Lack of gender diversity is glaring in the AI Med space, but the problem it poses for innovation is subtle

For members of the AI Med community, there is no shortage of enthusiasm, passion, and optimism about the future. We are in Day One of a tremendous shift in thinking about how to approach problems in medicine and healthcare by leveraging cutting-edge technology and ideas from computer science and artificial intelligence. There is a shared sense of excitement about the promise of artificial intelligence (AI) in medicine and good reason to expect breakthroughs from both academia and industry.

However, in the midst of all of this growth and innovation, it’s hard not to notice that women are playing a decidedly small role.

Even though AI in medicine is nascent as a field, the phenomenon is not new; the gender gaps in both medicine [1] and artificial intelligence [2,3], have been well documented.

It’s not surprising, therefore, that the AI Med community, sitting at the intersection of medicine and computer science, reflects roughly the same (or worse) imbalance as we see in either field.

While the lack of women working on problems in AI in medicine is glaring, the problem this disparity represents is subtle. Although debates about gender diversity tend to become mired in identity politics and notions of equality, there is a more pragmatic issue for researchers, entrepreneurs, and investors alike: a lack of diversity is a hindrance to innovation.

Learn why in part 2, coming soon, or see the whole feature in AIMed Magazine issue 04 here.

By Crystal Valentine, PhD

See more on the topic of gender diversity, inclusivity and bias in AI Medicine

Part 2: The business case for diversity

Part 3: How AI can develop biases and discriminate against patients

Part 4: Intentional Inclusivity: a new strategy for solving medical problems


[1] Lautenberger, Diana M., Valerie M. Dandar, Claudia L. Raezer, and Rae Anne Sloane.

“The State of Women in Academic Medicine: The Pipeline and Pathways to Leadership.”  Association of American Medical Colleges, 2014.  Retrieved from:


[2] See, for example: Lauren D’Ambra Faggella .  “Women in Artificial Intelligence – A Visual Study of Leadership Across Industries.”, 2017.  Retrieved from:


[3] “Gender Diversity in Silicon Valley: A Comparison of Silicon Valley Public Companies and Large Public Companies.”  Fenwick & West, LLP,  2016.  Retrieved from:


gender diversity crystal valentine machine learning aimedArticle by: Crystal Valentine, PhD,

Crystal served as the VP of Technology Strategy at MapR Technologies for the past two years.  She has nearly two decades’ experience in big data and machine learning research and practice.  A former professor of computer science at Amherst College, she is the author of several academic publications in the areas of big data, algorithms, computational biology, and high-performance computing, and she holds a patent for Extreme Virtual Memory.  Dr. Valentine was named a Person to Watch in 2018 by Datanami and was awarded the Silver Stevie Award in 2017 for Female Executive of the Year in the computer software category.  She is a member of the Forbes Tech Council and is a frequent contributor to industry journals. Dr. Valentine was a Fulbright Scholar to Italy.