Key result
Explainable Artificial Intelligence (XAI) provides a promising framework to derive mechanistic insights from complex neural data and improve closed-loop behavioral neurostimulation for psychiatric disorders.
Why the study?
Despite AI's accurate predictions in neuroscience, it largely lacks the ability to provide a mechanistic understanding of how inputs and outputs relate to each other.
Explainable Artificial Intelligence (XAI) has the potential to provide mechanistic insights into brain function and improve closed-loop neurostimulation for psychiatric and neurological disorders.
XAI merits testing in closed-loop psychiatric neurostimulation; leaves open need for prospective clinical validation.
The use of Artificial Intelligence and machine learning in basic research and clinical neuroscience is increasing. AI methods enable the interpretation of large multimodal datasets that can provide unbiased insights into the fundamental principles of brain function, potentially paving the way for earlier and more accurate detection of brain disorders and better informed intervention protocols. Despite AI's ability to create accurate predictions and classifications, in most cases it lacks the ability to provide a mechanistic understanding of how inputs and outputs relate to each other. Explainable Artificial Intelligence (XAI) is a new set of techniques that attempts to provide such an understanding, here we report on some of these practical approaches. We discuss the potential value of XAI to the field of neurostimulation for both basic scientific inquiry and therapeutic purposes, as well as, outstanding questions and obstacles to the success of the XAI approach.
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Fellous et al. (2019) conducted a review in Neuropsychiatric conditions. Explainable Artificial Intelligence (XAI) was evaluated. Explainable Artificial Intelligence (XAI) provides a promising framework to derive mechanistic insights from complex neural data and improve closed-loop behavioral neurostimulation for psychiatric disorders.
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