James Shaw

Ethical debate about artificial intelligence (AI) for health is fundamental to the good governance and long-term success of this group of technologies in healthcare and public health. Building on foundational contributions on this topic from the field of bioethics, we propose that interdisciplinary work on AI ethics should deepen the already strong engagements that exist with Science and Technology Studies (STS) to uphold core commitments of global population health. STS is an interdisciplinary field that specifically explores the meaning, development and governance of science and technology and offers enormous conceptual resources for AI ethics. We present a path towards deeper integration of STS and bioethics for an AI ethics that can more meaningfully address the most important issues presented by AI for global population health.

Rural communities face persistent challenges in accessing timely, culturally relevant, and trustworthy health information due to inadequate communication infrastructures, workforce shortages, and infrastructural constraints. As generative artificial intelligence (GenAI) tools become increasingly accessible, rural serving organizations are often pushed to explore their use to expand communication reach and reduce staff burden through funding incentives, vendor offerings, and policy signals, even when adoption is misaligned with local capacity or priorities. However, guidance is lacking on how rural systems should approach GenAI adoption in ways that strengthen, rather than undermine, trust and equity. This Opinion offers a systems-oriented and community-centered perspective on rural GenAI readiness by distinguishing between non-agentic applications that support human communicators and agentic systems that introduce varying degrees of autonomy. We propose a staged maturity framework tailored to rural health communication ecosystems, outlining opportunities, risks, and governance needs at each stage of adoption. By centering on rural context, communication trust, and system readiness, this Opinion aims to support the intentional, ethical, and sustainable integration of GenAI into rural health communication systems.

Digital health tools are generally poorly regulated and often lack strong research evidence, posing challenges for purchasers of point solutions such as employer groups and insurers. Point solutions, which are digital tools designed to manage health-related conditions, to promote wellness, and/or to drive member engagement, present unique challenges for purchasers. In this Review we provide a concise summary of 10 pitfalls in the interpretation of messaging from digital health vendors for purchasers seeking to make good investments in point solutions. We highlight existing population health research approaches that digital health vendors can employ to mitigate these pitfalls. We conclude with a recommendation that employers and health care entities gain clarity on goals related to outcomes they hope to achieve with digital health technologies, and make purchasing decisions based on a framework and objective data supporting the desired outcomes specifically related to those goals.

Purpose: This study explores Canadian physical therapy (PT) leaders’ knowledge of digital technologies and examines their beliefs about the ways in which emerging technologies will shape PT practice in the future. Method: We recruited 11 physical therapists with leadership roles at universities, commercial technology vendors, clinics, and regulatory bodies. We conducted semi-structured interviews and then analyzed them using the DEPICT model. Results: Participants were most knowledgeable in the use of telerehabilitation in PT practice. Participants believed there were opportunities for and challenges with the further integration of technology into the profession. Participants believed technology is increasingly pressuring the profession to change. A subset of participants believed the idea of professional evolution and maturity should serve as a lens through which the profession views the implications and further integration of technology into physical therapy care. Last, we found that access to care was a focal point for discussion of the integration of technology into the profession. Conclusions: The findings underscore the importance of further research and policy development regarding the maturity of the PT profession, with a particular focus on the interconnected issues of digital technology and access to care.

The Pan-Canadian Health Data Charter (the Charter) provides a strong foundation for Canada’s strategy to building a trustworthy and adaptable health information ecosystem. A key requirement is to ensure that governance mechanisms are informed by the views of the public, raising important questions about what constituencies make up “the public” and what inclusion looks like in practice. In this article, we use health data justice as the theoretical basis to justify the importance of centring equity in inclusionary efforts and outline key considerations for involving diverse members of the public, patients, and communities in advising health information systems and policy. We suggest that engagement should be fit-for-purpose, marrying the methods and selection of participants with overarching goals, and multi-modal, employing multiple methods to facilitate wider participation from individuals with varying needs. We also recognize the importance of long-term partnerships between civil society organizations, policy actors, and researchers to ensure that the needs and preferences of structurally marginalized groups are prioritized in engagement initiatives and downstream policy decisions.

The integration of established and emerging digital technologies into the institutions and activities of daily life produces important digital transformations that have profound impacts on health and well-being. In this Perspective, we articulate the urgent need for close attention to the health-related impacts of the diverse uses of digital technologies and the transformations they bring about on a global scale. We provide a conceptual summary of definitions of the Digital Determinants of Health published in the peer-reviewed literature and present progress toward a conceptual framework for understanding the Digital Determinants of Health as operating through primary (individual and community health), secondary (domestic institutions), and tertiary (global political economy) mechanisms. We call for dialogue and collaboration to deepen work on the Digital Determinants of Health for more just and equitable impacts of digital transformations for global population health.

Thank you for the opportunity to review this interesting paper addressing a very important topic. The paper includes lots of very helpful information. In my view as a reader, I believe the paper could be improved to enhance conceptual coherence and u...

Data-intensive health innovation is a concept that has garnered significant attention with the widespread collection of digitized health-related data and the rapidly evolving analytics capabilities...

Machine learning (ML), a subset of artificial intelligence, uses large datasets to identify patterns between potential predictors and outcomes. ML involves iterative learning from data and is increasingly used in population and public health. Examples include early warning of infectious disease outbreaks, predicting the future burden of noncommunicable diseases, and assessing public health interventions. However, ML can inadvertently produce biased outputs related to the quality and quantity of data, who is engaged and helping direct the analysis, and how findings are interpreted. Specific guidelines for using ML in population and public health have not yet been created. We assembled a diverse team of experts in computer science, statistical modeling, clinical and population health epidemiology, health economics, ethics, sociology, and public health. Drawing on literature reviews and a modified Delphi process, we identified five key recommendations: (1) prioritize partnerships and interventions to support communities considered structurally disadvantaged; (2) use ML for dynamic situations, such as public health emergencies, while adhering to ethical standards; (3) conduct risk assessments and bias mitigation strategies aligned with identified risks; (4) ensure technical transparency and reproducibility by publicly sharing data sources and methodologies; and (5) foster multidisciplinary dialogue to discuss the potential harms of ML-related bias and raise awareness among the public and public health community. The proposed guidelines provide operational steps for stakeholders, ensuring that ML tools are not only effective but also ethically grounded and feasible in real-world scenarios.