A5043862316- Advanced MRI Techniques and Applications
- Topic Modeling
- Artificial Intelligence in Healthcare and Education
- Medical Imaging Techniques and Applications
- Cardiac Imaging and Diagnostics
- Machine Learning in Healthcare
- Advanced Neuroimaging Techniques and Applications
- Radiomics and Machine Learning in Medical Imaging
- Natural Language Processing Techniques
- Tissue Engineering and Regenerative Medicine
- Radiology practices and education
- Pluripotent Stem Cells Research
- Advanced X-ray and CT Imaging
- Ultrasound Imaging and Elastography
- Radiation Detection and Scintillator Technologies
- Superconducting and THz Device Technology
- Thermal Radiation and Cooling Technologies
- Cardiovascular Function and Risk Factors
- Multimodal Machine Learning Applications
- Nasal Surgery and Airway Studies
- Simulation Techniques and Applications
- Biochemical Analysis and Sensing Techniques
- Oral health in cancer treatment
- Optical Network Technologies
- Cell death mechanisms and regulation
Google (United States)
2022-2025
Google (United Kingdom)
2024
DeepMind (United Kingdom)
2024
Stanford University
2006-2014
HeartVista (United States)
2014
Resonance Research (United States)
2011-2013
Stanford Medicine
2010-2012
École Polytechnique
2005
Laboratoire d'Informatique de l'École Polytechnique
2005
Abstract Large language models (LLMs) have demonstrated impressive capabilities, but the bar for clinical applications is high. Attempts to assess knowledge of typically rely on automated evaluations based limited benchmarks. Here, address these limitations, we present MultiMedQA, a benchmark combining six existing medical question answering datasets spanning professional medicine, research and consumer queries new dataset questions searched online, HealthSearchQA. We propose human...
Purpose There are many T 1 mapping methods available, each of them validated in phantoms and reporting excellent agreement with literature. However, values literature vary greatly, white matter ranging from 690 to 1100 ms at 3 Tesla. This brings into question the accuracy one most fundamental measurements quantitative MRI. Our goal was explain these variations look ways mitigating them. Theory Methods We evaluated three common (inversion recovery, Look‐Locker, variable flip angle) through...
Recent artificial intelligence (AI) systems have reached milestones in "grand challenges" ranging from Go to protein-folding. The capability retrieve medical knowledge, reason over it, and answer questions comparably physicians has long been viewed as one such grand challenge. Large language models (LLMs) catalyzed significant progress question answering; Med-PaLM was the first model exceed a "passing" score US Medical Licensing Examination (USMLE) style with of 67.2% on MedQA dataset....
BackgroundMedicine is inherently multimodal, requiring the simultaneous interpretation and integration of insights between many data modalities spanning text, imaging, genomics, more. Generalist biomedical artificial intelligence systems that flexibly encode, integrate, interpret these might better enable impactful applications ranging from scientific discovery to care delivery.MethodsTo catalyze development models, we curated MultiMedBench, a new multimodal benchmark. MultiMedBench...
At the heart of medicine lies physician-patient dialogue, where skillful history-taking paves way for accurate diagnosis, effective management, and enduring trust. Artificial Intelligence (AI) systems capable diagnostic dialogue could increase accessibility, consistency, quality care. However, approximating clinicians' expertise is an outstanding grand challenge. Here, we introduce AMIE (Articulate Medical Explorer), a Large Language Model (LLM) based AI system optimized dialogue. uses novel...
Excellence in a wide variety of medical applications poses considerable challenges for AI, requiring advanced reasoning, access to up-to-date knowledge and understanding complex multimodal data. Gemini models, with strong general capabilities long-context offer exciting possibilities medicine. Building on these core strengths Gemini, we introduce Med-Gemini, family highly capable models that are specialized medicine the ability seamlessly use web search, can be efficiently tailored novel...
Large language models (LLMs) have shown promise in medical question answering, with Med-PaLM being the first to exceed a 'passing' score United States Medical Licensing Examination style questions. However, challenges remain long-form answering and handling real-world workflows. Here, we present 2, which bridges these gaps combination of base LLM improvements, domain fine-tuning new strategies for improving reasoning grounding through ensemble refinement chain retrieval. 2 scores up 86.5% on...
Abstract In this article, a robust methodology for in vivo T 1 mapping is presented. The approach combines gold standard scanning procedure with novel fitting procedure. Fitting complex data to five‐parameter model ensures accuracy and precision of the estimation. A reduced‐dimension nonlinear least squares method proposed. This turns complicated multi‐parameter minimization into straightforward one‐dimensional search. As range possible values known, global grid search can be used, ensuring...
An accurate differential diagnosis (DDx) is a cornerstone of medical care, often reached through an iterative process interpretation that combines clinical history, physical examination, investigations and procedures. Interactive interfaces powered by Large Language Models (LLMs) present new opportunities to both assist automate aspects this process. In study, we introduce LLM optimized for diagnostic reasoning, evaluate its ability generate DDx alone or as aid clinicians. 20 clinicians...
Automated radiology report generation has the potential to improve patient care and reduce workload of radiologists. However, path toward real-world adoption been stymied by challenge evaluating clinical quality artificial intelligence (AI)-generated reports. We build a state-of-the-art system for chest radiographs, called Flamingo-CXR, perform an expert evaluation AI-generated reports engaging panel board-certified observe wide distribution preferences across settings, with 56.1%...
Large language models (LLMs) have demonstrated impressive capabilities in natural understanding and generation, but the quality bar for medical clinical applications is high. Today, attempts to assess models' knowledge typically rely on automated evaluations limited benchmarks. There no standard evaluate model predictions reasoning across a breadth of tasks. To address this, we present MultiMedQA, benchmark combining six existing open question answering datasets spanning professional exams,...
Many clinical tasks require an understanding of specialized data, such as medical images and genomics, which is not typically found in general-purpose large multimodal models. Building upon Gemini's models, we develop several models within the new Med-Gemini family that inherit core capabilities Gemini are optimized for use via fine-tuning with 2D 3D radiology, histopathology, ophthalmology, dermatology genomic data. Med-Gemini-2D sets a standard AI-based chest X-ray (CXR) report generation...
Medicine is inherently multimodal, with rich data modalities spanning text, imaging, genomics, and more. Generalist biomedical artificial intelligence (AI) systems that flexibly encode, integrate, interpret this at scale can potentially enable impactful applications ranging from scientific discovery to care delivery. To the development of these models, we first curate MultiMedBench, a new multimodal benchmark. MultiMedBench encompasses 14 diverse tasks such as medical question answering,...
Abstract Cell death by apoptosis is critical in myocardial diseases, and noninvasive detection of early, reversible might be useful clinically. Exogenous Annexin‐V (ANX) protein binds membrane phosphatidylserine, which externalized early apoptosis. A molecular MRI probe was constructed with superparamagnetic iron oxide (SPIO) conjugated to recombinant human ANX (ANX‐SPIO). Apoptosis induced doxorubicin, a cardiotoxic cancer drug, culture neonatal rat ventricular myocytes, cardiac...
As a noninvasive modality, MR is attractive for in vivo skin imaging. Its unique soft tissue contrast makes it an ideal imaging modality to study the water content and resolve different layers. In this work, challenges of high-resolution are addressed. Three 3D Cartesian sequences customized achieve their respective performance evaluated. The balanced steady-state free precession (bSSFP) gradient echo (GRE) fast but can be sensitive off-resonance artifacts. large-angle spin (FLASE) sequence...
Purpose The balanced steady‐state free precession (bSSFP) pulse sequence has shown to be of great interest due its high signal‐to‐noise ratio efficiency. However, bSSFP images often suffer from banding artifacts off‐resonance effects, which we aim minimize in this article. Methods We present a general and fast two‐step algorithm for 1) estimating the unknowns signal model multiple phase‐cycled acquisitions, 2) reconstructing band‐free images. first step, linearization estimation (LORE),...
Abstract Embryonic stem cells (ESCs) have shown the potential to restore cardiac function after myocardial injury. Superparamagnetic iron oxide nanoparticles (SPIO) been widely employed label ESCs for cellular MRI. However, nonspecific intracellular accumulation of SPIO limits long‐term in vivo assessment transplanted cells. To overcome this limitation, a novel reporter gene (RG) has developed express antigens on ESC surface. By employing SPIO‐conjugated monoclonal antibody against these...
Background FeCo/graphitic-carbon nanocrystals (FeCo/GC) are biocompatible, high-relaxivity, multi-functional nanoparticles. Macrophages represent important cellular imaging targets for assessing vascular inflammation. We evaluated FeCo/GC macrophage uptake and in vivo using fluorescence MRI. Methods Results Hyperlipidemic diabetic mice underwent carotid ligation to produce a macrophage-rich lesion. In situ ex were performed at 48 hours after intravenous injection of conjugated Cy5.5 (n = 8,...
<title>Abstract</title> Radiology reports are an instrumental part of modern medicine, informing key clinical decisions such as diagnosis and treatment. The worldwide shortage radiologists, however, restricts access to expert care imposes heavy workloads, contributing avoidable errors in report delivery. While recent progress automated generation with vision-language models offers clear potential ameliorate this situation, the path toward real-world adoption has been stymied by challenge...