Revolutionizing Healthcare is a series of engagement sessions aiming to share ideas and discuss topics that will define the future of machine learning in healthcare. These events target the healthcare community and focus on challenges and opportunities in clinical application of machine learning. We now have roughly 400 clinicians from around the world registered to participate in these sessions.
As a lab, our purpose is to create new and powerful machine learning techniques and methods that can revolutionize healthcare. This doesn’t happen in a vacuum. At inception, we are inspired by ideas and discussions; in implementation, we need connections, trust, and partnership to make a real difference.
While you can learn about our work at major conferences in machine learning or in our papers, we think it’s a better idea to create a community and keep these conversations going. We’re also aware that many people—both in healthcare and machine learning—have questions about what we do, and how they can contribute.
For more information about Revolutionizing Healthcare—and to sign up to join in—please have a look at the sections below, and keep checking for new updates.
Themed discussion sessions specifically for healthcare professionals (primarily clinicians).
We would like to:
– introduce machine learning concepts as they relate to healthcare
– spark new projects and collaborations
– demonstrate the real-world impact of machine learning in clinical settings
– discuss institutional barriers preventing wider adoption
– develop a shared vision for the future of machine learning in healthcare.
Standard session format:
– brief introductory presentation
– roundtable discussion featuring clinicians
– open Q&A
Next session on February 8!
Our next session will take place on February 8, and will run for just over 1 hour. The session will be a roundtable on the potential of machine learning to transform early detection and diagnosis (ED&D) of diseases such as cancer. For more information on this session, please visit this page.
If you’d like to join us for this or future sessions, please sign up using the form below, and check this page regularly.
Cambridge, United Kingdom 2022-02-08 at 16:00 GMT
Paris, France 2022-02-08 at 17:00 CET
New York, USA 2022-02-08 at 11:00 EST
San Francisco, USA 2022-02-08 at 08:00 PST
Beijing, China 2022-02-09 at 00:00 CST
More time zones can be found here.
(for practicing healthcare professionals)
CPD accreditation for UK-based clinicians
We’re delighted to announce that Royal College of Physicians has approved the Revolutionizing Healthcare series as a source of continuing professional development (CPD) credits.
If you are a clinician practicing in the UK, you can claim CPD credits by joining our live sessions or by viewing archived versions up to a year after they took place. Each session lasts at least 1 hour, and is worth 1 CPD credit.
If you plan to claim credits for attendance, contact us via the simple instructions we’ll provide during the session, and we’ll issue a certificate of attendance. You can then apply for credits within the CPD system administered by the Royal College of Physicians.
An PDF overview of Revolutionizing Healthcare, created as part of the CPD accreditation application process, can be found here.
Session 1: what machine learning can offer healthcare
Ahead 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.
Session 2: a framework for ML for healthcare
Ahead of this session, Mihaela published a piece of content entitled Machine learning for healthcare: Towards a unifying framework.
Session 3: tools for acute care
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.
Session 4: ML tools for cancer (risks, screening, diagnosis)
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.
Session 5: ML tools for cancer (post-diagnosis care)
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.
Session 6: roundtable on interpretability
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.
Session 7: second roundtable on interpretability
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.
Session 8: roundtable on personalized therapeutics
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 personalized therapeutics, including the limitations of existing clinical guidelines, and the potential for machine learning and AI to aid the development of more personalized and useful guidelines in healthcare.
Session 9: roundtable on AI/ML decision-support tools
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).
Session 10: getting ML-powered tools in the hands of clinicians (part 1)
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.
Session 11: getting ML-powered tools in the hands of clinicians (part 2)
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.
Session 12: how can AI/ML transform organ transplantation?
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.