| Xu et al. (2019) [9] |
Magnetic resonance imaging and AI for Parkinson’s disease diagnosis |
71 |
Systematic review |
1990–2019 |
To review studies in three subfields: diagnosis, differential diagnosis, and subtyping of Parkinson’s disease, to depict the general workflow from magnetic resonance image to classification results, and to summarize an essential assessment of the recent research and to offer suggestions for future research. |
| Shaver et al. (2019) [7] |
Deep learning approaches for glioma imaging |
12 |
Systematic review |
2009–2018 |
To summarize recent applications of deep learning to detect glioma and predict outcome, with foci on pre- and post-operative tumor segmentation, genetic characterization of tissue, and prognostication. |
| Sakai, K and Yamada (2019) [10] |
Machine learning studies on major brain diseases |
209 |
Systematic review |
2014–2018 |
To summarize detailed information such as machine learning approaches, sample size, inputted features types and reported accuracy. |
| Kamal et al. (2018) [8] |
Machine learning in acute ischemic stroke neuroimaging |
10 |
Systematic review |
2011–2018 |
To summarize detailed information such as machine learning approaches, features, and results. |
| Senders et al. (2018) [11] |
Machine learning for predicting neurosurgical outcome |
30 |
Systematic review |
1998–2017 |
To offer an overview of the theoretical concepts of machine learning and to examine its usefulness to assist neurosurgical decision making, and to compare the performance of machine learning with prognostic indices, traditional statistical approaches, and clinical experts. |
| Lee et al. (2017) [6] |
AI in stroke imaging |
49 |
Systematic review |
till 2017 |
To provide an overview of the applications of AI in stroke imaging, with particular foci on technical principles, clinical applications, and future perspectives. |
| Sotoudeh et al. (2019) |
AI in the management of glioma |
84 |
Systematic review |
till 2019 |
To offer a succinct depiction of the foundational concepts of AI techniques and their relevance to clinical medicine, and to review innovative AI techniques in glioma diagnosis and management. |
| Sotoudeh et al. (2019) [12] |
AI for mental health and mental illnesses |
28 |
Systematic review |
2015–2019 |
To review AI’s applications in healthcare, to discuss how AI could facilitate clinical practice, issues requiring further study, and ethical implications concerning AI technologies. |
| Aneja et al. (2019) [13] |
Artificial intelligence in neuro-oncology |
27 |
Systematic review |
2017–2019 |
To discuss current adoption of AI within neuro-oncology and to demonstrate emerged challenges in the integration of AI in clinical practice. |
| Senders et al. (2018) [14] |
Machine learning in neurosurgical care |
221 |
Systematic review |
till 2017 |
To summarize detailed information such as treatment stages, disease conditions machine learning methods inputted features neurosurgical applications, and results. |
| Hassabis et al. (2017) [3] |
Neuroscience-inspired AI |
187 |
Systematic review |
till 2017 |
To review interactions between AI and neuroscience and to demonstrate latest progresses in AI motivated by research of neural computations. |
| Chen et al. (2019) [15] |
Human brain study using AI |
6317 |
bibliometric analysis |
2009–2018 |
To analyze distributions of annual article and citation counts, identify productive journals and institutions, visualize scientific collaborations, and to uncover the most frequently used keywords. |
