Is there a gender gap in clinical neurosciences? A cross-sectional analysis of female participation in academic neurology, neurosurgery, and psychiatry.

Gender equality or the lack thereof is a constantly recurring theme. Here, we sought to provide an overview of the status with respect to the participation and leadership of female doctors in clinical neuroscience analyzing different disciplines (neurosurgery, neurology and psychiatry). A total of 1910 articles published in six representative journals (07-12/2020) were reviewed.

Path to success: female leaders in German neurosurgery.

Despite advances in gender equality, only 6% of German neurosurgical departments are currently led by women. With regard to their pioneering work and the importance of their role model effect, we aimed at reporting on the career pathways of the present and former female chairs of neurosurgical departments in Germany. We approached current and former female chairs in German neurosurgery and gathered descriptive information on their ways into leadership positions through structured interviews.

The Artificial Intelligence Doctor: Considerations for the Clinical Implementation of Ethical AI.

The applications of artificial intelligence (AI) and machine learning (ML) in modern medicine are growing exponentially, and new developments are fast-paced. However, the lack of trust and appropriate legislation hinder its clinical implementation. Recently, there is a clear increase of directives and considerations on Ethical AI.

Foundations of Time Series Analysis.

For almost a century, classical statistical methods including exponential smoothing and autoregression integrated moving averages (ARIMA) have been predominant in the analysis of time series (TS) and in the pursuit of forecasting future events from historical data. TS are chronological sequences of observations, and TS data are therefore prevalent in many aspects of clinical medicine and academic neuroscience. With the rise of highly complex and nonlinear datasets, machine learning (ML) methods have become increasingly popular for prediction or pattern detection and within neurosciences, including neurosurgery.

Introduction to Machine Learning in Neuroimaging.

Advancements in neuroimaging and the availability of large-scale datasets enable the use of more sophisticated machine learning algorithms. In this chapter, we non-exhaustively discuss relevant analytical steps for the analysis of neuroimaging data using machine learning (ML), while the field of radiomics will be addressed separately (c.f.