Summary of Reviewing Ai’s Role in Non-muscle-invasive Bladder Cancer Recurrence Prediction, by Saram Abbas et al.
Reviewing AI’s Role in Non-Muscle-Invasive Bladder Cancer Recurrence Prediction
by Saram Abbas, Rishad Shafik, Naeem Soomro, Rakesh Heer, Kabita Adhikari
First submitted to arxiv on: 15 Mar 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This comprehensive review paper critically analyzes machine learning (ML)-based frameworks for predicting Non-muscle-invasive Bladder Cancer (NMIBC) recurrence. The study examines the strengths and weaknesses of each framework by focusing on various prediction tasks, data modalities, and ML models, highlighting their remarkable performance alongside inherent limitations. Notably, a diverse array of ML algorithms leveraging multimodal data, including radiomics, clinical, histopathological, and genomic data, exhibit significant promise in accurately predicting NMIBC recurrence. However, the path to widespread adoption faces challenges concerning generalizability and interpretability of models, emphasizing the need for collaborative efforts, robust datasets, and cost-effectiveness considerations. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at how machine learning can help predict when bladder cancer will come back. Right now, doctors use scoring systems that don’t always get it right. Researchers are trying to find new ways using computers to look at different types of data like images, lab results, and genes. They’re finding some promising ideas, but there’s still a lot to figure out before these predictions can be trusted. It’s like solving a puzzle – they need to make sure the pieces fit together just right. |
Keywords
* Artificial intelligence * Machine learning
