data+AI meets MEDICINE meets EDUCATION

marta g. zanchi
nina capital
Published in
3 min readJan 4, 2022

a webinar for Bytes of Innovation

JANUARY 2022

by Marta Gaia Zanchi

Last month, I was invited to dive deep into the medical field by producing a webinar with Martin Willemink, Stanford physician-scientist and co-founder of Segmed, and Aline Lutz, Segmed’s medical director. With renowned researchers, physicians, and investors, webinar attendees learn about up and coming artificial intelligence technology in healthcare, talking about topics ranging from clinical AI applications to regulatory issues, ethical dilemmas (bias, diversity, etc), current challenges for AI implementation, etc.

As someone who has happily spent over three decades in school after kindergartener, going from elementary student in a tiny public school in the North of Italy to faculty member at Stanford University and Stanford School of Medicine in California, I jumped at the opportunity to speak about the intersection of my favorite three topics: technology, healthcare, and education. Specifically and considering how applications of data and artificial intelligence (AI) in healthcare are maturing, the fundamental premise here is that the need to rethink medical education is becoming increasingly timely.

If you’d like to learn more about how:

  • the use of data to improve decision making pushes the need for skillful medicine-machine interaction and setting of awareness for both its potential and limitations;
  • a growing number of physicians and healthcare professionals will be at the forefront of the opportunity for continuous innovation and maximizing data+AI utility in the real world clinical setting, with nowadays limited incentives and tools to seize it;
  • and, data+AI can be used to rethink education itself for these professions,

find a recording of Bytes of Innovation, Episode 7, on YouTube:

https://www.youtube.com/watch?v=n6ZJIE1Zmtw

and you can download here the slides I produced for this occasion (or contact me to request the PDF).

Do you want to learn more?

Here is a list of selected references:

1 — on how artificial intelligence models often score well on statistical tests of predictive accuracy, or in highly controlled research settings, but perform surprisingly poorly in real-time medical settings.

2 — on how models are more accurate for affluent white male patients, often because they were trained on data that came from that demographic, than they are for black, female, or low-income patients. Some models work well in one geographic region but not in others. There is a ‘calibration drift’.

3 — on the issue of low adherence to reporting guidelines by commonly used machine learning models developed by an electronic health record (EHR) vendor

4 —on a thoughtful call to action for health professions educators

5 — on how the disconnect between clinicians and data scientists can be narrowed by creating a culture of collaboration between these two disciplines (and more), such that the future medical practice will be an explicit partnership among physicians, other health care professionals, machines, and patients

6 — on Biodesign education, to empower medical students to take an active role in the continuous development of emerging technology as Innovators: equipped with the knowledge, skills, and toolkit to be at the forefront of designing new health technology innovation

6 — and finally, two nice overall meta:

Happy watching and reading!

MartaGaia Zanchi

Learn more about Nina Capital here

--

--

nina capital
nina capital

Published in nina capital

nina capital is a new venture capital firm investing at the intersection of healthcare and deep technology.

marta g. zanchi
marta g. zanchi

Written by marta g. zanchi

health∩tech. recognizing the need = primary condition for innovation. founder, managing partner @ninacapital

No responses yet