Summary of Evaluation Of Predictive Reliability to Foster Trust in Artificial Intelligence. a Case Study in Multiple Sclerosis, by Lorenzo Peracchio et al.
Evaluation of Predictive Reliability to Foster Trust in Artificial Intelligence. A case study in Multiple Sclerosis
by Lorenzo Peracchio, Giovanna Nicora, Enea Parimbelli, Tommaso Mario Buonocore, Roberto Bergamaschi, Eleonora Tavazzi, Arianna Dagliati, Riccardo Bellazzi
First submitted to arxiv on: 27 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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| 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 method assesses the reliability of machine learning (ML) predictions, which is crucial when using AI-driven clinical decisions. The approach evaluates whether an instance is from the same distribution as the training set using Autoencoders (AEs), and whether the ML classifier performs well on similar instances. This reliability measure can help decision-makers accept or reject predictions based on their trustworthiness. The method was tested in a simulated scenario and on a model predicting Multiple Sclerosis disease progression, demonstrating its effectiveness. A Python package called relAI embeds these reliability measures into ML pipelines, enabling clinicians to spot potential failures during deployment. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A team of researchers has developed a way to make sure artificial intelligence (AI) predictions are reliable in important fields like medicine. This is super important because AI mistakes can have serious consequences. They’re trying to figure out when an AI prediction might be wrong and when it’s trustworthy. To do this, they’re using special tools called Autoencoders to check if the new information being predicted is similar to what the AI learned from before. If it’s not, that means the prediction might be unreliable. They also tested their method on a project predicting how multiple sclerosis progresses in patients and found it worked well. This could help doctors make better decisions by spotting when an AI prediction isn’t reliable. |
Keywords
* Artificial intelligence * Machine learning
