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http://hdl.handle.net/1942/45991| Title: | Strategies for mitigating data heterogeneities in AI-based neuro-disease detection | Authors: | Leming, Matthew Kim, Kyungsu BRUFFAERTS, Rose Im, Hyungsoon |
Issue Date: | 2025 | Publisher: | CELL PRESS | Source: | Neuron, 113 (8) , p. 1129 -1132 | Abstract: | In this NeuroView, we discuss challenges and best practices when dealing with disease-detection AI models that are trained on heterogeneous clinical data, focusing on the interrelated problems of model bias, causality, and rare diseases. | Notes: | Im, H (corresponding author), Massachusetts Gen Hosp, Ctr Syst Biol, Boston, MA 02114 USA.; Im, H (corresponding author), Massachusetts Gen Hosp, Massachusetts Alzheimers Dis Res Ctr, Boston, MA 02114 USA.; Im, H (corresponding author), Massachusetts Gen Hosp, Dept Radiol, Boston, MA 02114 USA. im.hyungsoon@mgh.harvard.edu |
Keywords: | Humans;Nervous System Diseases;Artificial Intelligence | Document URI: | http://hdl.handle.net/1942/45991 | ISSN: | 0896-6273 | e-ISSN: | 1097-4199 | DOI: | 10.1016/j.neuron.2025.01.028 | ISI #: | 001473755700001 | Rights: | 2025 Elsevier Inc. All rights are reserved, including those for text and data mining, AI training, and similar technologies. | Category: | A1 | Type: | Journal Contribution |
| Appears in Collections: | Research publications |
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| Strategies for mitigating data heterogeneities in AI-based neuro-disease detection.pdf Restricted Access | Published version | 1.76 MB | Adobe PDF | View/Open Request a copy |
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