Andreas BedorfAhead 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.
