Latest AI and machine learning research in medical education for healthcare professionals.
Recent advances in reinforcement learning with verifiable, rule-based rewards have greatly enhanced the reasoning capabilities and out-of-distribution generalization of VLMs/LLMs, obviating the need for manually crafted reasoning chains. Despite these promising developments in the general domain, their translation to medical imaging remains limited. Current medical reinforcement fine-tuning (RFT...
Effective teaching requires adapting instructional strategies to accommodate the diverse cognitive and behavioral profiles of students, a persistent challenge in education and teacher training. While Large Language Models (LLMs) offer promise as tools to simulate such complex pedagogical environments, current simulation frameworks are limited in two key respects: (1) they often reduce students t...
Natural images exhibit label diversity (clean vs. noisy) in noisy-labeled image classification and prevalence diversity (abundant vs. sparse) in lon...
Current Large Language Models (LLMs) exhibit significant limitations, notably in structured, interpretable, and verifiable medical reasoning, alongs...
Current Large Language Models (LLMs) exhibit significant limitations, notably in structured, interpretable, and verifiable medical reasoning, alongs...
Approximate computing is an effective computing paradigm for improving energy efficiency of error-tolerant applications. Approximate logic synthesis...
How to design reinforcement learning (RL) tasks that effectively unleash the reasoning capability of large language models (LLMs) remains an open qu...
Discovering regularities from spatiotemporal systems can benefit various scientific and social planning. Current spatiotemporal learners usually tra...
Real-world multimodal systems routinely face missing-input scenarios, and in reality, robots lose audio in a factory or a clinical record omits lab ...
Objective: As AI becomes increasingly central to healthcare, there is a pressing need for bioinformatics and biomedical training systems that are pe...
Vision-language-action (VLA) models have shown promise as generalist robotic policies by jointly leveraging visual, linguistic, and proprioceptive m...
Transcranial focused ultrasound (tFUS) is an emerging modality for non-invasive brain stimulation and therapeutic intervention, offering millimeter-...
Open-domain dialogue systems aim to generate natural and engaging conversations, providing significant practical value in real applications such as ...
Self-Supervised Learning (SSL) has become a powerful solution to extract rich representations from unlabeled data. Yet, SSL research is mostly focus...
Large-scale pre-training using videos has proven effective for robot learning. However, the models pre-trained on such data can be suboptimal for ro...
With the proliferation of large language models (LLMs) in the medical domain, there is increasing demand for improved evaluation techniques to asses...
Medical reasoning in large language models (LLMs) aims to emulate clinicians' diagnostic thinking, but current benchmarks such as MedQA-USMLE, MedMC...
Background and Objective: Precise preoperative planning and effective physician training for coronary interventions are increasingly important. Desp...
The inherence of personality in human-robot interaction enhances conversational dynamics and user experience. The deployment of Chat GPT-4 within a co...
In this research work, we present our open-source Geant4-based Monte-Carlo simulation application, called RadField3D, for generating three-dimensional...