FULL TEXT.  Requiring undergraduate students to perform what is termed original research for their thesis, an investigation that cannot constitute a replication of an existing study, is a failed opportunity for science and education, argues Daniel Quintana.

Requiring undergraduate students to perform what is termed original research for their thesis, an investigation that cannot constitute a replication of an existing study, is a failed opportunity for science and education.

The detection and removal of poor-quality data in a training set is crucial to achieve high-performing AI models. In healthcare, data can be inherently poor-quality due to uncertainty or subjectivity, but as is often the case, the requirement for data privacy restricts AI practitioners from accessing raw training data, meaning manual visual verification of private patient data is not possible. Here we describe a novel method for automated identification of poor-quality data, called Untrainable Data Cleansing. This method is shown to have numerous benefits including protection of private patient data; improvement in AI generalizability; reduction in time, cost, and data needed for training; all while offering a truer reporting of AI performance itself. Additionally, results show that Untrainable Data Cleansing could be useful as a triage tool to identify difficult clinical cases that may warrant in-depth evaluation or additional testing to support a diagnosis.

Interdisciplinarity is widely considered necessary to solving many contemporary problems, and new funding structures and instruments have been created to encourage interdisciplinary research at universities. This article looks at a small technical university specializing in green technology which implemented a strategy aimed at promoting and developing interdisciplinary collaboration.