This scoping review identifies the first wave of randomized controlled trials testing large language models in digestive diseases. Four published and ten ongoing trials suggest that AI tools such as ...
A research team affiliated with UNIST has unveiled a novel AI system capable of grading and providing detailed feedback on ...
openbench provides standardized, reproducible benchmarking for LLMs across 30+ evaluation suites (and growing) spanning knowledge, math, reasoning, coding, science, reading comprehension, health, long ...
Dietary assessment has long been a bottleneck in nutrition research and public health. Common tools such as food frequency questionnaires, 24-hour recalls, and weighed food records rely heavily on ...
Abstract: This paper introduces the LLaVA-NeXT-Med model, a medical multi-modal system that is pretrained and fine tuned using datasets incorporating LLaVA-Med data to enhance the performance of ...
Abstract: As Large language models (LLMs) become increasingly integrated into high-stakes applications, ensuring their trustworthiness has emerged as a critical research concern. This study proposes a ...
With the rising technological prowess and greater openness of Chinese models, the world is increasingly turning to the East for efficient and customizable AI, a new report finds.
The release marks a significant strategic pivot for Google DeepMind and the Google AI Developers team. While the industry ...
Local Interpretable Model-Agnostic Explanations (LIME) was used to explain and enhance physicians’ trust in model outputs within the medical context. Results: Nine models were included in this study, ...
The cLLM (chat-optimized Large Language Model, "clem") framework allows researchers to easily evaluate the ability of large language models (LLM) by engaging them in games – rule-constituted ...
CLIP is one of the most important multimodal foundational models today. What powers CLIP’s capabilities? The rich supervision signals provided by natural language, the carrier of human knowledge, ...
CLIP is one of the most important multimodal foundational models today, aligning visual and textual signals into a shared feature space using a simple contrastive learning loss on large-scale ...
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