Summary of Hulp: Human-in-the-loop For Prognosis, by Muhammad Ridzuan et al.
HuLP: Human-in-the-Loop for Prognosis
by Muhammad Ridzuan, Mai Kassem, Numan Saeed, Ikboljon Sobirov, Mohammad Yaqub
First submitted to arxiv on: 19 Mar 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Human-Computer Interaction (cs.HC)
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 The proposed HuLP model is a Human-in-the-Loop for Prognosis framework that seeks to improve the reliability and interpretability of prognostic models in clinical settings. By allowing human experts to interact with and correct AI-driven predictions, HuLP aims to foster collaboration between humans and machines to produce more accurate prognosis. The model addresses missing data challenges through neural networks and a tailored methodology for imputing missing covariates based on imaging features. Experimental results on two publicly available medical datasets demonstrate the competitiveness of HuLP. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary HuLP is a new way to make AI-powered doctor predictions better. Right now, doctors and AI computers don’t work together well when making prognosis predictions. This paper shows how HuLP can help by letting doctors correct AI mistakes. The model also fixes problems with missing data by using special imaging features. Scientists tested HuLP on real medical data and it worked really well. |
