This article investigates the conditions under which intellectual property rights are created in prison as well as the possibilities inmates are given to exercise these rights. The article adopts a predominantly Swedish perspective, while it also refers to illustrative examples of cases of creative works created behind bars in US prisons. The two penitentiary systems differ considerably, but there are some interesting conclusions to be drawn from the US experience and some unexpected similarities that may be noted in the way copyright protected works made in prison are treated. The article discusses, in its first part, the evolution of the penitentiary system in Sweden and the regulation of penal work and leisure times activities. The copyright system is briefly presented with a focus on the way copyright operates when the author is an inmate. Furthermore, the article provides illustrations of prison programmes that have resulted in the creation of copyright-protected works in the US, and how the US legal system has dealt with the question of copyright ownership of works created by inmates. Turning back to Sweden, the article reflects on the two writing programmes that have recently led to book publications authored by inmates, as well as on the fate of the ‘made in prison’ branded products. The article investigates yet another project, the Krimtech project that concerns other forms of inmate input to intellectual property, namely, the collection of data. The Foucauldian theory provides a theoretical umbrella under which the discrepancies between the copyright-law principle of equality of authors, and the actual practical implementation as well as the impact prison regulation has in this respect is discussed and analysed.

Massively parallel sequencing helps create new knowledge on genes, variants and their association with disease phenotype. This important technological advancement simultaneously makes clinical decision making, using genomic information for cancer patients, more complex. Currently, identifying actionable pathogenic variants with diagnostic, prognostic, or predictive impact requires substantial manual effort. Objective: The purpose is to design a solution for clinical diagnostics of lymphoma, specifically for systematic variant filtering and interpretation. Methods: A scoping review and demonstrations from specialists serve as a basis for a blueprint of a solution for massively parallel sequencing-based genetic diagnostics. Results: The solution uses machine learning methods to facilitate decision making in the diagnostic process. A validation round of interviews with specialists consolidated the blueprint and anchored it across all relevant expert disciplines. The scoping review identified four components of variant filtering solutions: algorithms and Artificial Intelligence (AI) applications, software, bioinformatics pipelines and variant filtering strategies. The blueprint describes the input, the AI model and the interface for dynamic browsing. Conclusion: An AI-augmented system is designed for predicting pathogenic variants. While such a system can be used to classify identified variants, diagnosticians should still evaluate the classification’s accuracy, make corrections when necessary, and ultimately decide which variants are truly pathogenic.