Rusk Rehabilitation

Over the past five years, medical question answering (QA) has witnessed rapid advancements, driven by the evolution of large language models (LLMs), explainability techniques, and the growing emphasis on human-centered AI in healthcare. In clinical settings, where decisions directly impact patient outcomes, the need for transparency, interpretability, and trust in AI-generated answers is not merely beneficial but essential..Traditional QA systems, although reasonably accurate, often lack explainability, thus continuing to function as black boxes. This opacity raises fundamental concerns about accountability, safety in high-stakes medical domains. We introduce ExplainMed++, a novel system designed to generate human-centered medical QA summaries with intelligible explanations. Our approach enhances the interpretability and reliability of QA outputs by fine-tuning state-of-the-art LLMs using domain-specific datasets such as MedQA. This design is influenced by cognitive models of physician decision-making and recent progress in self-rationale LLMs. This literature increasingly highlights the limitations of accuracy-centric but also provides evidence-based and clinically aligned explanations.We incorporate an explanation module that leverages retrieval-augmented generation and reflective reasoning to align model outputs with clinical reasoning patterns. This literature review synthesizes findings from 70 influential studies published between 2020 and 2025,spanning foundational work in biomedical NLP, explainable AI(XAI), highlighting key technological advances, unresolved challenges, and promising future directions in explainable medical QA. These works collectively illuminate both the promise and limitations of current methodologies, including prompt-based reasoning and counterfactual generation. ExplainMed++ contributes to this landscape by bridging the gap between model performance and clinical usability and also, advancing both the performance and the trust-worthiness of QA systems in healthcare.

Fiberoptic Endoscopic Evaluation of Swallowing (FEES) is a procedure utilized by speech language pathologists to evaluate swallowing function in infants and children. FEES has been found to be a valid and reliable procedure for the assessment of pediatric dysphagia. It is the only option for instrumental examination of swallowing in breastfeeding infants. This article describes the differences between the more common videofluoroscopic swallow study (VFSS) and FEES, as well as management of interprofessional collaboration.

Antimicrobial resistance (AMR) is one of the most urgent challenges in modern microbiology, both an evolutionary inevitability and a global health crisis shaped by clinical practices, ecological disruption, and social inequities. Generative artificial intelligence (AI) and large language models (LLMs) present new opportunities to anticipate resistance pathways, design novel antimicrobial agents, and guide interventions that are informed by evolutionary dynamics. Their successful integration, however, depends on addressing three fundamental imperatives. The first is evolutionary robustness, requiring models that incorporate mutation, horizontal gene transfer, and adaptive landscapes to move beyond retrospective classification toward predictive evolutionary inference. The second is explainability and biosafety, which demand interpretable and biologically credible outputs that clinicians, microbiologists, and policymakers can trust, while safeguarding against dual use risks. The third is data equity, which calls for strategies that mitigate structural biases in global microbial datasets and ensure that predictive systems serve the populations most affected by AMR. This Perspective advances the view that generative AI must be conceived as a transformative epistemic infrastructure that is evolution aware, transparent, and globally inclusive, capable of supporting sustainable drug discovery, adaptive surveillance, and equitable microbiological futures.

For many people with long COVID (LC), new-onset pain is a debilitating consequence. This study examined the nature of new-onset pain and concomitant symptoms in patients with LC to infer mechanisms of pain from the relationships between pain and health-related factors. Pain and other symptoms were evaluated in 153 individuals with LC using the Self-Administered Leeds Assessment of Neuropathic Symptoms and Signs, EuroQoL Visual Analog Scale, and Quality of Life in Neurological Disorders. The relationships between pain and patient factors were analyzed using Chi Square and independent t-tests. 20.3% of individuals who reported new-onset pain had neuropathic pain, which was associated with lower quality of life and higher rates of cognitive dysfunction compared to those with non-neuropathic pain. Other symptoms were similar between groups, however heart-related symptoms were more prevalent in individuals with neuropathic pain and mood swings were more prevalent for individuals with non-neuropathic pain. Characterizing the relationships between NP and quality of life in individuals with LC can aid in developing better clinical management strategies. Understanding the associations between NP and cognitive dysfunction provides the imperative foundation for future studies further examining the pathophysiological mechanisms underlying pain development in LC.

We provide a theory for atomic resolution phonon spectroscopy in scanning transmission electron microscopy. This formulation goes beyond a recently proposed simple approach to atomic-scale phonon spectroscopy by explicitly including the dependence on probe position in the inelastic scattering cross section itself and considers the contribution to the spectrum from individual atoms. An application is made to existing experimental data that demonstrates how a direct comparison of the data with phonon densities of states projected onto individual atoms can be made and also the importance of contributions to the energy-loss spectrum on a particular atomic site from surrounding atoms due to probe spreading.