News

• Invited Speaker lineup published.
• Accepted paper list published. See Papers and Program announced
• Paper acceptance notification updated to Jul 07, 2023
• Paper submission deadline extended to Jun 15, 2023. See updated dates

Overview

Generative models have a long history and there are many application areas in medical machine learning (ML) and artificial intelligence (AI). With the development in deep neural networks, researchers focused on Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), and autoregressive models in the past years. More recently, very large deep generative models have gained popularity, including the large language models (LLMs) such as Generative Pre-trained Transformer 3 (GPT-3) and text-to-image diffusion models such as Stable Diffusion. In healthcare research, one of the most common applications of generative models has been the generation of synthetic data for training of machine learning models. It is often used to increase representation of patient subgroups to improve generalization and mitigate algorithmic biases. This is especially valuable in application domains where data is hard to come by. The generative models can also be used for specific model evaluation purposes (e.g., within a robustness or generalizability assessment; virtual clinical trials). They can help to generate synthetic ground truth data when labeling of data is extremely burdensome. Moreover, generative models have been successfully applied in data preprocessing or enhancement, such as image reconstruction or denoising deep learning algorithms in the medical imaging space. While such generative models have proven their utility in the health domain, many open questions remain with regard to the approaches for evaluation of their effectiveness and safety. Testing and evaluation of such models require specific considerations. Taking the assessment of the gap between the generated data and the reality — so called Sim2Real challenge — as an example, it is often unclear how to (i) quantify this domain gap and its impact on downstream performance in a meaningful manner and (ii) reduce it in order to fully leverage the potential of generative models. New challenges are also emerging on a more grand scale. The recent advances in Large Language Models (LLMs) makes the generation of data even more effortless. However, the misinformation that is generated with such models may cause a “pollution” of data for future model training. We can expect an increased need for effective fact checking approaches. Despite the huge growth of this area of research, the actual use of NLP technology for fact checking is still in its infancy. In this one day workshop we would like to discuss some of the most common applications of generative models in the ML/AI research in the healthcare domain, the current challenges and also explore what are the potential new areas of application

Previous Iterations

• KDD Health Day - DSHealth 2022: 2022 KDD Workshop on Applied Data Science for Healthcare: Transparent and Human-centered AI
• KDD Health Day - DSHealth 2021: Joint KDD 2021 Health Day and 2021 KDD Workshop on Applied Data Science for Healthcare State of XAI and trustworthiness in Health
• DSHealth 2020: 2020 KDD Workshop on Applied Data Science for Healthcare: Trustable and Actionable AI for Healthcare
• DSHealth 2019: 2019 KDD Workshop on Applied Data Science for Healthcare: Bridging the Gap between Data and Knowledge
• MLMH 2018: 2018 KDD Workshop on Machine Learning for Medicine and Healthcare