Current applications and challenges in large language models for patient care: a systematic review.

Background: The introduction of large language models (LLMs) into clinical practice promises to improve patient education and empowerment, thereby personalizing medical care and broadening access to medical knowledge. Despite the popularity of LLMs, there is a significant gap in systematized information on their use in patient care. Therefore, this systematic review aims to synthesize current applications and limitations of LLMs in patient care.

Serological evidence of Crimean-Congo haemorrhagic fever in domestic animals from eight regions of Namibia.

Crimean-Congo haemorrhagic fever (CCHF) is a viral zoonotic disease endemic to regions of Africa, the Balkans, the Middle East, and Asia, with increasing reports of cases in southern Europe. Human transmission occurs primarily through the bite of infected ticks and by body fluids from infected human. Crimean-Congo haemorrhagic fever virus (CCHFV) affects a broad host range, including both domestic and wild vertebrates.

Open-source Large Language Models can Generate Labels from Radiology Reports for Training Convolutional Neural Networks.

Rationale And Objectives: Training Convolutional Neural Networks (CNN) requires large datasets with labeled data, which can be very labor-intensive to prepare. Radiology reports contain a lot of potentially useful information for such tasks. However, they are often unstructured and cannot be directly used for training.

Conversation With Mark Solms.

Occasioned by the publication of , the text represents exchanges held in the summer of 2024 at the invitation of . It addresses the genesis and the history of the project, and its impact on psychoanalytic rethinking, formation, and practice. While exploring the potential and the limits of revisioning, it also raises questions about the nature of transmission of psychoanalytic knowledge and about the field's relation to the state of its own standards in the age of Anthropocene.

Large Language Model Ability to Translate CT and MRI Free-Text Radiology Reports Into Multiple Languages.

Background High-quality translations of radiology reports are essential for optimal patient care. Because of limited availability of human translators with medical expertise, large language models (LLMs) are a promising solution, but their ability to translate radiology reports remains largely unexplored. Purpose To evaluate the accuracy and quality of various LLMs in translating radiology reports across high-resource languages (English, Italian, French, German, and Chinese) and low-resource languages (Swedish, Turkish, Russian, Greek, and Thai).

Autonomous medical evaluation for guideline adherence of large language models.

Autonomous Medical Evaluation for Guideline Adherence (AMEGA) is a comprehensive benchmark designed to evaluate large language models' adherence to medical guidelines across 20 diagnostic scenarios spanning 13 specialties. It includes an evaluation framework and methodology to assess models' capabilities in medical reasoning, differential diagnosis, treatment planning, and guideline adherence, using open-ended questions that mirror real-world clinical interactions. It includes 135 questions and 1337 weighted scoring elements designed to assess comprehensive medical knowledge.

Multilingual feasibility of GPT-4o for automated Voice-to-Text CT and MRI report transcription.

Purpose: Large language models (LLMs) promise to streamline radiology reporting. With the release of OpenAI's GPT-4o (Generative Pre-trained Transformers-4 omni), which processes not only text but also speech, multimodal LLMs might now also be used as medical speech recognition software for radiology reporting in multiple languages. This proof-of-concept study investigates the feasibility of using GPT-4o for automated voice-to-text transcription of radiology reports in English and German.

Large language models for structured reporting in radiology: past, present, and future.

Structured reporting (SR) has long been a goal in radiology to standardize and improve the quality of radiology reports. Despite evidence that SR reduces errors, enhances comprehensiveness, and increases adherence to guidelines, its widespread adoption has been limited. Recently, large language models (LLMs) have emerged as a promising solution to automate and facilitate SR.

Large Language Models for Simplified Interventional Radiology Reports: A Comparative Analysis.

Purpose: To quantitatively and qualitatively evaluate and compare the performance of leading large language models (LLMs), including proprietary models (GPT-4, GPT-3.5 Turbo, Claude-3-Opus, and Gemini Ultra) and open-source models (Mistral-7b and Mistral-8×7b), in simplifying 109 interventional radiology reports.

Methods: Qualitative performance was assessed using a five-point Likert scale for accuracy, completeness, clarity, clinical relevance, naturalness, and error rates, including trust-breaking and post-therapy misconduct errors.

Global cross-sectional student survey on AI in medical, dental, and veterinary education and practice at 192 faculties.

Background: The successful integration of artificial intelligence (AI) in healthcare depends on the global perspectives of all stakeholders. This study aims to answer the research question: What are the attitudes of medical, dental, and veterinary students towards AI in education and practice, and what are the regional differences in these perceptions?

Methods: An anonymous online survey was developed based on a literature review and expert panel discussions. The survey assessed students' AI knowledge, attitudes towards AI in healthcare, current state of AI education, and preferences for AI teaching.

Tribulus (Zygophyllaceae) as a case study for the evolution of C and C photosynthesis.

C photosynthesis is a photosynthetic pathway in which photorespiratory CO release and refixation are enhanced in leaf bundle sheath (BS) tissues. The evolution of C photosynthesis has been hypothesized to be a major step in the origin of C photosynthesis, highlighting the importance of studying C evolution. In this study, physiological, anatomical, ultrastructural, and immunohistochemical properties of leaf photosynthetic tissues were investigated in six non-C Tribulus species and four C Tribulus species.

Serological survey on Hepatitis E virus in Namibian dogs, cats, horses, and donkeys.

The present study investigated the seropositivity rate of Hepatitis E virus (HEV) in domestic and working animals in Namibia, which included dogs, cats, horses, and donkeys. HEV poses a growing threat as a significant cause of human hepatitis globally and has several genotypes of varying zoonotic potential. As epidemiological data on the seroprevalence of HEV in Namibia is scarce, a serosurvey was conducted on archived serum samples of 374 dogs, 238 cats, 98 horses, and 60 donkeys collected between 2018 and 2022 from different regions, to assess the potential of these animals as sources of HEV infection.

Correction: Integrating Text and Image Analysis: Exploring GPT-4V's Capabilities in Advanced Radiological Applications Across Subspecialties.

[This corrects the article DOI: 10.2196/54948.].

Open Access Data and Deep Learning for Cardiac Device Identification on Standard DICOM and Smartphone-based Chest Radiographs.

Purpose To develop and evaluate a publicly available deep learning model for segmenting and classifying cardiac implantable electronic devices (CIEDs) on Digital Imaging and Communications in Medicine (DICOM) and smartphone-based chest radiographs. Materials and Methods This institutional review board-approved retrospective study included patients with implantable pacemakers, cardioverter defibrillators, cardiac resynchronization therapy devices, and cardiac monitors who underwent chest radiography between January 2012 and January 2022. A U-Net model with a ResNet-50 backbone was created to classify CIEDs on DICOM and smartphone images.

Integrating Text and Image Analysis: Exploring GPT-4V's Capabilities in Advanced Radiological Applications Across Subspecialties.

This study demonstrates that GPT-4V outperforms GPT-4 across radiology subspecialties in analyzing 207 cases with 1312 images from the Radiological Society of North America Case Collection.

Serological Evidence of Crimean-Congo Haemorrhagic Fever in Livestock in the Omaheke Region of Namibia.

This research examined the positivity ratio of Crimean-Congo haemorrhagic fever (CCHF) antibodies in cattle and sheep within Namibia's Omaheke region after a human disease outbreak in the same geographical area. A total of 200 samples (100 cattle and 100 sheep) were randomly collected from animals brought to two regional auction sites, and then tested using the ID Screen CCHF Double Antigen Multi-Species Enzyme-Linked Immunosorbent Assay kit. Of the cattle samples, 36% tested positive, while 22% of the sheep samples were seropositive.

Protein-Centric Analysis of Personalized Antibody Repertoires Using LC-MS-Based Fab-Profiling on a timsTOF.

Endogenous antibodies, or immunoglobulins (Igs), abundantly present in body fluids, represent some of the most challenging samples to analyze, largely due to the immense variability in their sequences and concentrations. It has been estimated that our body can produce billions of different Ig proteins with different isotypes, making their individual analysis seemingly impossible. However, recent advances in protein-centric proteomics using LC-MS coupled to Orbitrap mass analyzers to profile intact Fab fragments formed by selective cleavage at the IgG-hinge revealed that IgG repertoires may be less diverse, albeit unique for each donor.

Using Carbon Stable Isotopes to Study C and C Photosynthesis: Models and Calculations.

Stable carbon isotopes are a powerful tool to study photosynthesis. Initial applications consisted of determining isotope ratios of plant biomass using mass spectrometry. Subsequently, theoretical models relating C isotope values to gas exchange characteristics were introduced and tested against instantaneous online measurements of C photosynthetic discrimination.

Photosynthetic Gas Exchange in Land Plants at the Leaf Level.

Leaf-level gas exchange enables insights into the physiology and in vivo biochemical processes of plants. Advances in infrared gas analysis have resulted in user-friendly off-the-shelf gas exchange systems that allow researchers to collect physiological measurements with the push of a few buttons. Here, I describe how to set up the gas exchange equipment, what to pay attention to while making measurements, and provide some guidelines on how to analyze and interpret the data obtained.

Tools for Measuring Photosynthesis at Different Scales.

Measurements of in vivo photosynthesis are powerful tools that probe the largest fluxes of carbon and energy in an illuminated leaf, but often the specific techniques used are so varied and specialized that it is difficult for researchers outside the field to select and perform the most useful assays for their research questions. The goal of this chapter is to provide a broad overview of the current tools available for the study of photosynthesis, both in vivo and in vitro, so as to provide a foundation for selecting appropriate techniques, many of which are presented in detail in subsequent chapters. This chapter will also organize current methods into a comparative framework and provide examples of how they have been applied to research questions of broad agronomical, ecological, or biological importance.