# Rapid Review of Generative AI in Smart Medical Applications

arXiv: https://arxiv.org/abs/2406.06627 · HTML: https://mlsanity.com/paper/2406.06627/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 65 of its 133 references that are in the map.
- First posted on arXiv 8 Jun 2024.

## Builds on (most cited first, 30 of 65)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Generative Adversarial Networks](https://mlsanity.com/paper/2203.00667/) (2022)
- [Auto-Encoding Variational Bayes](https://mlsanity.com/paper/1312.6114/) (2013)
- [On the Opportunities and Risks of Foundation Models](https://mlsanity.com/paper/2108.07258/) (2021)
- [Progressive Growing of GANs for Improved Quality, Stability, and Variation](https://mlsanity.com/paper/1710.10196/) (2017)
- [Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks](https://mlsanity.com/paper/1511.06434/) (2015)
- [Glow: Generative Flow with Invertible 1x1 Convolutions](https://mlsanity.com/paper/1807.03039/) (2018)
- [Adversarial Discriminative Domain Adaptation](https://mlsanity.com/paper/1702.05464/) (2017)
- [Improving Variational Inference with Inverse Autoregressive Flow](https://mlsanity.com/paper/1606.04934/) (2016)
- [Wasserstein GAN](https://mlsanity.com/paper/1701.07875/) (2017)
- [Weight Uncertainty in Neural Networks](https://mlsanity.com/paper/1505.05424/) (2015)
- [BioGPT: Generative Pre-trained Transformer for Biomedical Text Generation and Mining](https://mlsanity.com/paper/2210.10341/) (2022)
- [Domain Adaptive Faster R-CNN for Object Detection in the Wild](https://mlsanity.com/paper/1803.03243/) (2018)
- [Tutorial on Variational Autoencoders](https://mlsanity.com/paper/1606.05908/) (2016)
- [Adversarial Autoencoders](https://mlsanity.com/paper/1511.05644/) (2015)
- [On the Automatic Generation of Medical Imaging Reports](https://mlsanity.com/paper/1711.08195/) (2017)
- [Variational Lossy Autoencoder](https://mlsanity.com/paper/1611.02731/) (2016)
- [Exploiting Spatial Dimensions of Latent in GAN for Real-time Image Editing](https://mlsanity.com/paper/2104.14754/) (2021)
- [Generative AI for Medical Imaging: extending the MONAI Framework](https://mlsanity.com/paper/2307.15208/) (2023)
- [PathAsst: A Generative Foundation AI Assistant Towards Artificial General Intelligence of Pathology](https://mlsanity.com/paper/2305.15072/) (2023)
- [Variational Approaches for Auto-Encoding Generative Adversarial Networks](https://mlsanity.com/paper/1706.04987/) (2017)
- [Navigating the GAN Parameter Space for Semantic Image Editing](https://mlsanity.com/paper/2011.13786/) (2020)
- [From optimal transport to generative modeling: the VEGAN cookbook](https://mlsanity.com/paper/1705.07642/) (2017)
- [Using StyleGAN for Visual Interpretability of Deep Learning Models on Medical Images](https://mlsanity.com/paper/2101.07563/) (2021)
- [Cascaded Latent Diffusion Models for High-Resolution Chest X-ray Synthesis](https://mlsanity.com/paper/2303.11224/) (2023)
- [Learning a Continuous Representation of 3D Molecular Structures with Deep Generative Models](https://mlsanity.com/paper/2010.08687/) (2020)
- [Trust and Medical AI: The challenges we face and the expertise needed to overcome them](https://mlsanity.com/paper/2008.07734/) (2020)
- [High-resolution medical image synthesis using progressively grown generative adversarial networks](https://mlsanity.com/paper/1805.03144/) (2018)
- [Aligning Synthetic Medical Images with Clinical Knowledge using Human Feedback](https://mlsanity.com/paper/2306.12438/) (2023)
- [Generation of Synthetic Electronic Medical Record Text](https://mlsanity.com/paper/1812.02793/) (2018)

## Built on by (most cited first, 2 of 2)

- [Enhancing Healthcare through Large Language Models: A Study on Medical Question Answering](https://mlsanity.com/paper/2408.04138/) (2024)
- [Advanced AI Framework for Enhanced Detection and Assessment of Abdominal Trauma: Integrating 3D Segmentation with 2D CNN and RNN Models](https://mlsanity.com/paper/2407.16165/) (2024)

## Abstract (arXiv)

With the continuous advancement of technology, artificial intelligence has significantly impacted various fields, particularly healthcare. Generative models, a key AI technology, have revolutionized medical image generation, data analysis, and diagnosis. This article explores their application in intelligent medical devices. Generative models enhance diagnostic speed and accuracy, improving medical service quality and efficiency while reducing equipment costs. These models show great promise in medical image generation, data analysis, and diagnosis. Additionally, integrating generative models with IoT technology facilitates real-time data analysis and predictions, offering smarter healthcare services and aiding in telemedicine. Challenges include computational demands, ethical concerns, and scenario-specific limitations.