Faculty Bibliography

Large language models (LLMs) have shown increasing promise in the mental health field. LLMs are especially well suited to play a role in the labor-intensive, costly process of clinical assessment, as they can interact with a patient or participant directly to conduct a mental health assessment. We conducted a preregistered scoping review to (a) describe the unique capabilities of LLMs for clinical assessment, (b) determine the current state of the field in applying LLMs to directly assess patient/participant mental health (including screening, diagnosis, and monitoring of symptoms), and (c) highlight future research to facilitate the application of LLMs. We included work published in both Chinese and English. Only 10 studies met criteria for direct LLM-based mental health assessment. The evidence base was recent and heterogeneous: Four studies focused primarily on diagnostic interviewing or classification, five on symptom or severity assessment, and one on task-based multimodal depression assessment. Studies varied across text, voice, and multimodal formats, and depression was the dominant target. Across studies, stronger performance tended to be reported in tools that used structured interviewing logic, domain-specific adaptation, and clinically anchored reference standards. However, the evidence base remains small, with many studies employing limited validation procedures, and heavily weighted toward early-stage or nonjournal publications. The limited pace of academic validation means that, at present, LLMs are best understood as emerging assessment-support tools rather than replacements for clinical evaluation. (PsycInfo Database Record (c) 2026 APA, all rights reserved).

INTRODUCTION/BACKGROUND:Craniopharyngioma is histologically benign yet locally aggressive, with frequent recurrence. Long-term multicenter outcomes after stereotactic radiosurgery (SRS) remain incompletely defined. METHODS:We performed a retrospective multi-institutional cohort study through the International Radiosurgery Research Foundation including 296 patients from 13 centers. Median age at first SRS was 33.6 years. Median tumor volume was 1.32 cm³ and median margin dose was 12.0 Gy. The primary endpoint was local control (LC); secondary endpoints were progression-free survival (PFS) and overall survival (OS). Kaplan-Meier methods estimated outcomes, and Cox proportional hazards models evaluated predictors of LC. RESULTS:Actuarial 1-, 5-, and 10-year LC was 93.5%, 76.2%, and 70.1%. Actuarial 1-, 5-, and 10-year OS was 98.2%, 93.6%, and 85.2%, and PFS was 92.4%, 73.6%, and 64.8%. Mixed solid-cystic phenotype had worse LC than non-mixed tumors (log-rank p = 0.025); non-mixed phenotype remained independently associated with improved LC (HR 0.53, p = 0.026). Visual fields improved in 10%, were unchanged in 86%, and deteriorated in 4%; visual acuity improved in 6%, was unchanged in 91%, and worsened in 3%. Ten-year freedom from endocrine deterioration was 96.7%. Diabetes insipidus improved in 6.3%, worsened in 0.5% and other pituitary dysfunction was noted in 2.6%, CONCLUSION: In this international multi-institutional experience, SRS achieved durable long-term control with favorable survival and low incidence of visual and endocrinologic dysfunction. Mixed phenotype was an important determinant of LC. CLINICAL TRIAL NUMBER/BACKGROUND:Not applicable.

Astrocyte loss occurs in various neurological conditions and can disrupt local tissue homeostasis. While astrocytes surrounding border-forming lesions adopt reactive states without restoring astrocyte networks, how astrocytes respond to spatially confined astrocyte loss remains poorly understood. Here we used longitudinal in vivo two-photon microscopy, combined with spatiotemporal transcriptional profiling, to examine astrocyte responses following focal aquaporin-4 antibody-mediated ablation in the somatosensory cortex of adult mouse brain, a model of astrocytopathy relevant to neuromyelitis optica spectrum disorder. Here we show that perilesional astrocytes undergo pronounced structural remodeling during lesion repopulation, characterized by cell proliferation, prolonged multinucleated astrocyte states, polarized process extension into the depleted area and gradual displacement of nuclei into previously unoccupied astrocyte territories. Spatial transcriptomics reveal an injury-associated molecular response that resolves as the astrocyte network is restored. Together, our findings delineate the spatiotemporal dynamics of astrocyte regeneration after astrocyte loss, extending current understanding of astroglial plasticity in the adult brain.

OBJECTIVE:The quality of discharge processes impacts adverse outcomes post hospitalization for children with medical complexity (CMC). Perspectives of caregivers at risk for communication challenges (eg, non-English speakers, those with limited health literacy, immigrants) on understandability and feasibility of discharge instructions for CMC are understudied. Our objective was to explore their perspectives on barriers and facilitators to comprehension and implementation of discharge instructions for CMC. PATIENTS AND METHODS/METHODS:In this qualitative, descriptive study, we used maximum variation sampling to enroll caregivers of CMC (N = 40) discharged from acute or intensive care units of 2 urban hospitals. English- and Spanish-speaking caregivers of CMC (Pediatric Medical Complexity Algorithm) aged 18 years or older and discharged on 1 or more daily medications were eligible. We conducted semistructured interviews, audio-recorded and transcribed interviews, and performed content analysis. Two team members applied codes from a codebook (developed based on prior literature and preliminary analyses) and identified emerging themes. RESULTS:Equal numbers of participants spoke English and Spanish (n = 20 per language) and were recruited from each hospital (n = 20 per hospital). Half (47.5%) had low health literacy; 85% were of non-US birth. Themes emerged across 4 categories: (1) clinician and medication access; (2) patient- and family-centered approaches to discharge planning and education; (3) caregiver prior experience; and (4) resources and support systems. CONCLUSIONS:Caregivers identified barriers and facilitators affecting discharge plan comprehension and implementation. Optimizing postdischarge care for CMC is complex and requires family-centered, language-concordant, and health literacy-informed processes. Future interventions should focus on systems-level changes that enhance access and account for caregiver perspectives.

IMPORTANCE/UNASSIGNED:Black women have twice the endometrial cancer mortality of White women, and studies suggest that differences in ultrasound diagnostic accuracy partially contribute to this disparity. OBJECTIVE/UNASSIGNED:To evaluate racial and ethnic differences in the diagnostic performance of transvaginal ultrasound for detecting endometrial neoplasia. DESIGN, SETTING, AND PARTICIPANTS/UNASSIGNED:A prospective cohort study of 1833 women aged 50 years or older at risk of endometrial neoplasia, conducted at a large urban academic medical center from February 2014 to August 2022, with follow-up through March 2023. The statistical analyses were conducted from March 2024 to December 2025. INTERVENTIONS/UNASSIGNED:Women underwent endometrial assessment via transvaginal ultrasound followed by sonohysterogram-directed biopsy and follow-up observation to establish final outcome. MAIN OUTCOME AND MEASURES/UNASSIGNED:Assessment of transvaginal ultrasound diagnostic performance for detecting endometrial cancer and hyperplasia across racial and ethnic groups, measured by: (1) completion rate (endometrial accessibility) and (2) accuracy of completed scans. To assess any association with fibroids, analyses were repeated after excluding women with fibroids. Subgroup analyses were conducted among women with endometrial cancer, those with postmenopausal bleeding, and those without prior exposure to estrogen or tamoxifen. RESULTS/UNASSIGNED:A total of 1833 women met inclusion criteria (mean [SD] age, 60.3 [8.1] years). Most were postmenopausal (1218 women [87.4%]), and 832 (45.4%) were Black, 705 (38.5%) were Hispanic, and 253 (13.8%) were White. Black women had significantly lower completion rates (adequate endometrial visibility) for transvaginal ultrasound compared with White women (75.7% vs 88.9%; relative risk, 0.85; 95% CI, 0.80-0.90; P < .001). Accuracy of completed transvaginal ultrasound was slightly lower among Black women compared with White women (sensitivity, 96.3%; 95% CI, 91.3%-100% vs 100%; negative predictive value, 97.9%; 95% CI, 95.0%-100% vs 100%). After excluding women with fibroids, differences in completion and accuracy between Black and White women disappeared. Similar findings were observed in subgroup analyses of women with a final diagnosis of endometrial cancer, those presenting with postmenopausal bleeding, and those without prior exposure to estrogen or tamoxifen. CONCLUSIONS AND RELEVANCE/UNASSIGNED:In this cohort study of a diverse population of women, ultrasound completion rates were lower among Black women, primarily due to fibroids impairing endometrial visibility. Nevertheless, transvaginal sonography demonstrated a 76% completion rate and retained excellent ability to exclude cancer in Black women, regardless of fibroid presence.

Since the introduction of the Vesical Imaging-Reporting and Data System (VI-RADS), MRI has become an important imaging modality in the management of patients with bladder cancer. Its excellent diagnostic performance for determining muscle invasion in bladder cancer has been supported by numerous prospective and retrospective studies. Nevertheless, there needs to be continued improvement of the diagnostic performance of VI-RADS and sustained efforts to address remaining unmet clinical needs within the field of bladder cancer. In this paper, we highlight several such areas, some of which are actively being investigated. These include (1) whether to use intravenous contrast media or not (multiparametric vs. biparametric MRI), (2) quantitative metrics to enhance assessment of muscle invasion, (3) anatomic locations that come with pitfalls in staging, (4) introduction of bladder MRI image quality, (5) neoadjuvant chemotherapy VI-RADS (nacVI-RADS) and other considerations needed in the treatment response assessment after systemic therapies, (6) need for wider adoption, training, and implementation, and (7) application of artificial intelligence.

In response to declining public trust in healthcare institutions, the Institute for Healthcare Improvement (IHI) and the American Board of Internal Medicine Foundation (ABIMF) began a collaborative to identify changes that healthcare organizations could make to regain the trust of their care teams and communities. Using IHI and ABIMF's theory of change as a guide, six healthcare organizations tested this framework on-site and shared lessons learned, identifying the critical role of not just acknowledging past breaches but also demonstrating accountability as crucial steps to strengthening trust. The experiences of three healthcare organizations are summarized here.

Pancreatic ductal adenocarcinoma (PDAC) is a complex disease characterized by high levels of cellular heterogeneity and pronounced microenvironmental remodelling. Dynamic changes during its initiation and progression contribute to resistance to conventional therapies. Building upon key molecular catalogues established by bulk and single-cell profiling studies that have advanced our understanding of PDAC biology, recent advances in spatial biology have provided much-needed insights by elucidating regionally compartmentalized transcriptomic and proteomic programmes within the PDAC microenvironment. In parallel, emerging computational frameworks in digital pathology and artificial intelligence have advanced the field into a high-dimensional, quantitative discipline, particularly for classifying molecular and clinical features from histopathology images. Despite these advancements, integration of these two modalities remains a major challenge. Here, we summarize the convergence of molecular features identified through spatially resolved profiling in PDAC and its precursor lesions, as well as current developments in AI-powered pathology in cancer research. We further propose a multi-modal integration framework that maps molecular states onto morphological and architectural phenotypes, offering a roadmap for spatially informed patient stratification beyond descriptive tissue characterization. We posit that the path forward relies on disciplined cross-scale integration of spatial, histological, and clinical data to ensure meaningful translation into clinical practice.