Results 31 to 40 of about 16,504,576 (273)

AI-enabled language models (LMs) to large language models (LLMs) and multimodal large language models (MLLMs) in drug discovery and development

Journal of Advanced Research
Background: Due to the recent revolution of artificial intelligence (AI), AI-enabled large language models (LLMs) have flourished and started to be applied in various sectors of science and medicine.
Chiranjib Chakraborty, Manojit Bhattacharya, Soumen Pal, Srijan Chatterjee, Arpita Das, Sang-Soo Lee   +5 more
doaj   +1 more source

Comprehensive Assessment of Arterial, Tissue, and Venous Collaterals for Evaluating the Infarct Growth Rate: The Multimodal Collateral Score

Annals of Clinical and Translational Neurology, EarlyView.
ABSTRACT Background Collaterals are crucial factors that influence the infarct growth rate (IGR). We aimed to determine whether a comprehensive multimodal collateral score (MCS), incorporating collateral assessment at the arterial, tissue, and venous levels, is associated with functional independence and provides incremental prognostic value over ...
Giorgio Busto, Umberto Pensato, Ilaria Casetta, Giorgio U. Marcello, Angelo Barra, Anna Poggesi, Edoardo Carlesi, Sara Mancini, Andrea Ginestroni, Vittorio Miele, Alessandro Padovani, Andrea Morotti, Enrico Fainardi   +12 more
wiley   +1 more source

Grounding Multimodal Large Language Models in Actions

Advances in Neural Information Processing Systems 37
Multimodal Large Language Models (MLLMs) have demonstrated a wide range of capabilities across many domains, including Embodied AI. In this work, we study how to best ground a MLLM into different embodiments and their associated action spaces, with the goal of leveraging the multimodal world knowledge of the MLLM.
Andrew Szot, Bogdan Mazoure, Harsh Agrawal, R. Devon Hjelm, Zsolt Kira, Alexander Toshev   +5 more
openaire   +3 more sources

Cognitive and Neuroimaging Divergence Between Juvenile and Adult FUS Amyotrophic Lateral Sclerosis

Annals of Clinical and Translational Neurology, EarlyView.
ABSTRACT Objective Amyotrophic lateral sclerosis (ALS) is a neurodegenerative disorder characterized by progressive motor neuron degeneration. Fused in sarcoma (FUS)‐associated juvenile ALS (jALS) represents a distinct and aggressive subgroup with rapid deterioration and poor prognosis.
Alexandra V. Jürs, Marcel Naumann, Annaliis Lehto, Hanna Schön, Jens Kurth, Johannes Prudlo, Andreas Hermann, Elisabeth Kasper   +7 more
wiley   +1 more source

Multimodal Large Language Models in the Era of Distributed AI

The rapid advancements in Large Language Models (LLMs) and Multimodal Large Language Models (MLLMs) have revolutionized artificial intelligence (AI), enabling unprecedented capabilities in natural language processing (NLP), computer vision, and ...
Kumar, Rajesh, Laurent, Isabelle, Müller, David, Elli, Klaus   +3 more
core   +3 more sources

Multimodal Large Language Models as Image Classifiers

CoRR
Multimodal Large Language Model (MLLM) classification performance depends critically on evaluation protocol and ground truth quality. Studies comparing MLLMs with supervised and Vision-Language Models (VLMs) report conflicting conclusions, and we show these conflicts stem from protocols that either inflate or underestimate performance.
Nikita Kisel, Illia Volkov, Klára Janousková, Jiri Matas   +3 more
openaire   +2 more sources

Integration of Serum Neurofilament Light Chain and Cortical Dysfunction Improves Diagnostic Accuracy in ALS

Annals of Clinical and Translational Neurology, EarlyView.
ABSTRACT Objective To determine whether integration of serum neurofilament light chain (NfL) and cortical dysfunction improves diagnostic accuracy in amyotrophic lateral sclerosis (ALS) when applied alongside the Gold Coast criteria (GCC). Methods In this prospective study, 148 participants with suspected ALS were recruited (101 ALS and 47 with ALS ...
Aicee Dawn Calma, Nathan Pavey, Yukiko Tsuji, Amirul Ab Ghapar, Mehdi A.J. van den Bos, Bronwen Orden, Grace Lee, Linda Mekhael, Julie Ryder, Cláudia Santos Silva, David McDonald, Fakhria Kakar, Con Yiannikas, Matthew C. Kiernan, David A. Brown, Parvathi Menon, Steve Vucic   +16 more
wiley   +1 more source

Research on the identification and evaluation system of network security risks in large language models

Dianzi Jishu Yingyong
This study aims to comprehensively identify the network security risks of multimodal large language models, construct a security evaluation system, and propose response strategies to enhance their security and reliability.
Cai Chaoxiong
doaj   +1 more source

Long‐Term Neurologic Exam Findings in People Diagnosed and Treated During Acute HIV Infection

Annals of Clinical and Translational Neurology, EarlyView.
ABSTRACT Objective Evaluate clinical and laboratory correlates of abnormal neurologic exam findings after acute HIV infection (AHI). Methods Participants from the RV254/SEARCH 010 cohort in Bangkok underwent standardized neurologic examinations at Weeks 0 (AHI), 12, 96, and 288 following antiretroviral therapy (ART).
Kathryn B. Holroyd, Tyler Hamby, Carlo Sacdalan, Somchai Sriplienchan, Pathariya Promensa, Luxenaree Poonpitak, Netsiri Dumrongpisutikul, Eugène Kroon, Sandhya Vasan, Robert Paul, Ferron O'Campo, Lydie Trautmann, Phillip Chan, Serena Spudich, on behalf of the RV254/SEARCH 010 Study Team, Julie Ake, Sandhya Vasan, Lydie Trautmann, Merlin Robb, Trevor Crowell, Donn Colby, Diane Bolton, Shelly Krebs, Leigh Ann Eller, Morgane Rolland, Rasmi Thomas, Dominic Paquin Proulx, Suteeraporn Pinyakorn, Tyler Hamby, Mark de Souza, Ellen Turk, Amber Moodley, Gerald Quarles, Jintanat Ananworanich, Denise Hsu, Victoria Polonis, Nelson Michael, Sheila Peel, COL Eric Garges, Alexandra Schuetz, Siriwat Akapirat, Bessara Nantapinit, Pornsuk Visudhiphan, Saowanit Getchalarat, Nampueng Churikanont, Sangsulee Thamakaison, Somchai Sriplienchan, Carlo Sacdalan, Pathariya Promsena, Luxe‐naree Poonpitak, Eugene Kroon, Nitiya Chomchey, Jintana Intasan, Varaporn Pothipala, Ferron Ocampo, Nittaya Phanuphak, Nipat Teeratakulpisan, Yuwaree Pichitchok, Anchalee Avihingsanon, Opass Putcharoen, Kiat Ruxrungtham, Thanyawee Puthanakit, Sopark Manasnayakorn, Phandee Watanaboonyongcharoen, Rungsun Rerknimitr, Irini Sereti, Daniel Douek, Eli Bortiz, Frank Maldarelli, Mary Kearney, Serena Spudich, Phillip Chan, Lishomwa Ndhlovu, Guinevere Li, Michael Corley, Victor Valcour, Peter Hunt, Jacob Estes, Afam Okoye, Jeff Lifson, Claire Deleage, Nicolas Chomont, Andres Finzi, Lyle McKinnon, Henrik Zetterberg, Robert Siliciano, Robert Paul, Julie Mannarino, Jonathan Li, John Mellors, Josh Cytkor, Dan Barouch, Michael Seaman, Boris Juelg, Alex Shalek, Timothy Schacker, Ashley Haase, Mario Stevenson, Wenjun Li, Arthur Kim, Sharon Lewin, Adam Carrico, Susana Valente, Fred Hutchinson, Josh Schiffer, Guido Ferrari, Beau Ances, Alberto Bosque, Katharine Bar, Killian Pohl, David Ho, Yaoxing Huang, Michael Busch, Mohamed Abdel‐Mohsen, Johanna Gostner, Brian Agan, Fatah Kashanchi, Greg Laird, Kathryn Holroyd   +118 more
wiley   +1 more source

Generative Models in Medical Visual Question Answering: A Survey

Applied Sciences
Medical Visual Question Answering (MedVQA) is a crucial intersection of artificial intelligence and healthcare. It enables systems to interpret medical images—such as X-rays, MRIs, and pathology slides—and respond to clinical queries.
Wenjie Dong, Shuhao Shen, Yuqiang Han, Tao Tan, Jian Wu, Hongxia Xu   +5 more
doaj   +1 more source