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Found 10 papers, 1 papers with code
Pushing Boundaries: Exploring Zero Shot Object Classification with Large Multimodal Models
To bolster our analysis, we assess the model's performance post fine-tuning for specific tasks.
Can ChatGPT be Your Personal Medical Assistant?
This study aims to evaluate the potential of using a fine-tuned ChatGPT model as a personal medical assistant in the Arabic language.
Improving diagnosis and prognosis of lung cancer using vision transformers: A scoping review
This review provides valuable insights for researchers in the field of AI and healthcare to advance the state-of-the-art in lung cancer diagnosis and prognosis.
Artificial Intelligence-Based Methods for Precision Medicine: Diabetes Risk Prediction
This review provides insights into the current state and limitations of AI-based T2DM risk prediction models and highlights challenges for their development and clinical implementation.
Leveraging GANs for data scarcity of COVID-19: Beyond the hype
These recommendations might be useful to improve acceptability for the GAN-based approaches for data augmentation as GANs for data augmentation are increasingly becoming popular in the AI and medical imaging research community.
Brain Tumor Synthetic Data Generation with Adaptive StyleGANs
Furthermore, the model can generate high-quality synthetic brain MRI with a tumor that can limit the small sample size issues.
Spot the fake lungs: Generating Synthetic Medical Images using Neural Diffusion Models
In this work, we explore the possibilities of synthesis of medical images using neural diffusion models.
Artificial Intelligence-Based Methods for Fusion of Electronic Health Records and Imaging Data
Specifically, early fusion was the most used technique in most applications for multimodal learning (22 out of 34 studies).
Combating COVID-19 using Generative Adversarial Networks and Artificial Intelligence for Medical Images: A Scoping Review
This review included 57 full-text studies that reported the use of GANs for different applications in COVID-19 lungs images data.
Modeling Spatiotemporal Factors Associated With Sentiment on Twitter: Synthesis and Suggestions for Improving the Identification of Localized Deviations
Background: Studies examining how sentiment on social media varies depending on timing and location appear to produce inconsistent results, making it hard to design systems that use sentiment to detect localized events for public health applications.
