A5063201022- Prostate Cancer Diagnosis and Treatment
- Medical Image Segmentation Techniques
- Ultrasound Imaging and Elastography
- Artificial Intelligence in Healthcare and Education
- Topic Modeling
- Advanced Radiotherapy Techniques
- Medical Imaging and Analysis
- Photoacoustic and Ultrasonic Imaging
- Machine Learning in Healthcare
- Spectroscopy Techniques in Biomedical and Chemical Research
- Radiomics and Machine Learning in Medical Imaging
- Dialysis and Renal Disease Management
- Advanced X-ray and CT Imaging
- Parathyroid Disorders and Treatments
- Advanced Neural Network Applications
- AI in cancer detection
- Ultrasound and Hyperthermia Applications
- Vitamin D Research Studies
- Natural Language Processing Techniques
- Control and Dynamics of Mobile Robots
- Robotic Locomotion and Control
- Advanced Numerical Analysis Techniques
- Robotic Path Planning Algorithms
- Image and Object Detection Techniques
- Generative Adversarial Networks and Image Synthesis
Google (United States)
2023-2025
Google (Canada)
2023-2025
The Scarborough Hospital
2012-2024
ESPCI Paris
2024
Gulliver
2024
Google (United Kingdom)
2024
DeepMind (United Kingdom)
2024
University of Toronto
2012-2024
Alborz University of Medical Sciences
2024
Jahrom University of Medical Sciences
2024
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...
We present Imagen, a text-to-image diffusion model with an unprecedented degree of photorealism and deep level language understanding. Imagen builds on the power large transformer models in understanding text hinges strength high-fidelity image generation. Our key discovery is that generic (e.g. T5), pretrained text-only corpora, are surprisingly effective at encoding for synthesis: increasing size boosts both sample fidelity image-text alignment much more than model. achieves new...
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...
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%...
Abstract At the heart of medicine lies physician–patient dialogue, where skillful history-taking enables effective diagnosis, management and enduring trust 1,2 . Artificial intelligence (AI) systems capable diagnostic dialogue could increase accessibility quality care. However, approximating clinicians’ expertise is an outstanding challenge. Here we introduce AMIE (Articulate Medical Intelligence Explorer), a large language model (LLM)-based AI system optimized for dialogue. uses...
Abstract A comprehensive differential diagnosis is a cornerstone of medical care that often reached through an iterative process interpretation combines clinical history, physical examination, investigations and procedures. Interactive interfaces powered by large language models present new opportunities to assist automate aspects this 1 . Here we introduce the Articulate Medical Intelligence Explorer (AMIE), model optimized for diagnostic reasoning, evaluate its ability generate alone or as...
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...
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,...
Caffeine is detoxified by cytochrome P450 1A2 (CYP1A2), and genetic variation in CYP1A2 impacts the rate of caffeine clearance. Factors that may modify association between coffee intake kidney disease remain unclear.To assess whether genotype modifies dysfunction.The Hypertension Ambulatory Recording Venetia Study (HARVEST) was a prospective cohort study individuals with stage 1 hypertension Italy; HARVEST began on April 1, 1990, follow-up ongoing. The current used data from to June 30,...
Recent progress in Medical Artificial Intelligence (AI) has delivered systems that can reach clinical expert level performance. However, such tend to demonstrate sub-optimal "out-of-distribution" performance when evaluated settings different from the training environment. A common mitigation strategy is develop separate for each setting using site-specific data [1]. this quickly becomes impractical as medical time-consuming acquire and expensive annotate [2]. Thus, problem of "data-efficient...
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,...
Developing therapeutics is a lengthy and expensive process that requires the satisfaction of many different criteria, AI models capable expediting would be invaluable. However, majority current approaches address only narrowly defined set tasks, often circumscribed within particular domain. To bridge this gap, we introduce Tx-LLM, generalist large language model (LLM) fine-tuned from PaLM-2 which encodes knowledge about diverse therapeutic modalities. Tx-LLM trained using collection 709...
Low-dose-rate brachytherapy is a radiation treatment method for localized prostate cancer. The standard of care this procedure to acquire transrectal ultrasound images the in order devise plan deliver sufficient dose cancerous tissue. Brachytherapy planning involves delineation contours these images, which closely follow boundary, i.e., clinical target volume. This process currently performed either manually or semi-automatically, requires user interaction landmark initialization. In paper,...
Cells adapt to environments and tune gene expression by controlling the concentrations of proteins their kinetics in regulatory networks. In both eukaryotes prokaryotes, experiments theory increasingly attest that these networks can do consume bio-chemical energy. How does this dissipation enable cellular behaviors unobtainable equilibrium? This open question demands quantitative models transcend thermodynamic equilibrium. Here we study control a simple, ubiquitous motif explore consequences...