Lennard Y. W. Lee

Artificial intelligence (AI) has rapidly become the focal point of global governmental attention and investment. Nations are launching AI for science strategies on a scale comparable to historic endeavors such as Apollo and the Manhattan Project. These coordinated programs carry profound promise for people living with cancer, for those at risk of disease and for transformative public benefit. Central to this transformation is the rise of sovereign AI supercomputers which are fundamentally reshaping biomedical research. These publicly owned systems provide secure, large-scale computational capacity, enabling integration of complex health data and rapid analysis that was previously constrained. This review examines the geographic distribution, technical capabilities, and biomedical applications of these infrastructures. Key computational workloads that now benefit significantly from AI implementations include cancer imaging and diagnosis, personalized treatments, whole-genome and single-cell level analysis, and computational drug discovery. This approach has supercharged our efforts at the United Kingdom’s Cancer Vaccine AI & Supercomputing Project, our flagship national initiative to create new AI foundation models to accelerate the development of tools to establish immunity from cancer. In addition, this review evaluates governance models that safeguard patient privacy and intellectual property as well as measures that promote international collaboration while preserving compliance with regional regulations and make safer, more precise and effective treatments for public benefit. Substantial challenges exist, however, including inequitable resource availability, heterogeneous data standards and regulatory frameworks, and unbalanced computational expertise impeding the effective use of sovereign compute. Global collaborations are key to providing equitable access to advanced analytics, shortening the path from bench to bedside, and developing critical innovative tools for people affected by cancer.

Safeguarding patients from emerging infectious diseases demands strategies that prioritise patient well-being and protection. Immunobridging is an established trial methodology which has been increasingly employed to ensure patient protection and provide clinicians with swift access to vaccines. It uses immunological markers to infer the effectiveness of a new drug through a surrogate measure of efficacy. Recently, this method has also been employed to authorise novel drugs, such as COVID-19 vaccines, and this article explores the concepts behind immunobridging trials, their advantages, issues, and significance in the context of COVID-19 and other infectious diseases. Our goal is to improve awareness among clinicians, patient groups, regulators, and health leaders of the opportunities and issues of immunobridging, so that fewer patients are left without protection from infectious diseases, particularly from major pathogens that may emerge.

Background and Objective: The rapid evolution in artificial intelligence (AI) technology has led to the development of a range of softwares and tools within cancer diagnostics and therapeutics. This ranges from imaging, risk and outcome prediction, drug development and triage, and holds great promise in revolutionising cancer medicine by increasing efficiency, reducing waiting times and facilitating the provision of personalised medicine. However, various obstacles impede widespread integration of AI tools into clinical settings such as the black box nature of AI, data shift and ethical implications relating to the storage of personal data and how it is used. The aim of this narrative review is to inform the reader on current uses of AI in cancer care, and explore avenues for potential future uses and developments. Methods: A narrative literature search was undertaken to include all recent studies that discussed uses of AI within cancer medicine. Specific inclusion and exclusion criteria were used to further narrow down the results to articles pertaining to the use of AI within cancer medicine in diagnostics, drug development, treatment, prognostication and post-cancer journey care. Key Content and Findings: In this article, we review current uses of AI and how these can be further developed to improve patient outcomes, and discuss the ethical implications of integration of AI tools within clinical settings. Conclusions: The integration of AI-driven technologies early on in the journey of patients with cancer is likely to result in earlier and more accurate detection of diseased patients, facilitate precision medicine and help to empower cancer survivors by providing educational resources and support upon completion of their cancer journey. This will both decrease workload for clinicians and reduce preventable delays for patients during their cancer journey. However, this is hindered by the black box nature of AI which provides no explainability to clinicians, as well as security and data issues. Further investment in resources used to allow for the implementation of AI-driven technologies as well as complete transparency of the AI systems being utilised in clinical settings will help alleviate some of these problems and increase the trust in utilising these systems for patient care.

Immunocompromised people are facing ongoing inequality in health outcomes because of COVID-19. Let's remain ambitious and improve availability and access to COVID-19 prevention therapies that protect patients and aid management. This article brings together opinions from four experts based in the United Kingdom who specialise in immunology, solid organ transplantation, respiratory medicine and critical care, oncology and haematology. In this article, they communicate the impact of SARS-CoV-2 infection on vulnerable patients with underlying conditions and the need for immediate policies to protect vulnerable people from COVID-19.