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 will target the healthcare community and focus on challenges and opportunities in clinical application of machine learning.
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
Seventh session on April 27!
Our next session will be on April 27 at 16:00 BST, and will run for roughly 1 hour.
In this session we will continuing to focus on interpretability, and incorporating a live round-table with clinicians into the session format.
If you’d like to join us then (or for future sessions), please sign up using the form below, and check this page regularly.
Cambridge, United Kingdom 2021-04-27 at 16:00 BST
Paris, France 2021-04-27 at 17:00 CEST
New York, USA 2021-04-27 at 11:00 EDT
San Francisco, USA 2021-04-27 at 08:00 PDT
Beijing, China 2021-04-27 at 23:00 CST
Shanghai, China 2021-04-27 at 23:00 CST
To add your city/time zone, click 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 overview of Revolutionizing Healthcare, created as part of the CPD accreditation application process, can be found below.2021_01_Overview_Revolutionizing_Healthcare
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.