Hasil untuk "Specialties of internal medicine"

Ho Cheol Kim, Sydney Guthrie, Christopher S. King et al.

Abstract Idiopathic pulmonary fibrosis (IPF) is a progressive interstitial lung disease with a highly variable clinical course. Forced vital capacity (FVC) is widely used as a marker of disease severity and progression, yet its variability and dependence on patient effort raise concerns regarding its reliability. Given these limitations, we investigated the clinical significance of slow vital capacity (SVC) as a potential alternative measure of lung function in IPF. In a retrospective cohort of 89 IPF patients who underwent pulmonary function testing with concomitant SVC measurements, we observed a strong correlation between FVC and SVC (r = 0.973 at baseline, r = 0.978 at follow-up). However, in 99% of cases, SVC values were equal to or exceeded FVC, and follow-up assessments revealed that FVC exhibited greater variability than SVC. Notably, patients with a decrease in SVC demonstrated worse survival outcomes, whereas FVC decline did not show the same prognostic significance. These findings suggest that SVC may provide a more stable and clinically meaningful measure of disease progression in IPF. Moreover, its less effort-dependent nature could improve reproducibility, particularly in patients with advanced diseases. Our study highlights the potential role of SVC as a valuable metric in clinical practice and as an endpoint in future IPF trials. Prospective validation of these findings could further establish SVC as a superior tool for disease monitoring and therapeutic assessment.

Leonie Wagner, Jan Klocke, Philipp Enghard

S. Manivannan, M.K. Mustafa, S. Ahmed et al.

Thushara Madathil, Praveen K. Neema

Sarah Grube, Sarah Latus, Martin Fischer et al.

Purpose: Comprehensive legal medicine documentation includes both an internal but also an external examination of the corpse. Typically, this documentation is conducted manually during conventional autopsy. A systematic digital documentation would be desirable, especially for the external examination of wounds, which is becoming more relevant for legal medicine analysis. For this purpose, RGB surface scanning has been introduced. While a manual full surface scan using a handheld camera is timeconsuming and operator dependent, floor or ceiling mounted robotic systems require substantial space and a dedicated room. Hence, we consider whether a mobile robotic system can be used for external documentation. Methods: We develop a mobile robotic system that enables full-body RGB-D surface scanning. Our work includes a detailed configuration space analysis to identify the environmental parameters that need to be considered to successfully perform a surface scan. We validate our findings through an experimental study in the lab and demonstrate the system's application in a legal medicine environment. Results: Our configuration space analysis shows that a good trade-off between coverage and time is reached with three robot base positions, leading to a coverage of 94.96 %. Experiments validate the effectiveness of the system in accurately capturing body surface geometry with an average surface coverage of 96.90 +- 3.16 % and 92.45 +- 1.43 % for a body phantom and actual corpses, respectively. Conclusion: This work demonstrates the potential of a mobile robotic system to automate RGB-D surface scanning in legal medicine, complementing the use of post-mortem CT scans for inner documentation. Our results indicate that the proposed system can contribute to more efficient and autonomous legal medicine documentation, reducing the need for manual intervention.

Alberto Testoni, Iacer Calixto

Reliable uncertainty quantification (UQ) is essential when employing large language models (LLMs) in high-risk domains such as clinical question answering (QA). In this work, we evaluate uncertainty estimation methods for clinical QA focusing, for the first time, on eleven clinical specialties and six question types, and across ten open-source LLMs (general-purpose, biomedical, and reasoning models), alongside representative proprietary models. We analyze score-based UQ methods, present a case study introducing a novel lightweight method based on behavioral features derived from reasoning-oriented models, and examine conformal prediction as a complementary set-based approach. Our findings reveal that uncertainty reliability is not a monolithic property, but one that depends on clinical specialty and question type due to shifts in calibration and discrimination. Our results highlight the need to select or ensemble models based on their distinct, complementary strengths and clinical use.

Roberta Di Marino, Giovanni Dioguardi, Antonio Romano et al.

Medical question answering systems face deployment challenges including hallucinations, bias, computational demands, privacy concerns, and the need for specialized expertise across diverse domains. Here, we present SOLVE-Med, a multi-agent architecture combining domain-specialized small language models for complex medical queries. The system employs a Router Agent for dynamic specialist selection, ten specialized models (1B parameters each) fine-tuned on specific medical domains, and an Orchestrator Agent that synthesizes responses. Evaluated on Italian medical forum data across ten specialties, SOLVE-Med achieves superior performance with ROUGE-1 of 0.301 and BERTScore F1 of 0.697, outperforming standalone models up to 14B parameters while enabling local deployment. Our code is publicly available on GitHub: https://github.com/PRAISELab-PicusLab/SOLVE-Med.

Shuai Zhang, Yan Liu, Fangfang Liu et al.

Abstract Background Left ventricular global longitudinal strain (GLS) holds greater diagnostic and prognostic value than left ventricular ejection fraction (LVEF) in the heart failure (HF) patients. The triglyceride-glucose (TyG) index serves as a reliable surrogate for insulin resistance (IR) and is strongly associated with several adverse cardiovascular events. However, there remains a research gap concerning the correlation between the TyG index and GLS among patients with chronic heart failure (CHF). Method 427 CHF patients were included in the final analysis. Patient demographic information, along with laboratory tests such as blood glucose, lipids profiles, and echocardiographic data were collected. The TyG index was calculated as Ln [fasting triglyceride (TG) (mg/dL) × fasting plasma glucose (FPG) (mg/dL)/2]. Results Among CHF patients, GLS was notably lower in the higher TyG index group compared to the lower TyG index group. Following adjustment for confounding factors, GLS demonstrated gradual decrease with increasing TyG index, regardless of the LVEF level and CHF classification. Conclusion Elevated TyG index may be independently associated with more severe clinical left ventricular dysfunction in patients with CHF.

Shogo Watanabe

This report presents a small language model (SLM) for Japanese clinical and medicine, named NCVC-slm-1. This 1B parameters model was trained using Japanese text classified to be of high-quality. Moreover, NCVC-slm-1 was augmented with respect to clinical and medicine content that includes the variety of diseases, drugs, and examinations. Using a carefully designed pre-processing, a specialized morphological analyzer and tokenizer, this small and light-weight model performed not only to generate text but also indicated the feasibility of understanding clinical and medicine text. In comparison to other large language models, a fine-tuning NCVC-slm-1 demonstrated the highest scores on 6 tasks of total 8 on JMED-LLM. According to this result, SLM indicated the feasibility of performing several downstream tasks in the field of clinical and medicine. Hopefully, NCVC-slm-1 will be contributed to develop and accelerate the field of clinical and medicine for a bright future.

Xiaofei Chen

This article explores the critical role of statistical analysis in precision medicine. It discusses how personalized healthcare is enhanced by statistical methods that interpret complex, multidimensional datasets, focusing on predictive modeling, machine learning algorithms, and data visualization techniques. The paper addresses challenges in data integration and interpretation, particularly with diverse data sources like electronic health records (EHRs) and genomic data. It also delves into ethical considerations such as patient privacy and data security. In addition, the paper highlights the evolution of statistical analysis in medicine, core statistical methodologies in precision medicine, and future directions in the field, emphasizing the integration of artificial intelligence (AI) and machine learning (ML).

Demi Brownlie, Andreas von Kries, Giampiero Valenzano et al.

Lung cancer is a leading cause of cancer-related death worldwide. Despite recent advances in tissue immunology, little is known about the spatial distribution of tissue-resident lymphocyte subsets in lung tumors. Using high-parameter flow cytometry, we identified an accumulation of tissue-resident lymphocytes including tissue-resident NK (trNK) cells and CD8+ tissue-resident memory T (TRM) cells toward the center of human non-small cell lung carcinomas (NSCLC). Chemokine receptor expression patterns indicated different modes of tumor-infiltration and/or residency between trNK cells and CD8+ TRM cells. In contrast to CD8+ TRM cells, trNK cells and ILCs generally expressed low levels of immune checkpoint receptors independent of location in the tumor. Additionally, granzyme expression in trNK cells and CD8+ TRM cells was highest in the tumor center, and intratumoral CD49a+CD16− NK cells were functional and responded stronger to target cell stimulation than their CD49a− counterparts, indicating functional relevance of trNK cells in lung tumors.In summary, the present spatial mapping of lymphocyte subsets in human NSCLC provides novel insights into the composition and functionality of tissue-resident immune cells, suggesting a role for trNK cells and CD8+ TRM cells in lung tumors and their potential relevance for future therapeutic approaches.

Cyril Zakka, Akash Chaurasia, Rohan Shad et al.

Large-language models have recently demonstrated impressive zero-shot capabilities in a variety of natural language tasks such as summarization, dialogue generation, and question-answering. Despite many promising applications in clinical medicine, adoption of these models in real-world settings has been largely limited by their tendency to generate incorrect and sometimes even toxic statements. In this study, we develop Almanac, a large language model framework augmented with retrieval capabilities for medical guideline and treatment recommendations. Performance on a novel dataset of clinical scenarios (n = 130) evaluated by a panel of 5 board-certified and resident physicians demonstrates significant increases in factuality (mean of 18% at p-value < 0.05) across all specialties, with improvements in completeness and safety. Our results demonstrate the potential for large language models to be effective tools in the clinical decision-making process, while also emphasizing the importance of careful testing and deployment to mitigate their shortcomings.

Cristiana Rodrigues, Luis Peralta, Paulo Ferreira

Monte Carlo (MC) techniques are currently deemed the gold standard for internal dosimetry, since the simulations can perform full radiation transport and reach a precision level not attainable by analytical methods. In this study, a custom voxelized particle source was developed for the TOPAS-MC toolkit to be used for internal dosimetry purposes. The source was designed to allow the use of clinical functional scans data to simulate events that reproduce the patient-specific tracer biodistribution. Simulation results are very promising, showing that this can be a first step towards the extension of TOPAS-MC to nuclear medicine applications. In the future more studies are needed to further ascertain the applicability and accuracy of the developed routines.

Ummara Mumtaz, Awais Ahmed, Summaya Mumtaz

We aim to present a comprehensive overview of the latest advancements in utilizing Large Language Models (LLMs) within the healthcare sector, emphasizing their transformative impact across various medical domains. LLMs have become pivotal in supporting healthcare, including physicians, healthcare providers, and patients. Our review provides insight into the applications of Large Language Models (LLMs) in healthcare, specifically focusing on diagnostic and treatment-related functionalities. We shed light on how LLMs are applied in cancer care, dermatology, dental care, neurodegenerative disorders, and mental health, highlighting their innovative contributions to medical diagnostics and patient care. Throughout our analysis, we explore the challenges and opportunities associated with integrating LLMs in healthcare, recognizing their potential across various medical specialties despite existing limitations. Additionally, we offer an overview of handling diverse data types within the medical field.

Ridhima Kapoor, Manisha Sabharwal, Suparna Ghosh-Jerath

In India, indigenous communities are nutritionally vulnerable, with indigenous women suffering the greater burden. Studies and surveys have reported poor nutritional outcomes among indigenous women in India, yet systematic documentation of community-specific nutrition data is lacking. We conducted a narrative review of 42 studies to summarise the nutritional profile of indigenous women of India, with details on their food and nutrient intakes, dietary diversity, traditional food consumption and anthropometric status. Percentage deficits were observed in intake of pulses, green leafy vegetables, fruits, vegetables, flesh foods and dairy products when compared with recommended dietary intakes for moderately active Indian women. Indices of diet quality in indigenous women were documented in limited studies, which revealed poor dietary diversity as well as low consumption of diverse traditional foods. A high risk of nutritional inadequacy was reported in all communities, especially for iron, calcium, and vitamin A. Prevalence of chronic energy deficiency was high in most communities, with dual burden of malnutrition in indigenous women of north-eastern region. Findings from this review can thus help guide future research and provide valuable insights for policymakers and program implementers on potential interventions for addressing specific nutritional issues among indigenous women of India.

Chottiwatt Jittprasong

In practically every industry today, artificial intelligence is one of the most effective ways for machines to assist humans. Since its inception, a large number of researchers throughout the globe have been pioneering the application of artificial intelligence in medicine. Although artificial intelligence may seem to be a 21st-century concept, Alan Turing pioneered the first foundation concept in the 1940s. Artificial intelligence in medicine has a huge variety of applications that researchers are continually exploring. The tremendous increase in computer and human resources has hastened progress in the 21st century, and it will continue to do so for many years to come. This review of the literature will highlight the emerging field of artificial intelligence in medicine and its current level of development.

Sudeep Pasricha

The dawn of the digital medicine era, ushered in by increasingly powerful embedded systems and Internet of Things (IoT) computing devices, is creating new therapies and biomedical solutions that promise to positively transform our quality of life. However, the digital medicine revolution also creates unforeseen and complex ethical, regulatory, and societal issues. In this article, we reflect on the ethical challenges facing digital medicine. We discuss the perils of ethical oversights in medical devices, and the role of professional codes and regulatory oversight towards the ethical design, deployment, and operation of digital medicine devices that safely and effectively meet the needs of patients. We advocate for an ensemble approach of intensive education, programmable ethical behaviors, and ethical analysis frameworks, to prevent mishaps and sustain ethical innovation, design, and lifecycle management of emerging digital medicine devices.

Xiao Gan, Zixin Shu, Xinyan Wang et al.

Traditional Chinese medicine (TCM) relies on natural medical products to treat symptoms and diseases. While clinical data have demonstrated the effectiveness of selected TCM-based treatments, the mechanistic root of how TCM herbs treat diseases remains largely unknown. More importantly, current approaches focus on single herbs or prescriptions, missing the high-level general principles of TCM. To uncover the mechanistic nature of TCM on a system level, in this work we establish a generic network medicine framework for TCM from the human protein interactome. Applying our framework reveals a network pattern between symptoms (diseases) and herbs in TCM. We first observe that genes associated with a symptom are not distributed randomly in the interactome, but cluster into localized modules; furthermore, a short network distance between two symptom modules is indicative of the symptoms' co-occurrence and similarity. Next, we show that the network proximity of a herb's targets to a symptom module is predictive of the herb's effectiveness in treating the symptom. We validate our framework with real-world hospital patient data by showing that (1) shorter network distance between symptoms of inpatients correlates with higher relative risk (co-occurrence), and (2) herb-symptom network proximity is indicative of patients' symptom recovery rate after herbal treatment. Finally, we identified novel herb-symptom pairs in which the herb's effectiveness in treating the symptom is predicted by network and confirmed in hospital data, but previously unknown to the TCM community. These predictions highlight our framework's potential in creating herb discovery or repurposing opportunities. In conclusion, network medicine offers a powerful novel platform to understand the mechanism of traditional medicine and to predict novel herbal treatment against diseases.

Carl J. Lavie, Cemal Ozemek, Leonard A. Kaminsky