three board-certified dermatologists and Medical Dermatology Society members with expertise in PG completed 5 rounds of iterative surveys. Consensus was reached on the final set of PG phenotypes and disease modifiers, with 83% of experts in agreement. PG phenotypes were overall classified into 2 major groups: PG with autoinflammatory syndromes and nonsyndromic PG. Nonsyndromic PG included 4 phenotypes: inflammatory bowel disease–associated PG, PG in association with hematologic cancers and blood dyscrasias, drug-induced PG, and other (including idiopathic) PG. Disease modifiers of PG phenotype presentations included involvement of special sites (head/neck, genitals, or peristomal skin) and extracutaneous manifestations. Conclusions and Relevance This expert-established, descriptive framework provides a standardized classification system for distinct PG phenotypes and its modifiers. This nomenclature may inform upcoming clinical guidelines and allow for consistency in reporting epidemiological research and outcomes among patients with PG.

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.