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Roxana Daneshjou

As seen in: NEJM AI, Nature, Wiley Online Library, The BMJ, The Hill, JAMA (The Journal of the American Medical Association), Science Magazine, BioRxiv, HCP Live, medRxiv, Dialnet - Universidad de La Rioja and
Assistant professor of Biomedical Data Science @StanfordDBDS and Dermatology @StanfordMed | AI/ML & precision health | @Rice_BioE alum | @pdsoros Fellow 2014
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Articles

What Dermatologists Can Do to Make AI More Equitable

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By Roxana Daneshjou, Morayo Adisa
| consultantlive.com
Roxana Daneshjou, MD, PhD, assistant professor of biomedical data science and dermatology at Stanford University, outlined concrete steps the medical and technology fields can take to close AI equity gaps for patients with skin of color.1,2 Her discussion of these steps continues a conversation with Morayo Adisa, MD, medical director of Dermatology Physicians SC in Chicago and Kenilworth, Illinois, which took place during the latest episode of Skin of Color Savvy: The Art and Science of...
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Does Artificial Intelligence Worsen Health Care Disparities?

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By Roxana Daneshjou, Morayo Adisa
| consultantlive.com
Roxana Daneshjou, MD, PhD, assistant professor of biomedical data science and dermatology at Stanford University, spoke in a recent podcast about how artificial intelligence (AI) systems can replicate structural racism embedded in health care.
Open in Who Shared View Duplicates (2) Issue with article?

Divergent impacts of explainable AI for dermatological diagnosis on clinicians versus lay people

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By Haoyu Hu, Haoran Zhang, Will Wang, Reina Wang, Luis R. Soenksen, Omar Badri, Sheharbano Jafry, Elise Burger, Erik Duhaime, Asif Qasim, Hause Lin, Janis Pereira, Paulius Mui, Alejandro Gru, Noémie Elhadad, Lena Mamykina, Matthew Groh, Philipp Tschandl, Roxana Daneshjou, Marzyeh Ghassemi, Lotanna Nwandu, Jonathan Hershon
| Nature Verified
Abstract Artificial intelligence (AI) is increasingly permeating healthcare, from serving as a physician assistant to powering consumer applications. The opacity of AI algorithms makes the ability of humans to interact with AI algorithms challenging. To overcome this limitation, explainable AI (XAI) provides insight into AI decision-making, but evidence suggests that XAI can paradoxically induce bias in the human decision-making process.
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