Hasil untuk "Medicine (General)"

Harsha Nori, Nicholas King, S. McKinney et al.

Large language models (LLMs) have demonstrated remarkable capabilities in natural language understanding and generation across various domains, including medicine. We present a comprehensive evaluation of GPT-4, a state-of-the-art LLM, on medical competency examinations and benchmark datasets. GPT-4 is a general-purpose model that is not specialized for medical problems through training or engineered to solve clinical tasks. Our analysis covers two sets of official practice materials for the USMLE, a three-step examination program used to assess clinical competency and grant licensure in the United States. We also evaluate performance on the MultiMedQA suite of benchmark datasets. Beyond measuring model performance, experiments were conducted to investigate the influence of test questions containing both text and images on model performance, probe for memorization of content during training, and study probability calibration, which is of critical importance in high-stakes applications like medicine. Our results show that GPT-4, without any specialized prompt crafting, exceeds the passing score on USMLE by over 20 points and outperforms earlier general-purpose models (GPT-3.5) as well as models specifically fine-tuned on medical knowledge (Med-PaLM, a prompt-tuned version of Flan-PaLM 540B). In addition, GPT-4 is significantly better calibrated than GPT-3.5, demonstrating a much-improved ability to predict the likelihood that its answers are correct. We also explore the behavior of the model qualitatively through a case study that shows the ability of GPT-4 to explain medical reasoning, personalize explanations to students, and interactively craft new counterfactual scenarios around a medical case. Implications of the findings are discussed for potential uses of GPT-4 in medical education, assessment, and clinical practice, with appropriate attention to challenges of accuracy and safety.

Julie Holihan, MD, MS, Devi Bavishi, MD, Kush Brahmbhatt, BS et al.

Sebastian Joseph, Lily Chen, Barry Wei et al.

Technological progress has led to concrete advancements in tasks that were regarded as challenging, such as automatic fact-checking. Interest in adopting these systems for public health and medicine has grown due to the high-stakes nature of medical decisions and challenges in critically appraising a vast and diverse medical literature. Evidence-based medicine connects to every individual, and yet the nature of it is highly technical, rendering the medical literacy of majority users inadequate to sufficiently navigate the domain. Such problems with medical communication ripens the ground for end-to-end fact-checking agents: check a claim against current medical literature and return with an evidence-backed verdict. And yet, such systems remain largely unused. In this position paper, developed with expert input, we present the first study examining how clinical experts verify real claims from social media by synthesizing medical evidence. In searching for this upper-bound, we reveal fundamental challenges in end-to-end fact-checking when applied to medicine: Difficulties connecting claims in the wild to scientific evidence in the form of clinical trials; ambiguities in underspecified claims mixed with mismatched intentions; and inherently subjective veracity labels. We argue that fact-checking should be approached and evaluated as an interactive communication problem, rather than an end-to-end process.

YunHe Su, Zhengyang Lu, Junhui Liu et al.

This paper explores the advancements and applications of large-scale models in the medical field, with a particular focus on Medical Large Models (MedLMs). These models, encompassing Large Language Models (LLMs), Vision Models, 3D Large Models, and Multimodal Models, are revolutionizing healthcare by enhancing disease prediction, diagnostic assistance, personalized treatment planning, and drug discovery. The integration of graph neural networks in medical knowledge graphs and drug discovery highlights the potential of Large Graph Models (LGMs) in understanding complex biomedical relationships. The study also emphasizes the transformative role of Vision-Language Models (VLMs) and 3D Large Models in medical image analysis, anatomical modeling, and prosthetic design. Despite the challenges, these technologies are setting new benchmarks in medical innovation, improving diagnostic accuracy, and paving the way for personalized healthcare solutions. This paper aims to provide a comprehensive overview of the current state and future directions of large models in medicine, underscoring their significance in advancing global health.

Markus Bertl, Alan Mott, Salvatore Sinno et al.

The digitization of healthcare presents numerous challenges, including the complexity of biological systems, vast data generation, and the need for personalized treatment plans. Traditional computational methods often fall short, leading to delayed and sometimes ineffective diagnoses and treatments. Quantum Computing (QC) and Quantum Machine Learning (QML) offer transformative advancements with the potential to revolutionize medicine. This paper summarizes areas where QC promises unprecedented computational power, enabling faster, more accurate diagnostics, personalized treatments, and enhanced drug discovery processes. However, integrating quantum technologies into precision medicine also presents challenges, including errors in algorithms and high costs. We show that mathematically-based techniques for specifying, developing, and verifying software (formal methods) can enhance the reliability and correctness of QC. By providing a rigorous mathematical framework, formal methods help to specify, develop, and verify systems with high precision. In genomic data analysis, formal specification languages can precisely (1) define the behavior and properties of quantum algorithms designed to identify genetic markers associated with diseases. Model checking tools can systematically explore all possible states of the algorithm to (2) ensure it behaves correctly under all conditions, while theorem proving techniques provide mathematical (3) proof that the algorithm meets its specified properties, ensuring accuracy and reliability. Additionally, formal optimization techniques can (4) enhance the efficiency and performance of quantum algorithms by reducing resource usage, such as the number of qubits and gate operations. Therefore, we posit that formal methods can significantly contribute to enabling QC to realize its full potential as a game changer in precision medicine.

Faraz Farhadi, Shadi A. Esfahani, Fereshteh Yousefirizi et al.

The field of Clinical-Computational Nuclear Medicine is rapidly advancing, fueled by AI, tracer kinetic modeling, radiomics, and integrated informatics. These technologies improve imaging quality, automate lesion detection, and enable personalized radiopharmaceutical therapy through physiologically based pharmacokinetic (PBPK) modeling and voxel-level dosimetry. Workflow automation and Natural Language Processing (NLP) further enhance operational efficiency. However, successful implementation and adoption of these tools require clinical oversight to ensure accuracy, interpretability, and patient safety. This paper highlights key computational innovations and emphasizes the critical role of clinician-guided evaluation in shaping the future of precision imaging and therapy.

Bianca Forrester, Georgia Fisher, Louise A. Ellis et al.

Abstract Introduction The COVID‐19 pandemic challenged primary care to rapidly innovate. In response, the Western Victorian Primary Health Network (WVPHN) developed a COVID‐19 online Community of Practice comprising general practitioners (GPs), practice nurses, pharmacists, aged care and disability workers, health administrators, public health experts, medical specialists, and consumers. This Experience Report describes our progress toward a durable organizational learning health system (LHS) model through the COVID‐19 pandemic crisis and beyond. Methods In March 2020, we commenced weekly Community of Practice sessions, adopting the Project ECHO (Extension of Community Health Outcomes) model for a virtual information‐sharing network that aims to bring clinicians together to develop collective knowledge. Our work was underpinned by the LHS framework proposed by Menear et al. and aligned with Kotter's eight‐step change model. Results There were four key phases in the development of our LHS: build a Community of Practice; facilitate iterative change; develop supportive organizational infrastructure; and establish a sustainable, ongoing LHS. In total, the Community of Practice supported 83 unique COVID‐19 ECHO sessions involving 3192 h of clinician participation and over 10 000 h of organizational commitment. Six larger sessions were run between March 2020 and September 2022 with 3192 attendances. New models of care and care pathways were codeveloped in sessions and network leaders contributed to the development of guidelines and policy advice. These innovations enabled WVPHN to lead the Australian state of Victoria on rates of COVID vaccine uptake and GP antiviral prescribing. Conclusion The COVID‐19 pandemic created a sense of urgency that helped stimulate a regional primary care‐based Community of Practice and LHS. A robust theoretical framework and established change management theory supported the purposeful implementation of our LHS. Reflection on challenges and successes may provide insights to support the implementation of LHS models in other primary care settings.

Vinod Sharma, Saikat Deb, Yogesh Mahajan et al.

Purpose This study investigates the psychological impact of Artificial Intelligence (AI)-driven job displacement among Indian IT professionals. It specifically explores how individuals psychologically experience the loss of roles due to automation, and how these experiences influence their emotional, cognitive, and behavioural well-being. Method A qualitative phenomenological approach was used to capture the lived experiences of 24 IT professionals who faced AI-induced job loss or reassignment. Data were collected via in-depth semi-structured interviews and analysed through thematic analysis. To ensure rigour and theoretical saturation, a three-round Delphi process involving 20 domain experts—spanning clinical psychology, organizational behaviour, and AI policy—was used to validate and refine the emergent themes. Results Six core psychological themes were identified: emotional shock, erosion of professional identity, chronic anxiety and anticipatory rumination, social withdrawal, adaptive and maladaptive coping strategies, and perceived organizational betrayal. These themes reflect a multilayered resource loss, including identity, control, employability, and social belonging. Conclusion AI-driven role redundancy in the Indian IT sector is more than a labour market shift a deep psychological disruption. This study underscores the urgent need for organizations, mental health practitioners, and policymakers to develop anticipatory and compassionate interventions that can buffer the mental health consequences of technological transformation.

Peipei Yang, Xiaoshuai Li, Yuqing Huang et al.

Abstract Objectives To explore the associations of carotid artery hemodynamic parameters with cerebral perfusion and cerebral small vessel disease (CSVD) in a healthy population. Methods A total of 654 participants from the Kailuan community were included after those with incomplete data or carotid stenosis ≥ 50% were excluded. Carotid ultrasound was used to measure the peak carotid flow velocity (PSV), end-diastolic velocity (EDV), resistance index (RI), pulsatility index (PI) and carotid intima–media thickness (IMT) of the common carotid artery (CCA) and internal carotid artery (ICA). MRI was performed to assess cerebral perfusion and CSVD features, including white matter hyperintensities (WMH), lacunes (LA), cerebral microbleeds (CMB), and enlarged perivascular spaces (EPVS). Multivariate regression models were used to analyze the associations of carotid hemodynamics with cerebral perfusion and CSVD. Results CCA–EDV was positively associated with whole-brain perfusion (β = 0.14, p = 0.017) and regional perfusion in gray matter (GM) (β = 0.16, p = 0.012), frontal (β = 0.15, p = 0.014), parietal (β = 0.19, p = 0.006), temporal (β = 0.14, p = 0.020), and hippocampus (β = 0.12, p = 0.034), but no significant associations were found between CCA–EDV and white matter (WM) perfusion. A higher CCA–EDV was associated with a lower risk of LA (OR = 0.93, p = 0.029). The CCA–PSV was negatively correlated with the total CSVD score (OR = 0.99, p = 0.047). No significant associations were detected between other carotid hemodynamic parameters and LA, CMB, EPVS, WMH, or total CSVD score. Conclusions Carotid artery hemodynamic parameters, especially the CCA–EDV, are closely related to cerebral perfusion and the development of CSVD. Reductions in total and regional cerebral perfusion, along with an increased LA burden, were associated with decreased CCA–EDV.

Cong Shen, Meina Wu, Minxuan Su et al.

Abstract Background The emergence of plasmid-mediated colistin resistance, primarily driven by the mcr-1 gene, represents a major global health threat. IncI2 plasmids, one of the leading carriers of mcr-1, have been frequently recovered from clinical and agricultural settings. However, their persistence in the absence of antibiotic pressure and adaptive responses to colistin exposure remain poorly understood. Methods We conducted 60-day laboratory evolution experiments using Escherichia coli C600 carrying the mcr-1-harboring IncI2 plasmid pBD110 under three colistin concentrations (0, 2, and 4 mg/L). Stability was evaluated using polymerase chain reaction (PCR). Bacterial fitness was assessed using growth curve analysis and competition assays. Antimicrobial susceptibility was determined by the broth microdilution method. Conjugation potential was examined using conjugation experiments. Genomic alterations were investigated using whole-genome sequencing combined with bioinformatic analysis. Results pBD110 was stably maintained for 120 passages under all conditions, with no significant loss observed in the absence of colistin. Under strong selection (4 mg/L), plasmid abundance increased, whereas moderate pressure (2 mg/L) led to fitness costs and reduced plasmid copy number. Whole-genome sequencing revealed distinct adaptive strategies: plasmids under non-selective conditions accumulated mutations in conjugation-related genes, enhancing transfer frequency, whereas those under colistin exposure retained structural stability but acquired shufflon inversions that impaired conjugation. Host genomes accumulated numerous chromosomal mutations, particularly in metabolic and stress response pathways, to compensate for resistance-associated burdens. Conclusions IncI2 plasmids exhibit dual evolutionary strategies. In the absence of colistin, they optimized horizontal transfer, whereas under selective pressure, they prioritized the stability and vertical inheritance of mcr-1. These findings provide new insights into the persistence and dissemination of colistin resistance and highlight evolutionary trade-offs that shape plasmid-host coadaptation.

Marko Škrabić, Marija Majer, Zdravko Siketić et al.

Thin amorphous oxide films (a-SiO₂, a-Al₂O₃, a-MgO) were prepared by magnetron sputtering deposition. Their response to high-energy heavy ion beams (23 MeV I, 18 MeV Cu, 2.5 MeV Cu) and gamma-ray (1.25 MeV) irradiation was studied by elastic recoil detection analysis and infrared spectroscopy. It was established that their high radiation hardness is due to a high level of disorder, already present in as-prepared samples, so the high-energy heavy ion irradiation cannot change their structure much. In the case of a-SiO₂, this resulted in a completely different response to high-energy heavy ion irradiation found previously in thermally grown a-SiO₂. In the case of a-MgO, only gamma-ray irradiation was found to induce significant changes.

Ehsan Abadi, Aldo Badano, Predrag Bakic et al.

This submission comprises the proceedings of the 1st Virtual Imaging Trials in Medicine conference, organized by Duke University on April 22-24, 2024. The listed authors serve as the program directors for this conference. The VITM conference is a pioneering summit uniting experts from academia, industry and government in the fields of medical imaging and therapy to explore the transformative potential of in silico virtual trials and digital twins in revolutionizing healthcare. The proceedings are categorized by the respective days of the conference: Monday presentations, Tuesday presentations, Wednesday presentations, followed by the abstracts for the posters presented on Monday and Tuesday.

Manlio De Domenico, Luca Allegri, Guido Caldarelli et al.

The adoption of digital twins (DTs) in precision medicine is increasingly viable, propelled by extensive data collection and advancements in artificial intelligence (AI), alongside traditional biomedical methodologies. However, the reliance on black-box predictive models, which utilize large datasets, presents limitations that could impede the broader application of DTs in clinical settings. We argue that hypothesis-driven generative models, particularly multiscale modeling, are essential for boosting the clinical accuracy and relevance of DTs, thereby making a significant impact on healthcare innovation. This paper explores the transformative potential of DTs in healthcare, emphasizing their capability to simulate complex, interdependent biological processes across multiple scales. By integrating generative models with extensive datasets, we propose a scenario-based modeling approach that enables the exploration of diverse therapeutic strategies, thus supporting dynamic clinical decision-making. This method not only leverages advancements in data science and big data for improving disease treatment and prevention but also incorporates insights from complex systems and network science, quantitative biology, and digital medicine, promising substantial advancements in patient care.

Katharina Schindler, Katharina Eva Ruppel, Claudia Müller et al.

Chimeric antigen receptor (CAR) T cell therapies have demonstrated significant successes in treating cancer. Currently, there are six approved CAR T cell products available on the market that target different malignancies of the B cell lineage. However, to overcome the limitations of CAR T cell therapies, other immune cells are being investigated for CAR-based cell therapies. CAR natural killer (NK) cells can be applied as allogeneic cell therapy, providing an economical, safe, and efficient alternative to autologous CAR T cells. To improve CAR research and future in-patient monitoring of cell therapeutics, a simple, reliable, and versatile CAR detection reagent is crucial. As most existing CARs contain a single-chain variable fragment (scFv) with either a Whitlow or a G4S linker site, linker-specific monoclonal antibodies (mAbs) can detect a broad range of CARs. This study demonstrates that these linker-specific mAbs can detect different CAR NK cells in vitro, spiked in whole blood, and within patient-derived tumor spheroids with high specificity and sensitivity, providing an effective and almost universal alternative for scFv-based CAR detection. Additionally, we confirm that linker-specific antibodies can be used for functional testing and enrichment of CAR NK cells, thereby providing a useful research tool to fast-track the development of novel CAR-based therapies.

Joshua Durso-Finley, Jean-Pierre Falet, Raghav Mehta et al.

Image-based precision medicine aims to personalize treatment decisions based on an individual's unique imaging features so as to improve their clinical outcome. Machine learning frameworks that integrate uncertainty estimation as part of their treatment recommendations would be safer and more reliable. However, little work has been done in adapting uncertainty estimation techniques and validation metrics for precision medicine. In this paper, we use Bayesian deep learning for estimating the posterior distribution over factual and counterfactual outcomes on several treatments. This allows for estimating the uncertainty for each treatment option and for the individual treatment effects (ITE) between any two treatments. We train and evaluate this model to predict future new and enlarging T2 lesion counts on a large, multi-center dataset of MR brain images of patients with multiple sclerosis, exposed to several treatments during randomized controlled trials. We evaluate the correlation of the uncertainty estimate with the factual error, and, given the lack of ground truth counterfactual outcomes, demonstrate how uncertainty for the ITE prediction relates to bounds on the ITE error. Lastly, we demonstrate how knowledge of uncertainty could modify clinical decision-making to improve individual patient and clinical trial outcomes.

Akiko Kurachi, Yusuke Ishida, Koichi Nakazawa et al.

Abstract Streptococcal toxic shock syndrome (STSS) has a high mortality rate, and most patients die within a few days of onset. We report an elderly patient with STSS, necrotizing fasciitis and septic shock caused by group G streptococcus who was successfully treated with multidisciplinary therapy.

G. Ortega Suero, M.J. Abenza Abildúa, C. Serrano Munuera et al.

Introduction: Ataxia and hereditary spastic paraplegia are rare neurodegenerative syndromes. We aimed to determine the prevalence of these disorders in Spain in 2019. Patients and methods: We conducted a cross-sectional, multicentre, retrospective, descriptive study of patients with ataxia and hereditary spastic paraplegia in Spain between March 2018 and December 2019. Results: We gathered data from a total of 1933 patients from 11 autonomous communities, provided by 47 neurologists or geneticists. Mean (SD) age in our sample was 53.64 (20.51) years; 938 patients were men (48.5%) and 995 were women (51.5%). The genetic defect was unidentified in 920 patients (47.6%). A total of 1371 patients (70.9%) had ataxia and 562 (29.1%) had hereditary spastic paraplegia. Prevalence rates for ataxia and hereditary spastic paraplegia were estimated at 5.48 and 2.24 cases per 100 000 population, respectively. The most frequent type of dominant ataxia in our sample was SCA3, and the most frequent recessive ataxia was Friedreich ataxia. The most frequent type of dominant hereditary spastic paraplegia in our sample was SPG4, and the most frequent recessive type was SPG7. Conclusions: In our sample, the estimated prevalence of ataxia and hereditary spastic paraplegia was 7.73 cases per 100 000 population. This rate is similar to those reported for other countries. Genetic diagnosis was not available in 47.6% of cases. Despite these limitations, our study provides useful data for estimating the necessary healthcare resources for these patients, raising awareness of these diseases, determining the most frequent causal mutations for local screening programmes, and promoting the development of clinical trials. Resumen: Introducción: Las ataxias (AT) y paraparesias espásticas hereditarias (PEH) son síndromes neurodegenerativos raros. Nos proponemos conocer la prevalencia de las AT y PEH (APEH) en España en 2019. Pacientes y métodos: Estudio transversal, multicéntrico, descriptivo y retrospectivo de los pacientes con AT y PEH, desde Marzo de 2018 a Diciembre de 2019 en toda España. Resultados: Se obtuvo información de 1.933 pacientes procedentes de 11 Comunidades Autónomas, de 47 neurólogos o genetistas. Edad media: 53,64 años ± 20,51 desviación estándar (DE); 938 varones (48,5%), 995 mujeres (51,1%). En 920 pacientes (47,6%) no se conoce el defecto genético. Por patologías, 1.371 pacientes (70,9%) diagnosticados de AT, 562 diagnosticados de PEH (29,1%). La prevalencia estimada de AT es 5,48/100.000 habitantes, y la de PEH es 2,24 casos/100.000 habitantes. La AT dominante más frecuente es la SCA3. La AT recesiva más frecuente es la ataxia de Friedreich (FRDA). La PEH dominante más frecuente es la SPG4, y la PEH recesiva más frecuente es la SPG7. Conclusiones: La prevalencia estimada de APEH en nuestra serie es de 7,73 casos/100.000 habitantes. Estas frecuencias son similares a las del resto del mundo. En el 47,6% no se ha conseguido un diagnóstico genético. A pesar de las limitaciones, este estudio puede contribuir a estimar los recursos, visibilizar estas enfermedades, detectar las mutaciones más frecuentes para hacer los screenings por comunidades, y favorecer los ensayos clínicos.

Julie Rushmore, Brianna R. Beechler, Hannah Tavalire et al.

Abstract Many infectious pathogens are shared through social interactions, and examining host connectivity has offered valuable insights for understanding patterns of pathogen transmission across wildlife species. African buffalo are social ungulates and important reservoirs of directly‐transmitted pathogens that impact numerous wildlife and livestock species. Here, we analyzed African buffalo social networks to quantify variation in close contacts, examined drivers of contact heterogeneity, and investigated how the observed contact patterns affect pathogen invasion likelihoods for a wild social ungulate. We collected continuous association data using proximity collars and sampled host traits approximately every 2 months during a 15‐month study period in Kruger National Park, South Africa. Although the observed herd was well connected, with most individuals contacting each other during each bimonthly interval, our analyses revealed striking heterogeneity in close‐contact associations among herd members. Network analysis showed that individual connectivity was stable over time and that individual age, sex, reproductive status, and pairwise genetic relatedness were important predictors of buffalo connectivity. Calves were the most connected members of the herd, and adult males were the least connected. These findings highlight the role susceptible calves may play in the transmission of pathogens within the herd. We also demonstrate that, at time scales relevant to infectious pathogens found in nature, the observed level of connectivity affects pathogen invasion likelihoods for a wide range of infectious periods and transmissibilities. Ultimately, our study identifies key predictors of social connectivity in a social ungulate and illustrates how contact heterogeneity, even within a highly connected herd, can shape pathogen invasion likelihoods.

Jinping HUANG, Ju WEN, Ting LI et al.

A rare case of psoriasiform graft-versus-host disease is reported. The patient suffered from acute B-cell leukemia for more than 1 year. Nine months after allogenic hematopoietic stem cell transplantation, the patient developed red papules and plaques, with Auspitz sign positive and silver-white scales on the head, face, limb and trunk. The histopathology showed both psoriatic and interfacial dermatitis. Immunohistochemical staining demonstrated infiltration of CD4+ and CD8+ T cells. Diagnosis was psoriasiform graft-versus-host disease. Lesions were improved by topical 0.1% tacrolimus ointment.

Nicolae BACINSCHI, Lorina VUDU, Stela BACINSCHI-GHEORGHITA et al.

Туберкулез — инфекционное заболевание, поражающее практически все органы, в том числе и щитовидную железу. Одновременно изменение функции щитовидной железы может повысить восприимчивость к инфекции Mycobacterium tuberculosis. Лечение туберкулеза, особенно препаратами второго ряда, может обуславливать раз- витие гипотиреоза. Установлено, что рифампицин, этионамид, протионамид и парааминосалициловая кислота являются одними из наиболее распространенных противотуберкулезных препаратов, ответственных за развитие гипотиреоза. Эти препараты могут вызывать дисфункцию щитовидной железы путем увеличения метаболизма и клиренса тиреоидных гормонов за счет индукции ферментов цитохрома Р-450, нарушения регуляции поглощения йода и синтеза тиреоидных гормонов, изменения действия гормонов на уровне рецепторов и передачи внутрикле- точного сигнала. Лечение этими препаратами требует контроля функции щитовидной железы во время лечения, особенно в первые 3 месяцa, а также в пост-лечебный период. Развитие клинического и/или субклинического гипотиреоза потребует применения адекватных доз левотироксина на фоне противотуберкулезного лечения.