Journal: The Lancet Digital Health

Abbreviation

Lancet Digit Health

Publisher

Elsevier

Journal Volumes

ISSN

2589-7500

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2019 - 201912020 - 20257
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Publications 1 - 8 of 8
  • From the 100 Day Mission to 100 lines of software development: how to improve early outbreak analytics
    Item type: Journal Article
    Cuartero, Carmen Tamayo; Carnegie, Anna C.; Cucunuba, Zulma M.; et al. (2025)
    The Lancet Digital Health
    Since the COVID-19 pandemic, considerable advances have been made to improve epidemic preparedness by accelerating diagnostics, therapeutics, and vaccine development. However, we argue that it is crucial to make equivalent efforts in the field of outbreak analytics to help ensure reliable, evidence-based decision making. To explore the challenges and key priorities in the field of outbreak analytics, the Epiverse-TRACE initiative brought together a multidisciplinary group of experts, including field epidemiologists, data scientists, academics, and software engineers from public health institutions across multiple countries. During a 3-day workshop, 40 participants discussed what the first 100 lines of code written during an outbreak should look like. The main findings from this workshop are summarised in this Viewpoint. We provide an overview of the current outbreak analytic landscape by highlighting current key challenges that should be addressed to improve the response to future public health crises. Furthermore, we propose actionable solutions to these challenges that are achievable in the short term, and longer-term strategic recommendations. This Viewpoint constitutes a call to action for experts involved in epidemic response to develop modern and robust data analytic approaches at the heart of epidemic preparedness and response.
  • Decentralised clinical trials: ethical opportunities and challenges
    Item type: Journal Article
    Vayena, Effy; Blasimme, Alessandro; Sugarman, Jeremy (2023)
    The Lancet Digital Health
    Fuelled by adaptations to clinical trial implementation during the COVID-19 pandemic, decentralised clinical trials are burgeoning. Decentralised clinical trials involve many digital tools to facilitate research without physical contact between research teams and participants at various stages, such as recruitment, enrolment, informed consent, administering study interventions, obtaining patient-reported outcome measures, and safety monitoring. These tools can provide ways of ensuring participants’ safety and research integrity, while sometimes reducing participant burden and trial cost. Research sponsors and investigators are interested in expanding the use of decentralised clinical trials. The US Food and Drug Administration and other regulators worldwide have issued guidance on how to implement such adaptations. However, there has been little focus on the distinct ethical challenges these trials pose. In this Health Policy report, which is informed by both traditional research ethics and digital ethics frameworks, we group the related ethical issues under three areas requiring increased ethical vigilance: participants’ safety and rights, scientific validity, and ethics oversight. Our aim is to describe these issues, offer practical means of addressing them, and prompt the delineation of ethical standards for decentralised trials.
  • From principles to practice: benchmarking government guidance on health apps
    Item type: Other Journal Item
    Ferretti, Agata; Ronchi, Elettra; Vayena, Effy (2019)
    The Lancet Digital Health
  • Ethnicity-based bias in clinical severity scores
    Item type: Other Journal Item
    Gumbsch, Thomas; Borgwardt, Karsten; borgwardt (2021)
    The Lancet Digital Health
  • Clinical trials for implantable neural prostheses: understanding the ethical and technical requirements
    Item type: Review Article
    Ienca, Marcello; Valle, Giacomo; Raspopovic, Stanisa (2025)
    The Lancet Digital Health
    Neuroprosthetics research has entered a stage in which animal models and proof-of-concept studies are translated into clinical applications, often combining implants with artificial intelligence techniques. This new phase raises the question of how clinical trials should be designed to scientifically and ethically address the unique features of neural prostheses. Neural prostheses are complex cyberbiological devices able to acquire and process data; hence, their assessment is not reducible to only third-party safety and efficacy evaluations as in pharmacological research. In addition, assessment of neural prostheses requires a causal understanding of their mechanisms, and scrutiny of their information security and legal liability standards. Some neural prostheses affect not only human behaviour, but also psychological faculties such as consciousness, cognition, and affective states. In this Viewpoint, we argue that the technological novelty of neural prostheses could generate challenges for technology assessment, clinical validation, and research ethics oversight. To this end, we identify a set of methodological and research ethics challenges specific to this medical technology innovation. We provide insights into relevant ethical guidelines and assess whether oversight mechanisms are well equipped to ensure adequate clinical and ethical use. Finally, we outline patient-centred research ethics requirements for clinical trials involving implantable neural prostheses.
  • Digital tools against COVID-19: taxonomy, ethical challenges, and navigation aid
    Item type: Journal Article
    Gasser, Urs; Ienca, Marcello; Scheibner, James; et al. (2020)
    The Lancet Digital Health
  • FDA-cleared artificial intelligence and machine learning-based medical devices and their 510(k) predicate networks
    Item type: Journal Article
    Muehlematter, Urs J.; Bluethgen, Christian; Vokinger, Kerstin Noëlle (2023)
    The Lancet Digital Health
    The US Food and Drug Administration is clearing an increasing number of artificial intelligence and machine learning (AI/ML)-based medical devices through the 510(k) pathway. This pathway allows clearance if the device is substantially equivalent to a former cleared device (ie, predicate). We analysed the predicate networks of cleared AI/ML-based medical devices (cleared between 2019 and 2021), their underlying tasks, and recalls. More than a third of cleared AI/ML-based medical devices originated from non-AI/ML-based medical devices in the first generation. Devices with the longest time since the last predicate device with an AI/ML component were haematology (2001), radiology (2001), and cardiovascular devices (2008). Especially for devices in radiology, the AI/ML tasks changed frequently along the device's predicate network, raising safety concerns. To date, only a few recalls might have affected the AI/ML components. To improve patient care, a stronger focus should be placed on the distinctive characteristics of AI/ML when defining substantial equivalence between a new AI/ML-based medical device and predicate devices.
  • Continual Learning in Medical Devices: FDA’s Action Plan and Beyond
    Item type: Other Journal Item
    Vokinger, Kerstin Noëlle; Feuerriegel, Stefan; Kesselheim, Aaron S. (2021)
    The Lancet Digital Health
Publications 1 - 8 of 8