Citations

The spinal cord plays a central role in the pathophysiology and clinical manifestations of multiple sclerosis (MS), yet remains under-studied compared with the brain. This review summarizes key insights from the 2025 North American Imaging in MS Spinal Cord Imaging Workshop, highlighting recent advances, ongoing challenges, and future opportunities in MS spinal cord imaging. We review pathological studies and outline the clinical relevance of spinal cord lesions and atrophy for diagnosis, prognosis, and disease monitoring, highlighting emerging biomarkers of progression independent of relapse activity. Correlations between magnetic resonance imaging, histopathology, and clinical outcomes support the validation and translational potential of advanced spinal cord imaging techniques. Finally, we discuss spinal cord–specific processing pipelines and reproducibility challenges. Collectively, these insights underscore the need to integrate advanced and quantitative spinal cord imaging into clinical trials, research studies, and—when feasible—clinical care, to fully capture the extent of MS pathology, and ultimately improve patient outcomes.

Background: Liver transplantation (LT) remains the definitive treatment for end-stage liver disease; however, post-LT outcomes may differ depending on underlying disease etiology. Our study aimed to compare post-LT outcomes between alcoholic and non-alcoholic steatohepatitis (NASH)-related liver transplant recipients in the United States. Methods: A retrospective cohort study was conducted using the TriNetX Research Network. Adult liver transplant recipients (≥18 years) were categorized into two mutually exclusive cohorts: alcoholic liver disease and NASH-related liver disease. Propensity score matching (1:1) was performed to balance baseline characteristics. Clinical outcomes were assessed. Comparative analyses included risk ratios, risk differences, odds ratios, Kaplan–Meier survival analysis, and hazard ratios with log-rank testing. Results: Compared with NASH recipients, alcoholic LT recipients had a significantly higher risk of rejection (14.5% vs. 12.1%; RR 1.195, 95% CI 1.076–1.327; p = 0.001) and hepatic encephalopathy (14.3% vs. 8.3%; RR 1.720, 95% CI 1.526–1.938; p < 0.001). Acute kidney injury was also more frequent in the alcoholic cohort (47.3% vs. 43.6%; RR 1.084, 95% CI 1.036–1.133; p < 0.001). In contrast, sepsis (15.6% vs. 18.0%; RR 0.869, 95% CI 0.794–0.952; p = 0.003) and CKD (46.1% vs. 51.1%; RR 0.901, 95% CI 0.863–0.939; p < 0.001) occurred less frequently in alcoholic LT patients. No significant differences were observed for liver transplant failure or ascites. Kaplan–Meier analyses demonstrated significantly lower rejection-free survival (HR 1.241, p < 0.001), higher hepatic encephalopathy (HR 1.808, p < 0.001), increased AKI risk (HR 1.150, p < 0.001) and higher all-cause mortality in the alcoholic cohort (HR 1.106, p = 0.031). Conclusions: In this large real-world matched cohort study, alcoholic LT recipients demonstrated higher risks of complications and all-cause mortality compared with NASH LT recipients.

Background/Objectives: Speech perception is measured in quiet and in the presence of background noise, such as multi-talker babble (MTB), to establish cochlear implant (CI) candidacy and measure post-operative outcomes. Testing protocols often use interrupted noise that begins just before and ends shortly after the target sentence, providing insufficient time for the noise-reduction algorithms to activate in the hearing device and provide an improved signal-to-noise ratio (SNR). Furthermore, interrupted noise does not allow the hearing-impaired user to acclimatize to the abrupt onset of noise or to focus on the target sentence, a challenge related to auditory stream segregation. The present study compares speech perception in bimodal CI users across interrupted and continuous MTB noise conditions to highlight performance differences between these two distinct listening environments. Methods: Speech perception was evaluated for two different bimodal CI groups. Group 1: This group evaluated an automatic hearing aid program across three environments: quiet, continuous MTB, and interrupted MTB. Group 2: This group evaluated two manual hearing aid programs across the same three environments (quiet, continuous, and interrupted MTB). Results: For Group 1, scores in quiet reached 88%, significantly declined in interrupted MTB (38%), but improved in continuous MTB (67%). For Group 2, no significant mean differences were found between interrupted and continuous noise across the two manual programs, though individual variations persisted. Conclusions: Based on the findings of this study, continuous noise rather than interrupted noise should be utilized for establishing CI candidacy and evaluating post-implantation speech perception.

Background: Longitudinal assessments are increasingly common as a component of continuing certification programs offered by medical specialty boards. The goal of this study was to understand anesthesiologists’ utilization pattern, performance, and perception of the longitudinal assessment program administered by the American Board of Anesthesiology (Raleigh, North Carolina). Methods: The authors analyzed assessment usage and survey data from all anesthesiologists who participated in the Maintenance of Certification in Anesthesiology (MOCA) Minute from January 2018 to December 2019. Results: During the 24-month study period, 25,404 anesthesiologists completed all 120 questions assigned in at least 1 calendar year, collectively 5,801,160 responses to 1,646 unique questions. Participants were more likely to complete questions at the end of each quarter, with the highest frequency at the end of the year. Seventy-five percent of responses were first-time attempts, and 17% were first repeats. The median interval between original question presentation and first repeat was 314 days. The mean ± SD percent correct score was 68.3 ± 8.0% for first-time questions and 75.5 ± 12.6% for first repeats. The mean ± SD time to answer a first-time question was 26.8 ± 13.1 s, with shorter times for questions with correct than incorrect responses (25.0 ± 12.7 vs . 30.7 ± 13.3 s; time difference, −5.69 s; 95% CI, −5.71 to −5.66; P < 0.001). Of 5.11 million responses with ratings of relevance (88.2% of all responses analyzed), 4.86 million (95.2%) were felt to be somewhat or very relevant to practice. The percent correct scores were higher for responses with higher relevance and confidence ratings. For responses rated “not relevant,” “somewhat relevant,” and “very relevant,” the percent correct was 58.5%, 67.6%, and 77.0%, respectively; for responses rated as “unsure,” “somewhat confident,” and “very confident,” the percent correct was 53.6%, 67.9%, and 84.4%, respectively. Conclusions: MOCA Minute was generally used as designed. Evidence of learning was suggested by improved performance on repeat questions.

Purpose of review Ophthalmology has emerged as the proving ground for medical artificial intelligence (AI) because of its imaging-centric workflows, standardized data acquisition, and well defined clinical endpoints. Recently, the field has started transitioning from narrowly trained, task-specific algorithms toward multimodal models that are trained on large datasets and can be adapted to many tasks. These models may integrate information from images, text, and clinical data and are increasingly trialed in real-world settings. This review examines recent advances and ongoing controversies in the use of AI as a basic science, translational, and clinical research tool in ophthalmology. Recent findings Recent literature highlights the growing role of self-supervised and foundation models for scalable phenotyping, biomarker discovery, and cross-task generalization, particularly in retinal imaging and optical coherence tomography (OCT). In parallel, large-scale clinical deployments, including cloud-based OCT platforms and autonomous diabetic retinopathy screening, demonstrate clinical impact while revealing persistent challenges related to workflow integration and patient generalizability. Regulatory and ethics-focused studies underscore increasing attention to validation rigor, bias, and postdeployment monitoring, exposing heterogeneity in research standards and physician practices. Summary Ophthalmic AI is maturing toward real-world clinical implementation. Future progress will depend less on performance gains alone and more on clinical alignment, rigorous validation, equity-aware evaluation, and postdeployment surveillance, alongside continued investment in basic science to advance disease understanding and inform robust, generalizable model development.

Although complications of autologous bone flap cranioplasty are well described, data on long-term bone flap survival and the comorbidities associated with secondary reconstruction are limited. This study aimed to quantify the need for secondary alloplastic cranioplasty within a standardized patient cohort. Patients who underwent delayed cranioplasty following craniectomy with banked, frozen autologous bone between 2012 and 2025 were identified and analyzed using Cox regression, spline analysis, and Kaplan–Meier survival analysis. A total of 367 patients with an average age of 39.2±19.7 years (range: 0.3–84.4 years) at the time of cranioplasty were included. A secondary cranioplasty was required in 13.6% of patients (n=50 of 367), with resorption (52%) and infection (42%) as the most common indications. In adjusted analyses, patients who underwent cranioplasty following a traumatic injury (HR: 2.37; 95% CI: 1.07–5.26; P =0.03) and patients younger than 18 years (HR: 2.99; 95% CI: 1.35–6.62; P =0.01) were more likely to require a secondary cranioplasty due to bone flap resorption. Increasing defect size was also associated with a higher risk of secondary cranioplasty due to bone flap resorption, becoming significant at roughly 95 cm² (HR: 2.54; 95% CI: 1.04–6.20; P =0.04). The estimated cumulative incidence of secondary cranioplasty was 37% at ten years after autologous reconstruction. These results indicate that although autologous bone flap cranioplasty is largely effective, select patients face a substantially higher risk of secondary cranioplasty, and alternative reconstructive options may be warranted.

Objectives: Identify the frequency of abnormal neurological examinations among patients presenting in person for headache consultation. Determine what proportion of patients with abnormal examinations have accompanying red flags in their histories and/or undergo diagnostic workup. Methods: We conducted a retrospective review of patients presenting in person for headache consultation at a tertiary/quaternary headache clinic between June 2022 and May 2023. We excluded examination abnormalities consistent with known comorbidity, functional neurological disorder, and/or non-headache disease. We then calculated the proportion of patients with an abnormal neurological examination. For patients with abnormal examinations, the electronic medical record was further reviewed for red-flag components in the history, diagnostic workup ordered, and the resulting diagnosis. Results: Of 1281 patients presenting for in-person headache consultation, 218 (17%) had abnormal neurological examinations that were not consistent with known comorbidity, functional neurological disorder, or non-headache disease. Of these, 203 (93%) had a red flag in their history. The remaining 15 patients with abnormal examinations and no historical red flags comprised 1.2% of all 1281 patients. Regarding workup, 160 (73%) had neuro-imaging and 61 (28%) had laboratory studies ordered and/or requested for review, of which 13% and 8% were concerning for pathology, respectively. Conclusions: A small but notable proportion of patients presenting for in-person headache consultation had abnormalities on neurological examination. The majority of these patients received imaging, with a diagnostic yield higher than previously reported. However, the vast majority of patients with abnormal examinations also had red flags in their history to prompt diagnostic workup.

Abstract Background Preparing tumor board patient summaries is time intensive. Large-language-model based systems may automate summarization but require real-world evaluation prior to clinical use. We performed an exploratory retrospective evaluation of the Microsoft Healthcare Agent Orchestrator (HAO), deployed in a Mayo Clinic–controlled staged environment, to generate tumor board–style patient summaries from retrospective Electronic Health Record (EHR) notes. Methods HAO generated summaries for breast, hepatobiliary, and neuro-oncology tumor board cases using up to the most recent 1,000 clinical notes. Clinician reviewers evaluated outputs via REDCap surveys across perceived factuality, completeness, clarity/conciseness, temporal cohesion, comparative performance, safety, and clinical utility (0–4 Likert scale). Reviewers were permitted to query the HAO chat interface to address missing details. Automated factuality was assessed using TBFact (bidirectional entailment), reporting precision and recall against available reference summaries. Results Among 57 survey responses from 5 different physicians, mean scores exceeded 2.8 across domains, with medians of 3 for most axes. In an exploratory comparison, oncology fellows required less time to review HAO-generated summaries than to manually generate patient summaries (mean difference 13.57 minutes per patient, p<0.001), although this difference may be influenced by prior familiarity with the same cases; 96% of survey responses indicated that HAO would save time. TBFact evaluations showed higher recall than precision across domains, consistent with broad capture of reference content alongside additional content that was not present in gold-standard summaries. Attribution was viewed favorably but showed issues with primary-source specificity and link reliability. Conclusions In a controlled Mayo environment, HAO demonstrated moderate performance and was associated with reduced review time for tumor board preparation. These findings are promising but preliminary and do not establish clinical safety, noninferiority to manual review, or readiness for routine clinical use. Limitations, including verbosity, specialty-specific content gaps, and inconsistent attribution, highlight the need for iterative refinement and further evaluation. Graphical Abstract Created using ChatGPT

Abstract Background Sepsis is a life-threatening condition in which delayed recognition and treatment are associated with increased mortality. While predictive models such as Epic’s Early Detection of Sepsis Model (ESM) were developed to support early intervention, their real-world impact after integration into clinical workflows remains difficult to evaluate. Objectives To evaluate the real-world impact of ESM integrated into clinical workflow on clinical outcomes, antibiotic use, and harm-benefit tradeoffs. Methods We conducted a quasi-experimental study in a single healthcare system using encounter-level data from inpatient settings. Inpatient mortality, prolonged hospitalization, antibiotic use, and sepsis prevalence were compared between the pre-implementation period (3 June 2023 to 20 August 2024) and the online period (21 August 2024 to 26 December 2024) when the model became visible to clinicians. We also applied a counterfactual framework using models trained on pre-implementation data to estimate expected outcomes without ESM and to quantify harms related to overtreatment and delayed treatment. Results Among 101,138 encounters, 86,884 occurred during the pre-implementation period and 14,254 during the online period. In unadjusted analyses, the online period had lower inpatient mortality, prolonged hospitalization, antibiotic use, and sepsis prevalence (all p≤0.002). In the counterfactual analyses, observed outcomes were lower than expected without ESM for mortality (1.21% vs 1.82%; p<0.001), prolonged hospitalization (5.56% vs 7.95%; p<0.001), and antibiotic use (43.52% vs 47.04%; p<0.001). False positive harm (37.72% vs 41.68%; p<0.001) was also lower than expected. Conclusions Integration of ESM into clinical workflow was associated with improved patient outcomes, reduced antibiotic use, and decreased harm from overtreatment, without evidence of increased harm from delayed treatment, supporting a positive net clinical benefit and the safety and effectiveness of ESM under Software as a Medical Device principles.

This cross-sectional study evaluates the performance of the US Food and Drug Administration–cleared phosphorylated tau 217 (p-tau217) to β-amyloid 42 ratio cut points used to detect amyloid pathology associated with Alzheimer disease.