Retrieval-Augmented Generation for Survival Analysis in Cancers
Abstract
Healthcare is one of the most important fields that benefits from advancements in Artificial Intelligence (AI). From classic models like linear regression to cuttingedge transformers, AI is applied across various healthcare subdomains, such as drug discovery, predictive analytics, and personalized medicine, to name a few. These techniques enable medical practitioners to make more informed decisions, significantly improving both the speed and accuracy of diagnoses and treatments. Machine learning has played a transformative role in oncology, especially in areas like early detection, diagnosis, treatment planning, and patient monitoring, by analyzing medical images, clinical information, genomic data, sensor information. Our research aims to develop a solution for predicting cancer patient survivability using an emerging approach in AI, called Generative AI. Specifically, we will leverage Large Language Models, a powerful subset of Generative AI, including other facets of AI, namely Natural Language Processing (NLP), Machine Learning (ML), and Rule based systems. We also look into how Retrieval Augmented Generation can be used to derive insights from larger datasets, followed by evaluation of the responses given by LLMs, using the LLM as a Judge approach. This research enables us to capture insights from unstructured data that can go unnoticed in traditional machine learning pipelines. By integrating generative models with retrieval mechanisms, our goal is to deliver more…
Read the paper
Related studies
- Knowledge Management Perspective of Generative Artificial Intelligence
- Navigating Generative Artificial Intelligence Promises and Perils for Knowledge and Creative Work
- Characterizing the evolving research on enterprise content management
- Large language models and multimodal foundation models for precision oncology
- From Generative AI to Extended Reality: Multidisciplinary Perspectives on the Challenges, Opportunities, and Future of Educational Computing
- Generative Artificial Intelligence: A New Engine for Advancing Environmental Science and Engineering
Go deeper in Evidence Hub
Evidence Hub adds a study data sheet (design, sample size, evidence level, key numbers), an AI summary and a chat grounded in the paper's text.