Event by Novo Nordisk Foundation Science Cluster Available online

    Conference: Precision medicine – from patient to lab and back again (online)

    The philosophy that all humans are essentially the same has provided key assumptions in driving medical breakthroughs. This guiding principle is now challenged by evidence demonstrating that variation from the mean is not simply noise but predictive. Precision medicine explains this by asserting that individuals belong to one or more overlapping groups determined by ancestry and environment. This conference will drive the conversation forward by focusing on patient centrality in the context of omics, digital medicine, and artificial intelligence. An unmet need in medical research is the link between molecular and genetic data (OMICs) with clinical characteristics for modelling outcomes like treatment response, survival, and risk of adverse events. This seems a simple task, however in reality it has proven to be harder than expected to manage. A major shift in thinking about how we might tackle these challenges is required from multiple domains. Scientific development of this goal will be facilitated by two back-to-back strategies. First, the conference whitepaper will ground discussion and the opinions of attendees in a peer-reviewed process; second, we will run a hackathon prior to the conference and focused on difficult immunological and clinical data sets provided by organizers.

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    02 May 2021


    Andrea Coravos ElektraLabs, United States
    Ariel Dora Stern Harvard Business School, United States
    Catherine Wu Dana-Farber Cancer Institute, United States
    Jens Lundgren Department of Infectious Diseases, Rigshospitalet, Denmark
    Magdalena Skipper Nature Research, United Kingdom
    Megan Doerr Sage Bionetworks, United States
    Mihaela van der Schaar University of Cambridge, United Kingdom
    Nigam Shah Stanford University, United States
    Rachel Liao Broad Institute of MIT and Harvard, United States
    Roger Kouyos University Hospital of Zürich, Zürich
    Stephen Friend Department of Psychiatry at University of Oxford and 4YouandMe
    Nikolai Brun Danish Medicines Agency, Denmark
    Yujin Hosida University of Texas Southwestern Medical Center, United States
    Girish Nadkarni Icahn School of Medicine at Mount Sinai, United States
    Henning Langberg Chief of Innovation på Rigshospitalet - National Hospital Denmark
    Pearse Keane NIHR Clinician Scientist at Institute of Ophthalmology, University College London
    Poster Design by Anncharlott Berglar, PhD, Scientific & Medical Visualisation (SciVisLab)

    Click here to see the Conference Programme*

    *Please note that this is a preliminary program and might be subject to changes

    Virtual Conference

    It is important to note that this will be a virtual conference due to covid-19 restrictions.

    Short Talks

    Some applicants will be invited to give a short talk during the conference based on their submitted abstract. Selected applicants will be notified in the end of March 2021.

    Registration fee for all approved applicants are covered by the Novo Nordisk Foundation. 



    A Ultrathon is currently being designed – a 6-week long competitive pre-event hackathon addressing important medical AI questions from real clinical environments.

    For more info and sign-up for the Ultrathon: Click Here


    Application instructions

    If you wish to attend our conference, you will need to apply and submit an abstract in our application system NORMA. If you have not used NORMA before, click REGISTER to create an account. When you enter the system, you can find the conference on the list of calls.

    If submitting an abstract for some reason is not an option for you, please category “MOTIVATIONAL ESSAY” followed by a paragraph explaining why you should participate in the conference.

    Late breaking deadline for abstract submission: April 21, 2021 (12:00 CET)

    Evaluation of incoming applications: End of April 2021

    Notifications to applicants: End of April 2021


    Registration fee for all approved applicants are covered by Novo Nordisk Foundation.

    For more general information about Copenhagen Biosciences Conferences click here. 

    Poster design by Anncharlott Berglar, PhD, Scientific & Medical Visualisation (SciVisLab), WebLinkedInInstagramFacebook.


    Abstract submission guidelines

    Content: The main goal of the conference is to connect participants in authoring a whitepaper on the current global state of medical AI with recommendations and guidelines to implement it in the clinic. We are therefore interested in any abstracts that promote the discussion of medical AI and in particular its implementation. Content may range from basic and applied research, through to presentation of real-world examples or thoughtful identification and discussion of needs and the future of medical AI. We are particularly interested in submissions that highlight the reality of AI in medicine and medical research in contrast to the hyperbole surrounding the topic. We therefore invite all stakeholders for whom AI impacts in any way to submit their work for consideration. We hope to collect a diverse array of speakers to focus on the major issues at hand and avoid the technical jargon. We have therefore created several topics designed to overlap and cover the broad issues surrounding this subject. When submitting your abstract, please consider clearly identifying one or more topics your work might be considered for.

    Novel data driven treatments: What does “data driven” mean and how is it different to what we have always done? What has changed to enable this, what does it take to employ it, and when and how will we see the impact of these new methods? Are we seeing such treatments in the clinic already and what were some of the challenges faced?

    Governance and policy: Often maligned but universally agreed as essential. Medicine and AI are also both abundant in ambiguity and combining these three topics seems doomed to fail. We have therefore doubled the number of talks on this important topic. What is the policy supporting medical AI? What does the global governance landscape look like? At what stage of an mAI project does ethics fit in? What are we talking about when it comes to bias in models?

    New collaborative models: Machine learning an AI has contributed toward significant gains in many industries. Those industries have contributed many methods back to data science. Their methods of collaborating and sharing data far exceed those in medicine. What are our barriers and how might we overcome them?

    Real-time medicine: Data driven research requires a lot of data from many instruments and real-time medicine, for example by mobile health, could be both a major contributor to data collection and disseminator of personalized health advice. What does real-time medicine look like in practice and what major opportunities remain?

    Merging data silos & omics: A major barrier to AI research and its broad implementation is the separation of datasets either intentionally or unintentionally through different interpretation of best practices in harmonisation, application of different ontologies, or mixed incentives. Omics technologies have vast promise but further exaggerate these disparities by contributing to one silo over another. Basic human research has mostly overcome these issues by centralizing omics data through public repositories, but how does clinical research fit into this landscape and how might hospitals in different locals best deploy data driven research from such resources?

    Lifecycles of algorithms in medicine: Implementation of medical AI implies the same or better degrees of trust that we currently place in standard treatment. What does it, or would it, take for us to trust AI in a clinical setting? And once deployed, what would it take for such an AI solution/treatment to remain? What considerations would be needed to retire an algorithm? Current guidelines mostly classify AI in the “software as a medical device” category, but while this allows for its implementation does it promote trust and is it the appropriate translational vehicle to move AI from the laboratory to the bedside? If we consider the rigor of drug design and clinical trials, what are the analogs for mAI and what does phase-IV even look like?

    Learning from COVID19: Before 2020, AI promised a lot to medical research and practice. But when COVID19 arrived much of the community fell back on tried-and-trusted methods. Of course, this was the correct move, but many who were not directly involved in the medical response also had the freedom to experiment with AI in diverse applications from triage to virology and epidemiology. What did they learn? What did first responders lack and what could AI have achieved if it were mature and trusted enough? Has the pandemic taught us anything about how we might do better? Or, was AI indeed a major player in our response and understanding of the virus?

    Communication and leadership, stakeholder perspectives and success stories: Several catch-all topics for those who want to share how they / their group / their institute has handled AI in their setting, what the challenges were and how they overcame them. Fully implemented models with “Real World Evidence” on clinical impact can be submitted for this topic as well.

    Lastly, we also accept research that focus on methods that address specific challenges and/or methods in precision medicine:

    • State-of-the-art algorithms
    • Interpretability and explainability
    • Algorithm confidence and trust
    • Robust validation approaches
    • Addressing the domain gap
    • Supervised learning for small cohorts
    • Collaborative or competitive modelling on common benchmarks
    • Generative models for simulating patient data
    • Privacy preserving data sharing and federated learning
    • Accessibility of algorithms for non-data scientists


    If you are not able to upload an abstract, we also welcome that you upload a short motivational essay instead in which you explain why you should be included in the conference.


    Conference topics

    Conference topics

    Novel data-driven treatments
    Governance and policy
    New collaborative models
    Real time medicine
    Merging data silos & omics
    Life-cycles of algorithms in medicine
    Full loop success stories
    Communication and leadership
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