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Found 11 papers, 7 papers with code
ReflecTool: Towards Reflection-Aware Tool-Augmented Clinical Agents
Large Language Models (LLMs) have shown promising potential in the medical domain, assisting with tasks like clinical note generation and patient communication.
Med-PMC: Medical Personalized Multi-modal Consultation with a Proactive Ask-First-Observe-Next Paradigm
The application of the Multi-modal Large Language Models (MLLMs) in medical clinical scenarios remains underexplored.
Decoding Linguistic Representations of Human Brain
Language, as an information medium created by advanced organisms, has always been a concern of neuroscience regarding how it is represented in the brain.
MedCare: Advancing Medical LLMs through Decoupling Clinical Alignment and Knowledge Aggregation
Based on this two-stage paradigm, we proposed a Medical LLM through decoupling Clinical Alignment and Knowledge Aggregation (MedCare), which is designed to achieve state-of-the-art (SOTA) performance on over 20 medical tasks, as well as SOTA results on specific medical alignment tasks.
TAIA: Large Language Models are Out-of-Distribution Data Learners
However, in certain specialized domains, such as healthcare or harmless content generation, it is nearly impossible to obtain a large volume of high-quality data that matches the downstream distribution.
MING-MOE: Enhancing Medical Multi-Task Learning in Large Language Models with Sparse Mixture of Low-Rank Adapter Experts
Large language models like ChatGPT have shown substantial progress in natural language understanding and generation, proving valuable across various disciplines, including the medical field.
Automatic Interactive Evaluation for Large Language Models with State Aware Patient Simulator
Large Language Models (LLMs) have demonstrated remarkable proficiency in human interactions, yet their application within the medical field remains insufficiently explored.
Leveraging Diverse Modeling Contexts with Collaborating Learning for Neural Machine Translation
Autoregressive (AR) and Non-autoregressive (NAR) models are two types of generative models for Neural Machine Translation (NMT).
MM-SAP: A Comprehensive Benchmark for Assessing Self-Awareness of Multimodal Large Language Models in Perception
We believe that these hallucinations are partially due to the models' struggle with understanding what they can and cannot perceive from images, a capability we refer to as self-awareness in perception.
An Automatic Evaluation Framework for Multi-turn Medical Consultations Capabilities of Large Language Models
A medical consultation training set is further constructed to improve the consultation ability of LLMs.
Deep learning for detection and segmentation of artefact and disease instances in gastrointestinal endoscopy
The Endoscopy Computer Vision Challenge (EndoCV) is a crowd-sourcing initiative to address eminent problems in developing reliable computer aided detection and diagnosis endoscopy systems and suggest a pathway for clinical translation of technologies.
