Next generation of free? Points to consider when navigating sponsored genetic testing.

Genetics has been integrated into patient care across many subspecialties. However, genetic and genomic testing (GT) remain expensive with disparities in access both within Canada and internationally. It is, therefore, not surprising that sponsored GT has emerged as one alternative. Sponsored GT, for the purpose of this document, refers to clinical-grade GT partially or fully subsidised by industry. In return, industry sponsors-usually pharmaceutical or biotechnology companies-may have access to patients' genetic data, practitioner information, DNA and/or other information. The availability of sponsored GT options in the Canadian healthcare landscape has appeared to simplify patient and practitioner access to GT, but the potential ethical and legal considerations, as well as the nuances of a publicly funded healthcare system, must also be considered. This document offers preliminary guidance for Canadian healthcare practitioners encountering sponsored GT in practice. Further research and dialogue is urgently needed to explore this issue to provide fulsome considerations that one must be aware of when availing such options.

The promises and challenges of clinical AI in community paediatric medicine.

The widespread adoption of virtual care technologies has quickly reshaped healthcare operations and delivery, particularly in the context of community medicine. In this paper, we use the virtual care landscape as a point of departure to envision the promises and challenges of artificial intelligence (AI) in healthcare. Our analysis is directed towards community care practitioners interested in learning more about how AI can change their practice along with the critical considerations required to integrate AI into their practice. We highlight examples of how AI can enable access to new sources of clinical data while augmenting clinical workflows and healthcare delivery. AI can help optimize how and when care is delivered by community practitioners while also improving practice efficiency, accessibility, and the overall quality of care. Unlike virtual care, however, AI is still missing many of the key enablers required to facilitate adoption into the community care landscape and there are challenges we must consider and resolve for AI to successfully improve healthcare delivery. We discuss several critical considerations, including data governance in the clinic setting, healthcare practitioner education, regulation of AI in healthcare, clinician reimbursement, and access to both technology and the internet.

Human germline genome editing is illegal in Canada, but could it be desirable for some members of the rare disease community?

Human germline genome editing may prove to be especially poignant for members of the rare disease community, many of whom are diagnosed with monogenic diseases. This community lacks broad representation in the literature surrounding genome editing, notably in Canada, yet is likely to be directly affected by eventual clinical applications of this technology. Although not generalizable, the literature does offer some commonalities regarding the experiences of rare disease patients. This manuscript seeks to contribute to the search for broader societal dialogue surrounding human germline genome editing by exploring some of those commonalities that comfort the notion that CRISPR may hold promise or be desirable for some members of this community. We first explore the legal and policy context surrounding germline genome editing, focusing closely on Canada, then provide an overview of the common challenges experienced by members of the rare disease community, and finally assess the opportunities of germline genome editing vis-à-vis rare disease as we advocate for the need to more actively engage with the community in our search for public engagement.

From Clinic to Computer and Back Again: Practical Considerations When Designing and Implementing Machine Learning Solutions for Pediatrics.

Purpose of review: Machine learning (ML), a branch of artificial intelligence, is influencing all fields in medicine, with an abundance of work describing its application to adult practice. ML in pediatrics is distinctly unique with clinical, technical, and ethical nuances limiting the direct translation of ML tools developed for adults to pediatric populations. To our knowledge, no work has yet focused on outlining the unique considerations that need to be taken into account when designing and implementing ML in pediatrics. Recent findings: The nature of varying developmental stages and the prominence of family-centered care lead to vastly different data-generating processes in pediatrics. Data heterogeneity and a lack of high-quality pediatric databases further complicate ML research. In order to address some of these nuances, we provide a common pipeline for clinicians and computer scientists to use as a foundation for structuring ML projects, and a framework for the translation of a developed model into clinical practice in pediatrics. Throughout these pathways, we also highlight ethical and legal considerations that must be taken into account when working with pediatric populations and data. Summary: Here, we describe a comprehensive outline of special considerations required of ML in pediatrics from project ideation to implementation. We hope this review can serve as a high-level guideline for ML scientists and clinicians alike to identify applications in the pediatric setting, generate effective ML solutions, and subsequently deliver them to patients, families, and providers.