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Article analysis:

The article provides a comprehensive overview of the use of machine learning-based early detection of Alzheimer's Disease using multi-modal neuroimaging data. The article is well written and provides an extensive review of the current research on the topic. It also outlines potential biological markers, multiple modalities of imaging, techniques of data pre-processing, classification algorithms, and challenges associated with the development of classification framework for the diagnosis of AD using multimodal data.

The article does not appear to be biased or one-sided in its reporting; it presents both sides equally by providing an overview of both traditional methods as well as modern machine learning approaches for diagnosing AD. Furthermore, it does not contain any promotional content or partiality towards any particular method or approach. The article also mentions possible risks associated with machine learning approaches such as overfitting and lack of interpretability but does not provide any further details on how these risks can be mitigated or avoided.

The article could have been improved by providing more detailed information on feature selection methods and scaling techniques used in neuroimaging field as well as exploring counterarguments against using machine learning approaches for diagnosing AD such as privacy concerns related to collecting large amounts of patient data for training ML models. Additionally, more evidence could have been provided to support the claims made in the article regarding the efficacy of ML approaches for diagnosing AD in its early stages.