A group of researchers wrote a review for Neuroinformatics, a neuroscience research, detailing how machine learning can be used to aid neuroimaging and improve brain health:
Machine learning is contributing to rapid advances in clinical translational imaging to enable early detection, prediction, and treatment of diseases that threaten brain health. Brain diseases, including cerebrovascular disease, depression, migraine headaches, and dementia, are leading causes of global disability (Vos et al., 2020). Continued progress in neuroimaging and machine learning, and the collection of increasingly large-scale data sets, promise to transform healthcare by providing non-invasive, reliable indicators of brain health, resilience, and vulnerability long before clinical manifestations of disease. But many technical challenges remain.
As the journal article explains, there are technical and ethical challenges involved with this work but the purpose is to “educate, motivate, and inspire graduate students, postdoctoral fellows, and early career investigators to contribute to a future where neuroimaging meaningfully contributes to the maintenance of brain health.” The ethical issues mentioned in the article were:
- Data Sharing
- Informed Consent for Expanded or Later Use of Data
- Intellectual Property and Commercialization
- Bias in Datasets
- Quantifying/understanding Uncertainty
Glad to see dataset bias getting a mention amongst them. The authors offered considerations at the ML deployment stage but you’ll have to read to find out what they were.