IntroductionVirtual reality (VR) and augmented reality (AR) technologies are increasingly being used in undergraduate medical education. We aim to evaluate the effectiveness of VR and AR technologies for improving knowledge and skills in medical students.Methods and analysisUsing Best Evidence in Medical Education (BEME) collaboration guidelines, we will search MEDLINE (via PubMed), Education Resources Information Center, PsycINFO, Web of Knowledge, Embase and the Cochrane Central Register of Controlled Trials for English-language records, from January 1990 to March 2021. Randomised trials that studied the use of VR or AR devices for teaching medical students will be included. Studies that assessed other healthcare professionals, or did not have a comparator group, will be excluded. The primary outcome measures relate to medical students’ knowledge and clinical skills. Two reviewers will independently screen studies and assess eligibility based on our prespecified eligibility criteria, and then extract data from each eligible study using a modified BEME coding form. Any disagreements will be resolved by discussion or, if necessary, the involvement of a third reviewer. The BEME Quality Indicators checklist and the Cochrane Risk of Bias Tool will be used to assess the quality of the body of evidence. Where data are of sufficient homogeneity, a meta-analysis using a random-effects model will be conducted. Otherwise, a narrative synthesis approach will be taken and studies will be evaluated based on Kirkpatrick’s levels of educational outcomes and the Synthesis Without Meta-analysis guidelines.Ethics and disseminationEthical approval is not required for this systematic review as no primary data are being collected. We will disseminate the findings of this review through scientific conferences and through publication in a peer-reviewed journal.

Machine learning (ML)-based algorithms are playing an important role in cancer diagnosis and are increasingly being used to aid clinical decision-making. However, these commonly operate as ‘black boxes’ and it is unclear how decisions are derived. Recently, techniques have been applied to help us understand how specific ML models work and explain the rational for outputs. This study aims to determine why a given type of cancer has a certain phenotypic characteristic. Cancer results in cellular dysregulation and a thorough consideration of cancer regulators is required. This would increase our understanding of the nature of the disease and help discover more effective diagnostic, prognostic, and treatment methods for a variety of cancer types and stages. Our study proposes a novel explainable analysis of potential biomarkers denoting tumorigenesis in non-small cell lung cancer. A number of these biomarkers are known to appear following various treatment pathways. An enhanced analysis is enabled through a novel mathematical formulation for the regulators of mRNA, the regulators of ncRNA, and the coupled mRNA–ncRNA regulators. Temporal gene expression profiles are approximated in a two-dimensional spatial domain for the transition states before converging to the stationary state, using a system comprised of coupled-reaction partial differential equations. Simulation experiments demonstrate that the proposed mathematical gene-expression profile represents a best fit for the population abundance of these oncogenes. In future, our proposed solution can lead to the development of alternative interpretable approaches, through the application of ML models to discover unknown dynamics in gene regulatory systems.

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