首页|New Findings from San Raffaele Roma Open University Update Understanding of Arti ficial Intelligence (Evaluating Explainable Machine Learning Models for Clinicia ns)
New Findings from San Raffaele Roma Open University Update Understanding of Arti ficial Intelligence (Evaluating Explainable Machine Learning Models for Clinicia ns)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Investigators discuss new findings in Artificial Intelligence. According to news originating from Rome, Italy, by News Rx correspondents, research stated, "Gaining clinicians' trust will unleash the full potential of artificial intelligence (AI) in medicine, and explaining AI de cisions is seen as the way to build trustworthy systems. However, explainable ar tificial intelligence (XAI) methods in medicine often lack a proper evaluation." Financial support for this research came from HORIZON EUROPE Health. Our news journalists obtained a quote from the research from San Raffaele Roma O pen University, "In this paper, we present our evaluation methodology for XAI me thods using forward simulatability. We define the Forward Simulatability Score ( FSS) and analyze its limitations in the context of clinical predictors. Then, we applied FSS to our XAI approach defined over an ML-RO, a machine learning clini cal predictor based on random optimization over a multiple kernel support vector machine (SVM) algorithm.To Compare FSS values before and after the explanation phase, we test our evaluation methodology for XAI methods on three clinical dat asets, namely breast cancer, VTE, and migraine. The ML-RO system is a good model on which to test our XAI evaluation strategy based on the FSS. Indeed, ML-RO ou tperforms two other base models-a decision tree (DT) and a plain SVM-in the thre e datasets and gives the possibility of defining different XAI models: TOPK, MIG F, and F4G. The FSS evaluation score suggests that the explanation method F4G fo r the ML-RO is the most effective in two datasets out of the three tested, and i t shows the limits of the learned model for one dataset. Our study aims to intro duce a standard practice for evaluating XAI methods in medicine."
RomeItalyEuropeArtificial Intellig enceCyborgsEmerging TechnologiesMachine LearningSan Raffaele Roma Open U niversity
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