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ISSN: 2950-1628

Prompt engineering for healthcare: Methodologies and applications

Prompt engineering is a critical technique in the field of natural language processing (NLP) that involves designing and optimizing the prompts used to input information into models, aiming to enhance...

Summary of ChatGPT-Related research and perspective towards the future of large language models

This paper presents a comprehensive survey of ChatGPT-related (GPT-3.5 and GPT-4) research, state-of-the-art large language models (LLM) from the GPT series, and their prospective applications across...

R2GenGPT: Radiology Report Generation with frozen LLMs

Large Language Models (LLMs) have consistently showcased remarkable generalization capa-bilities when applied to various language tasks. Nonetheless, harnessing the full potential of LLMs for Radiology...

A review of uncertainty estimation and its application in medical imaging

The use of AI systems in healthcare for the early screening of diseases is of great clinical importance. Deep learning has shown great promise in medical imaging, but the reliability and trustworthiness...

When brain-inspired AI meets AGI

Artificial General Intelligence (AGI) has been a long-standing goal of humanity, with the aim of creating machines capable of performing any intellectual task that humans can do. To achieve this, AGI...

Toward vibe medicine: A self-evolving multi-agent framework for clinical decision support

In recent years, the advances of large language models and autonomous agents have revolutionized the healthcare field, facilitating diagnosis and improving treatment results. However, most existing...

A comprehensive survey of ChatGPT: Advancements, applications, prospects, and challenges

Large Language Models (LLMs) especially when combined with Generative Pre-trained Transformers (GPT) represent a groundbreaking in natural language processing. In particular, ChatGPT, a state-of-the-art...

Quantum artificial intelligence: A comprehensive survey

Quantum Artificial Intelligence (QAI) has emerged at the nexus of quantum computing and AI, promising to redefine computational frontiers. This survey critically synthesizes the state-of-the-art through...

Integrating AI in college education: Positive yet mixed experiences with ChatGPT

The integration of artificial intelligence (AI) chatbots into higher education marks a shift towards a new generation of pedagogical tools, mirroring the arrival of milestones like the internet. With...

A review of dose prediction methods for tumor radiation therapy

Radiation therapy (RT) is currently the main clinical treatment of tumors. Before treatment initiation, precise delineation of the planned target volume (PTV) and organs at risk (OAR) is essential....

Thoracic CT imaging in obesity: Technical challenges, imaging findings and future outlook

Obesity is a highly prevalent and increasing global medical problem. It is expected that most radiologists will come across computed tomography studies of obese patients in their daily work. Obesity...

Large-Scale AI and Foundation Models for Neuroscience: A Comprehensive Review

The development of large-scale artificial intelligence (AI) models is influencing neuroscience research by enabling end-to-end learning from raw brain signals and neural data. In this paper, we review...

Cardiac ECV mapping: Underlying concepts and clinical applications

Myocardial Extracellular volume fraction (ECV) mapping, based on cardiac magnetic resonance (CMR), is a crucial technique for assessing myocardial histological changes by evaluating extracellular matrix...

Diagnostic accuracy of artificial intelligence models in ultrasound-based detection of abdominal trauma: A systematic review and meta-analysis

Abdominal trauma detection via ultrasound, particularly through Focused Assessment with Sonography for Trauma (FAST), is a cornerstone of emergency medicine due to its portability, real-time imaging...

Review of large vision models and visual prompt engineering

Visual prompt engineering is a fundamental methodology in the field of visual and image artificial general intelligence. As the development of large vision models progresses, the importance of prompt...

An umbrella review of the efficacy of different physical therapy modalities for post-traumatic stress disorder (PTSD)

This study aimed to assess the efficacy of different physiotherapy modalities for the treatment of post-traumatic stress disorder using an umbrella review. As of June 2024, we have published evidence...

Rethinking the studies of diagnostic biomarkers for mental disorders

For mental disorders, the identification of biomarkers with high specificity, sensitivity, and validity remains a major challenge due to their heterogeneity and symptomatic overlap across disorders....

Vision transformers in multi-modal brain tumor MRI segmentation: A review

Brain tumors have shown extreme mortality and increasing incidence during recent years, which bring enormous challenges for the timely diagnosis and effective treatment of brain tumors. Concretely,...

The general intelligence of GPT–4, its knowledge diffusive and societal influences, and its governance

Recent breakthroughs in artificial intelligence (AI) research include advancements in natural language processing (NLP) achieved by large language models (LLMs), and; in particular, generative pre–trained...

Radiology-GPT: A large language model for radiology

We introduce Radiology-GPT, a large language model for radiology. Using an instruction tuning approach on an extensive dataset of radiology domain knowledge, Radiology-GPT demonstrates superior performance...

Advances in neuroimaging applications of quantitative susceptibility mapping

This review article delves into the advancements of quantitative susceptibility mapping (QSM) in neuroimaging, highlighting its utility in detecting and quantifying magnetic susceptibility differences...

Assessment of Myocardial Stiffness in Healthy Adults by Magnetic Resonance Elastography: Effects of Slice Location and Demographic Factors

To establish normal myocardial stiffness values in healthy adults using cardiac MR elastography (MRE) and assess the impact of slice location and demographic factors....

BrainMCLIP: Brain Image Decoding with Multi-Layer feature Fusion of CLIP

Decoding images from fMRI often involves mapping brain activity to CLIP’s final semantic layer. To capture finer visual details, many approaches add a parameter-intensive VAE-based pipeline. However,...

Chest X-ray foundation models: A survey and future directions

Recent years have seen an increase in the development of foundation models for chest X-ray (CXR) analysis. Such foundation models provide robust, generalizable feature extraction abilities and allow...

Potential of multimodal large language models for data mining of medical images and free-text reports

Medical images and radiology reports are essential for physicians to diagnose medical conditions. However, the vast diversity and cross-source heterogeneity inherent in these data have posed significant...

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