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Machine learning models are increasingly being deployed in real-world clinical settings and have shown promise in patient diagnosis, treatment and outcome tasks. However, such models have also been shown to exhibit biases towards specific demographic groups, leading to inequitable outcomes for under-represented or historically marginalized communities.
Thriving in academia with a disability sparks creativity and innovation. My experience in chemistry and pharmacy laboratories highlights the need for universities to establish inclusive environments with clear, effective accommodations. I advocate for inclusivity, urging academia to support all researchers to excel and making science accessible to everyone.
Whole-brain modelling is an essential tool that provides relevant insights for neuroscientists as they work to discover the fundamental principles of healthy brain function.
Single-case experimental designs are rapidly growing in popularity. This popularity needs to be accompanied by transparent and well-justified methodological and statistical decisions. Appropriate experimental design including randomization, proper data handling and adequate reporting are needed to ensure reproducibility and internal validity. The degree of generalizability can be assessed through replication.
