Andreas Bedorf
Marika NiihoriAhead of this session, Mihaela published a piece of content on machine learning and the future of healthcare, entitled Revolutionizing healthcare: an invitation to clinical professionals everywhere.
Ahead of this session, Mihaela published a piece of content entitled Machine learning for healthcare: Towards a unifying framework.
The focus of the session was on addressing real-world problems in the acute care setting by matching them to formalisms.
During the session, we introduced the lab’s Hub for Healthcare, which contains a classification of some medical problems and associated examples, and then provides formalisms and methods by which they can be solved.
The focus of the session was on addressing real-world problems in the cancer domain (with an emphasis on the pathway up to the point of diagnosis) by matching them to formalisms.
The focus of the session was on addressing real-world problems in the cancer domain (with an emphasis on post-diagnosis treatment) by matching them to formalisms.
Our sixth session was a roundtable on the topic of interpretability.
Following a presentation by Mihaela van der Schaar, a panel of four clinicians discussed various definitions and types of interpretability, as well as real-world needs and contexts in healthcare settings.
This session was the second roundtable in a double-header focusing on interpretability in ML/AI for healthcare.
Following a quick introduction by Mihaela van der Schaar, a panel of four clinicians and the audience of clinicians discussed a range of complex issues surrounding interpretability, including whether or not current expectations among the clinical community are realistic.
This session was a roundtable focusing on personalized therapeutics and individualized treatment effects (ITE).
Following a short presentation by Mihaela van der Schaar, a panel of four clinicians and the audience discussed a range of topics related to personalised therapeutics, including the limitations of existing clinical guidelines, and the potential for machine learning and AI to aid the development of more personalised and useful guidelines in healthcare.
This session featured an international panel of 5 clinicians who discussed AI and machine learning decision support tools for diseases such as breast cancer. One such tool (developed by the van der Schaar lab) is Adjutorium, which was the focus of an extensive study published in Nature Machine Intelligence.
The session started with presentations on Adjutorium from Mihaela van der Schaar and postdoc Ahmed Alaa, which formed the basis of the subsequent clinician roundtable. During the roundtable, the panelists explored a range of topics related to the clinical application of tools like Adjutorium—such as the kinds of information that would be useful for clinicians and patients, and how this information can be displayed (among a range of other topics).
In this session, Mihaela van der Schaar and a panel of 4 international clinicians discussed the importance of bridging the gap between ideas and implementation in machine learning for healthcare.
In the first part of the session, Mihaela and the clinicians showed how machine learning can help clinicians estimate individualized treatment effects, using a working demonstrator to show real-world case studies from the ICU setting. The latter part of the session featured a clinician roundtable, during which the panelists discussed different approaches to ensuring that clinicians can make full use of machine learning models.
In this session, Mihaela van der Schaar and a panel of 3 international clinicians expanded on the agenda outlined in the previous session: the importance of bridging the gap between ideas and implementation in machine learning for healthcare.
In the first part of the session, Mihaela and the clinicians showed how machine learning can assist dynamic time-to-event analysis and temporal phenotyping, using a working demonstrator to show real-world case studies from prostate cancer. The latter part of the session featured a clinician roundtable, during which the panelists discussed different approaches to ensuring that clinicians can make full use of machine learning models.
In this session, Mihaela van der Schaar and a panel of 4 international clinicians explored possible AI and machine learning approaches to organ transplantation.
In the first part of the session, Dr. Alexander Gimson, a Cambridge-based transplant hepatologist, outlined the importance, complexities, and unique challenges of the organ transplantation setting, while also highlighting some recent collaborative projects using machine learning for donor-recipient matchmaking and survival prediction. The latter part of the session featured a clinician roundtable comprising an expert panel of four transplantation specialists. Our panelists answered an array of questions (most of which came from the audience) and discussed the path forward for AI and machine learning for organ transplantation.
In this session, Mihaela van der Schaar and an international panel of clinical experts explored how AI and machine learning can help transform early detection and diagnosis (ED&D). This session was the first in a double-header on this important topic.
The session started with a presentation by Mihaela van der Schaar, who highlighted the importance of ED&D as one of healthcare’s “holy grails” and introduced a wide range of areas where machine learning and AI can have a positive impact. The latter part of the session featured a clinician roundtable comprising an expert panel of five specialists in the area of diagnosis and detection (primarily related to cancer). Our panelists answered an array of questions posed by Mihaela, and discussed the path forward for AI and machine learning for ED&D.
This was the second instalment in a double-header focusing on the absolutely crucial topic of early detection and diagnosis (ED&D). The session included a roundtable consisting of Mihaela van der Schaar and an international panel of three expert clinicians exploring how ED&D can be transformed by AI and machine learning.
The roundtable followed an introductory presentation by Mihaela van der Schaar, who built on the explorations of the previous session in this double-header and set the stage for the panel. After our panelists engaged in their enlightening discussion about possible paths forward for AI and machine learning for ED&D, Dr Eoin McKinney presented a newly-developed machine learning demonstrator specifically designed for ED&D.
Over the course of three insightful, energetic, and often provocative discussions, Profs. Euan Ashley, Geraint Rees, and Eric Topol shared their personal views on the state of AI and machine learning in healthcare, as well as their respective visions for AI-powered healthcare systems of the future.
Following these discussions, we opened the session up to our audience of clinicians, who shared their own views on the topics Mihaela had discussed with our three leading clinical voices.
The session started with a presentation by Mihaela, who set the stage with and introduction to synthetic data and its value for modern healthcare. This was followed up with the presentation of a synthetic data demonstrator by Dr Jem Rashbass, MD, developed and designed by members of the van der Schaar lab.
The latter part of the session featured an engaging clinician roundtable comprising an expert panel of three specialists in the area of synthetic data. Our panelists answered a number of questions posed by Mihaela and the audience.
The session started with short presentations by our panellists framing the problems clinicians are currently facing in regards to clinical trials, defining important terms, and giving examples of current challenges and opportunities.
The latter part of the session featured an engaging clinician roundtable comprising an expert panel of four specialists in the area of clinical trials. Our panellists answered a number of questions posed by Mihaela and the audience.
The session started with a presentation by Mihaela, summarising the previous session, key opportunities, and discussing the main machine learning solutions to the established challenges.
This was followed up by a short, presented discussion of Mihaela and Dr Eoin McKinney about machine learning solutions for estimating heterogenous treatment effects, and SyncTwin to emulate clinical trials. A second conversation between Mihaela and Prof Richard Peck about personalised dose response using machine learning then set the stage for our roundtable.
The session started with a presentation by Mihaela and Andres, introducing to the complex challenges surrounding cystic fibrosis, and presenting what ML methods have been developed already.
This was followed up by a panel discussion and questions from the audience.
We thank Prof Damian Downey, Dr Jamie Duckers, Dr Robert Gray, Dr Charles Haworth, Prof Alexander Horsley, and Caroline Cartellieri Karlsen for their participation.
The session started with a presentation by Mihaela, introducing AutoPrognosis to the guests and audience. This was followed by a talk by Dr Eoin McKinney, presenting a clinical perspective on the potential of AutoPrognosis. Dr Tom Callender then presented an example of AutoPrognosis used in a real-world case-study.
More info about AutoPrognosis here: https://www.autoprognosis.vanderschaar-lab.com/
This was followed up by a panel discussion and questions from the audience.
We thank Prof Donald E. Ingber (Harvard), Prof Suetonia Palmer (Otago), and Dr Paul Goldsmith (NHS) for their participation.
The session started with an introduction to Revolutionizing Healthcare and the significance of taking action by Prof Mihaela van der Schaar. This was followed by two highly relevant presentations by and for practicing clinicians, as well as an extensive round table discussion with our clinical experts.
We invited questions from the audience and the session developed into a thought-provoking discussion including the perspectives from a variety of clinicians.
We thank Dr Tom Callender (UCL) and Prof Brent Ershoff (UCLA) for their participation.
More info about AutoPrognosis here: https://www.autoprognosis.vanderschaar-lab.com/
The session started with an introduction to data-centric machine learning for clinicians by Prof Mihaela van der Schaar. This was followed by a highly relevant roundtable discussion with practicing clinicians that joined us from our regular audience.
We invited questions from the audience and the session developed into a thought-provoking discussion including the perspectives from a variety of clinicians.
We thank Prof Carsten Utoft Niemann (Copenhagen University), Dr Mustafa Khanbhai (NHS/Imperial College London), Dr Nazima Pathan (University of Cambridge), and Dr Janak Gunatilleke (KPMG) for their participation.
You can learn more about data-centric AI by reading our dedicated research pillar, by exploring the potential of our DC-Check tool, and by engaging with our reality centric agenda as overarching effort to revolutionise Machine Learning, AI, and how we approach data.
The session started with a summary of our previous session and the introduction of “4 antidotes” for improving clinical data by Prof Mihaela van der Schaar. This was followed by a tremendously engaging roundtable discussion with practicing clinicians that joined us from a diverse array of backgrounds.
We thank Prof Evis Sala (Universitá Carrolica del Sacro Cuore), Prof Suetonia Palmer (Health New Zealand), Prof Douglas Bell (UCLA), Prof Martin Wagner (Technische Universität Dresden), and Prof Ari Ercole (Cambridge University Hospitals NHS Foundation Trust) for their participation.
If you would like to learn more about a data-centric AI, have a look at our dedicated research pillar and Mihaela’s four antidotes for imperfect data. You can read a comprehensive summary of our previous 22 March session here.
The session was shaped by presentations on the topic by our guests and Prof Mihaela van der Schaar. Questions and definitions brought up in these talks were then discussed in a tremendously engaging roundtable discussion.
We thank Dr Tobias Gauss (Hopitaux Universitaires Paris Nord Val de Sein), Dr Alexander Gimson (University of Cambridge), and Prof Eoin McKinney (University of Cambridge) for their participation.
The session also doubled as this year’s AI Clinic in cooperation with the Cambridge Centre for AI in Medicine.
The session’s main aim was to introduce the clinical audience to a range of specialised machine learning software, tailored to the needs of clinicians.
Who is CCAIM? The Cambridge Centre for AI in Medicine is devoted to transform healthcare through its world-leading research in AI and machine learning. We hold close partnerships with clinicians and industry, and a range of upcoming education initiatives.
What is the AI Clinic? We are aiming to inform you about the opportunities of AI for healthcare, as well as additional information about the work we do and how you can use it in your practise. There will be talks, demonstrators, and exemplary projects – all provided by Prof Mihaela van der Schaar, Prof Andres Floto, Prof Eoin McKinney and Dr Thomas Callender.
The session was led by Tim Schubert and Tim Oosterlinck, two visiting medical students at the van der Schaar Lab who were guiding the conversation with a number of questions about AI Education for Clinicians.
For this episode, we thoroughly focused on a communal effort to explore AI education for young clinicians. We informed the panel discussion with audience polls and then followed up with an open conversation, trying to illuminate the topic from a diverse range of angles.
We thank Prof Robert D Stevens (Johns Hopkins University), Dr Ari Ercole (University of Cambridge), Matthias Carl Laupichler (University of Bonn), and Dr Liam McCoy (University of Toronto) for their participation.
The session was led by Tim Schubert and Tim Oosterlinck, two visiting medical students at the van der Schaar Lab who were guiding the conversation around Timely & Early Diagnosis: Medical Decisions in the Era of EHRs and Machine Learning and a new Framework envisioned by the panel.
For this episode, we thoroughly focused on a new framework for timely & early diagnosis. We informed the panel discussion with an audience poll in the beginning and then followed up with the opinions of our panellists and an open conversation, trying to illuminate the topic from a diverse range of angles.
We thank Prof Henk van Weert (Amsterdam University Medical Centers), Dr Alexander Gimson (University of Cambridge/CCAIM), Dr Camelia Davtyan (UCLA), and Prof Richard Peck (University of Liverpool/CCAIM) for their participation.
The session was led by Tim Schubert and Tim Oosterlinck, two visiting medical students at the van der Schaar Lab who were guiding the conversation around Large Language Models and their (potential) impact on healthcare.
For this episode, we first focused on a comprehensive introduction to LLMs, unveiling their potential in healthcare. This was followed by two live demonstrations to witness LLMs for healthcare in action. We subsequently transitioned into a moderated discussion, exploring a spectrum of topics with a focus on innovative use cases. We informed said panel conversation with audience polls and the opinions of our panellists, trying to illuminate the topic from a diverse range of angles.
We thank Dr William Weeks (Microsoft), Prof Eoin McKinney (CCAIM), and Dr Graciela Gonzales-Hernandez (Cedars-Sinai Medical Center) for their participation.
The session was led by Tim Schubert and Tim Oosterlinck, two visiting medical students at the van der Schaar Lab who were guiding the conversation around Large Language Models based on our previous 8 December session.
For this episode, we first focused on a comprehensive recap of the previous session. This was followed by our guests giving their perspective on the matter, and Mihaela presenting the state of the art in LLMs. We subsequently transitioned into a moderated discussion, brainstorming challenges, asking collected questions from our followers, and we invited the audience to participate.
We thank Prof Nigam Shah (Stanford) and Prof Martin Wagner (TU Dresden) for their participation.
The session was led by Tim Schubert and Tim Oosterlinck, two visiting medical students at the van der Schaar Lab who were guiding the conversation around AI in Oncology.
For this episode, we first focused on a panel discussion around AI for oncology with topics ranging from screening to diagnosis, treatment, and beyond.
We thank Dr David Crosby (Cancer Research UK), Prof Olivier Elemento (Weill Cornell Medicine), Prof Sileny Han (UZ Leuven), and Prof Jan Oldenburg (Akershus University Hospitel & University of Oslo) for their participation.
For this episode, Prof Andres Floto first introduced the basics of Multi-omics to our audience, explaining how it integrates diverse biological data types, such as genomics, transcriptomics, proteomics, metabolomics, and more to provide a comprehensive view of an organism’s biological functions.
Then we moved on further introductory presentations by our panellist. That was followed by a panel discussion informed by questions from the audience. We discussed the challenges Multi-omics pose, and how Machine learning’s unparalleled ability to make sense of multi-modal datasets might tackle them.
We thank Prof Andres Floto (CCAIM/University of Cambridge), Prof Kun-Hsing Yu (Harvard Medical School), Prof Julio Saez-Rodriguez (Heidelberg University/EMBL), and Dr Fergus Imrie (University of Cambridge) for their participation.
For this episode, Prof Mihaela van der Schaar first introduced the current state and recent advances of ML in hospital and intensive care to our audience. Then we moved on further introductory presentations by our panellist.
That was followed by a panel discussion led by Prof van der Schaar. We discussed the challenges intensive care poses, and how Machine learning and AI can help solving them – as long as the tools make it to the bedside.
We thank Dr Tobias Gauss (Beaujon Hospital Hopitaux Universitaires Paris Nord Val de Sein), Prof Robert Stevens (Johns Hopkins University), Prof Paul Elbers (Amsterdam UMC), and Prof Ari Ercole (University of Cambridge/CCAIM) for their participation.
For this episode, Prof Mihaela van der Schaar first introduced the basics of personalised therapeutics to our audience, explaining the role of AI and state-of-the-art approaches. Then we moved on further introductory presentations by our panellist.
Treatment effect estimation helps to quantify the impact of specific medical interventions on patient outcomes – crucial information for improving clinical decision-making. By leveraging patient data, Machine Learning can predict outcomes with a precision previously unattainable. This technology is poised to not only forecast individual responses to treatments but also assist in developing new therapeutic strategies that are more effective. This allows healthcare to move beyond one-size-fits-all solutions and toward personalised medicine.
That was followed by a panel discussion informed by questions from the audience.
We thank Prof Richard Peck (CCAIM/University of Liverpool), Prof Pierre Marquet (University Hospital Limoges), and Prof Jean-Baptiste Woillard (University Hospital Limoges) for their participation.
In this session, we delve deep into the pivotal role of machine learning for Cardiology. Cardiovascular disease remains one of the leading causes of death worldwide. Machine learning methods have already been developed for several use-cases in cardiology, but many more ML-powered developments are on the horizon and most tools are not being used routinely.
The van der Schaar lab has done some of its best work at the intersection of AI and cardiology, developing novel, cutting-edge machine learning along the way. We have highlighted the most important pieces of this work in our most recent blog post. Have a look – this will be one of the best ways to get the most out of the session.
In the session, our panel of experts shares their latest research and clinical experiences, providing you with unique insights into the latest advancements. We also discuss the future of ML in cardiology, and answer questions. The session begins with a brief presentation from our panellists, followed by an in-depth discussion on the adoption, challenges and future potential of machine learning in cardiology.
We thank Prof Gregg Fonarow (UCLA) and Prof Folkert Asselbergs (UMC/UCL) for their participation.
Recording of the van der Schaar Lab’s thirty-seventh Revolutionizing Healthcare engagement session for clinicians which took place virtually on 1 October, 2024.
The session was led by Tim Schubert and Tim Oosterlinck, two visiting medical students at the van der Schaar Lab who were guiding the conversation. During our CCAIM Clinical Summer School, we unveiled our no-code virtual data science partner, capable of going from clinical dataset to predictive model and interpretation in just one conversation. This sparked a lot of excitement from the clinicians who got a first look. Now, it’s time to share it with you!
For this episode, Prof Mihaela van der Schaar first introduced the basics of using AI to empower clinicians and how the new tool (CliMB) or similar virtual AI/ML partners could transform clinical research. Then we moved on to a panel discussion to hear clinical perspectives before opening up to questions from the audience.
We thank Prof Andres Floto (CCAIM/University of Cambridge), Prof Vasilis Kosmoliaptsis (University of Cambridge), and Dr Tom Callender (CCAIM/UCL) for their participation.
Recording of the van der Schaar Lab’s thirty-eight Revolutionizing Healthcare engagement session for clinicians which took place virtually on 5 November, 2024.
In this session, we tell you all about the latest research in the field – Digital Twins – and explain how Digital Twins can be useful for individualised patient care and the optimisation of hospital workflows. We define and explain Digital Twins, and discuss use cases in medicine and healthcare.
Prof Mihaela van der Schaar first introduced the basics of Digital Twins to bring everyone up to speed. Then we moved on to a panel discussion to hear some brilliant minds who are at the forefront of bringing digital twins into the medical world, before opening up to questions from the audience.
We thank Dr Alexander Gimson (CCAIM), Dr Phyllis Thangaraj (Yale School of Medicine), Prof Radek Bukowski (University of Texas at Austin), and Prof Eoin McKinneky (CCAIM/University of Cambridge) for their participation.
Find our NeurIPS 2024 Spotlight paper on Digital Twins here: https://arxiv.org/abs/2410.23691
Learn more about our lab’s NeurIPS 2024 appearance here: https://www.vanderschaar-lab.com/van-der-schaar-lab-at-neurips-2024/
Recording of the van der Schaar Lab’s thirty-ninth Revolutionizing Healthcare engagement session for clinicians which took place virtually on 9 December, 2024.
In this session, We reflected on how much has changed since last year’s conversation on AI education (watch the previous session here).
This year’s discussion focused on how AI education has evolved and the gaps that still remain. To frame the discussion, Mihaela van der Schaar introduced the issue in general and explained how our lab is trying to contribute to AI Education through initiatives like Revolutionizing Healthcare and our Summer School for AI in Medicine.
Tim Schubert then presented our paradigm shifting paper on AI Education for clinicians that was recently published in eClinicalMedicine. In there, we distilled the key challenges of medical AI education to provide a structured discussion framework. In addition, we offer a three-tiered model of medical AI expertise and practical recommendations to guide stakeholders and institutions worldwide. You can find the paper here.
We thank Prof Adam Rodman (Harvard Medical School), Prof Curtis Langlotz (Stanford University), and Finn Fassbender (University of Tübingen).
Recording of the van der Schaar Lab’s thirty-ninth Revolutionizing Healthcare engagement session for clinicians which took place virtually on 7 January, 2025.
This was the first instalment of a new kind of session for Revolutionizing Healthcare. We invited Dean Lloyd B Minor of Stanford University, a visionary healthcare leader to an in-depth conversation on the future of AI in healthcare, framing our way forward through the next months.
Together, Prof van der Schaar and Prof Minor discuss the future of AI in healthcare, sharing insights that could shape the path forward. Among other topics, they discuss the role of AI in medicine, how clinicians can be empowered by collaboration with AI, and what challenges and limitations we are facing today, and how these challenges can be turned into opportunities for advancing patient care.
Lloyd B Minor, MD, is a scientist, surgeon, and academic leader. He is the Dean of the Stanford University School of Medicine and Vice President for Medical Affairs at Stanford University. Prof Minor is also a professor of Otolaryngology, Head and Neck Surgery and a professor of Bioengineering and of Neurobiology at Stanford University.
Recording of the van der Schaar Lab’s forty-first Revolutionizing Healthcare engagement session for clinicians which took place virtually on 4 February 2025.
The interest in copilots has skyrocketed in the past year alone, and we are excited to discuss the opportunities in medicine and medical research.
In this session, we brainstorm with our audience and our panel on how to effectively align research efforts in machine learning with your clinical needs. The future healthcare system will likely have AI-powered technology integrated into applications from EHRs to bedside monitors.
To frame the discussion, Mihaela van der Schaar introduced the topic of copilots in general, explaining the need for this session after our previous one on that issue.
Anders Boyd and Evgeny Saveliev then presented our novel co-pilot, both in theory and with an in-depth demonstration. You can find our new paper on the potential of AI Co-Pilots, also introducing CliMB-DC, our human-guided, data-centric framework for LLM co-pilots that combines advanced data-centric tools with LLM-driven reasoning to enable robust, context-aware data processing here.
We then moved onto a fascinating roundtable discussion on the potential of Co-Pilots with our guests and the audience.
We thank Prof Russ Altman (Stanford University), Prof Marry Beth Terry (Columbia University), and Dr Anders Boyd (Amsterdam UMC).
Recording of the van der Schaar Lab’s forty-second Revolutionizing Healthcare engagement session for clinicians which took place virtually on 11 March, 2025.
In this session, we go back to explore the exciting potential of AI in oncology – how cutting-edge machine learning models are transforming cancer detection, diagnosis, and treatment planning. From improving early detection through advanced imaging analysis to enabling personalised treatment strategies based on vast datasets, AI is revolutionising patient care.
We discuss real-world applications, emerging innovations, and the challenges of integrating AI into clinical workflows, offering a glimpse into a future where technology enhances decision-making and improves outcomes for patients.
We thank Prof Michael Rosenthal (Havard) and Prof Shuji Ogino (MIT & Havard) for their participation.
Recording of the van der Schaar Lab’s forty-third Revolutionizing Healthcare engagement session for clinicians which took place virtually on 7 April, 2025.
In this session, we reveal our newest big idea: Genies. Unlike conventional AI agents built for routine, reactive tasks, genies represent a major leap forward: they are intelligent companions that generate novel ideas, plan and execute complex strategies, engage in dynamic reasoning, and continuously learn – not only from real-world interactions but also by adapting to their users, whom they are designed to empower.
Find out more about Genies here.
Mihaela van der Schaar introduced her vision of Genies and a comprehensive example of how Genies can transform the way we engage with AI in healthcare.
You can find the full example of an AI-Empowered Clinician here.
We thank Prof Richard Peck (CCAIM) and Prof Eoin McKinney (CCAIM).
Recording of the van der Schaar Lab’s forty-fourth Revolutionizing Healthcare engagement session for clinicians which took place virtually on 06 May, 2025.
In this session, we dive into the potential of AI for Diabetes Prevention and Care. Our expert guests framed the beginning our session. Prof Dopazo introduced us to the challenges of diabetes, focussing on disease heterogeneity and comorbidities and highlighting two opportunities: prediction of diabetes risk in a general population an early prediction of comorbidities in risk populations. Prof Guasch-Ferré then introduced her work in metabolomics and new risk factors in diabetes.
Based on this, an intriguing discussion developed, amended and informed by questions from Mihaela van der Schaar, Tim Schubert, and our audience.
We thank Prof Marta Guasch-Ferré (University of Copenhagen) and Prof Joaquin Dopazo (Progress and Health Foundation) for their participation.
Recording of the van der Schaar Lab’s forty-fifth Revolutionizing Healthcare engagement session for clinicians which took place virtually on 17 June, 2025.
Prof van der Schaar opened the session by encouraging healthcare professionals to reach out to machine learning labs and collaborate on challenging problems in medicine. Dr Tom Callender then introduced challenges of cross-collaboration between researchers in medicine and AI. Our panellists from the Revolutionizing Healthcare community, Dr Avneesh Khare, Dr Valerie Vandeweerd, Dr N. Aizaan Anwar, Dr Crina Samarghitean, and Dr Marco Montagna, proceeded sharing their experience working with AI researchers or building machine learning models themselves. During the open discussion, panellists shared advice on how to get started, how to find collaborators, and how to keep up with the fast-paced world of AI.
We thank Dr Tom Callender Dr Avneesh Khare Dr Valerie Vandeweerd Dr N. Aizaan Anwar Dr Crina Samarghitean Dr Marco Montagna for their participation.
Recording of the van der Schaar Lab’s forty-sixth Revolutionizing Healthcare engagement session for clinicians which took place virtually on 25 September, 2025.
Session leads: Mihaela van der Schaar and Tim Schubert
This session will introduce: The vision for AI copilots – how they can transform clinical practice What makes copilots distinct from large language models Examples of copilots in action – including Syncraft and Climb-DC Clinician perspectives on what is needed from copilots – with live input from the audience
We thank Dr Tom Callender, Dr Anders Boyd, Prof Eoin McKinney, Prof Carsten Utoft Niemann and Dr Marco Montagna for their participation.
Recording of the van der Schaar Lab’s forty-seventh Revolutionizing Healthcare engagement session for clinicians which took place virtually on 22 October, 2025. The theme for this session as AI in Oncology.
We were delighted to be joined by: Prof Jakob Nikolas Kather Silas Ruhrberg Estévez and we thank them for their participation
Recording of the van der Schaar Lab’s forty-seventh Revolutionizing Healthcare engagement session for clinicians which took place virtually on 26 November, 2025.
Session leads:
- Prof Eoin McKinney – Versus Arthritis Chair of Rheumatology in the Department of Medicine at the University of Cambridge, and Honorary Consultant in Nephrology and Transplantation
- Dr Tom Callender – Clinical Research Fellow at the Department of Public Health and Primary Care, University of Cambridge, Consultant in Public Health Medicine at Cambridge University Hospitals NHS Foundation Trust
- Dr Chen Jin – Associate Principal AI Scientist, Astrazeneca
- van der Schaar lab members: Tim Schubert, Silas Ruhrberg Estévez and Chris Chiu
Recording of the van der Schaar Lab’s forty-ninth Revolutionizing Healthcare engagement session for clinicians which took place virtually on 16 February, 2026.
Session Theme: AI for clinical trial improvement and de-risking
We are grateful for the panellists who joined us:
- Dr Justine Rochon – Head of R&D Data & Quantitative Sciences, Takeda
- Dr Tracy R. Glass Jans – Co-Head Clinical Statistics and Data Management Group, Swiss Tropical and Public Health Institute
- Prof Richard Peck – Honorary Professor of Pharmacology & Therapeutics, University of Liverpool
- Prof Eoin McKinney – Professor in the Department of Medicine, University of Cambridge
- Prof Parashkev Nachev, Professor of Neurology, University College London
- Dr Anders Boyd – Biostatistician, Bern University Hospital, University of Bern and Amsterdam University Medical Centers, University of Amsterdam
If you’re interested in reading Prof Mihaela van der Schaar’s new white paper, “Clinical Trials as Continuously Learning Systems”, which sets out the conceptual framework that was discussed during the session, you can find it here: https://www.vanderschaar-lab.com/clinical-trials-as-continuously-learning-systems/
