Summary of Performance Evaluation Of Predictive Ai Models to Support Medical Decisions: Overview and Guidance, by Ben Van Calster et al.
Performance evaluation of predictive AI models to support medical decisions: Overview and guidance
by Ben Van Calster, Gary S. Collins, Andrew J. Vickers, Laure Wynants, Kathleen F. Kerr, Lasai Barreñada, Gael Varoquaux, Karandeep Singh, Karel G. M. Moons, Tina Hernandez-boussard, Dirk Timmerman, David J. Mclernon, Maarten Van Smeden, Ewout W. Steyerberg
First submitted to arxiv on: 13 Dec 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Methodology (stat.ME); Machine Learning (stat.ML)
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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 A novel study evaluates the performance of predictive AI models in medical practice by assessing various metrics. The research focuses on binary outcome models and identifies 32 measures across five domains: discrimination, calibration, overall, classification, and clinical utility. The findings highlight the importance of considering misclassification costs when selecting performance measures. Specifically, 17 measures exhibit proper characteristics, while 14 exhibit one characteristic, and only one measure lacks both. The study recommends a set of essential metrics to report, including AUROC, calibration plots, net benefit with decision curve analysis, and probability distributions per outcome category. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Predictive AI models are used in medical practice to make predictions about patient outcomes. But how do we know if these models are accurate? The answer lies in the performance measures we use to evaluate them. This study looks at different metrics that can help us assess the quality of predictive AI models. It focuses on models that have a binary outcome, like “diagnosed with cancer” or “not diagnosed.” The researchers identify 32 measures that fall into five categories: how well the model discriminates between outcomes, how well it’s calibrated to real-world data, and so on. They also explore what makes some measures better than others when it comes to making decisions about patients. |
Keywords
» Artificial intelligence » Classification » Probability
