We need more educational or clinical projects that have a real-world impact on reducing disparities in dermatology, said Art Papier, MD, dermatologist, CEO, VisualDx.
Art Papier, MD, dermatologist, CEO, VisualDx, talks about disparity prevention in dermatology, including the second year of Project IMPACT, and considerations in artificial intelligence (AI) and machine learning.
Transcript
What are your goals for the second year of Project IMPACT?
I think we really want to see specific projects that are either educational or clinical that have real-world impact. Project IMPACT, the name, we chose that really because we want to be substantial. We don't want to just be performative and be just discussing an issue. We want to see some results. In the second year, we want to see more people involved and we want to see specific projects, and we're hoping to do specific projects around medical education.
How can we ensure future projects are including underserved populations in their research?
I think there's tremendous interest in primary care. So, in rural areas or inner city areas where you have federally qualified health clinics serving the underserved, there's a real need to bring the kind of information that those health care professionals can use in real time. In my work, I've been thinking about this problem, "What's the right information for the right patient population?" So we're thinking about, if the clinic is urban, what do they need? If the clinic's rural, what's the patient population? How do we really bring customized information to each population?
Since dermatological conditions can appear differently depending on skin type, what steps should be taken in AI development?
People are beginning to understand that AI and machine learning are totally dependent on good quality data. It's garbage in, garbage out. You have to have good data, and if you train your algorithms just on white skin, it's not going to work on brown skin.
So in our work at VisualDx, as an example, we've been collecting imagery for over 20 years, and since the start of our efforts, it's always been equitable. We have something like 32% of the imagery in the system is in brown skin, and we have tags on that imagery so that when we train our AI, it's equitable. This is key to having equity in AI and machine learning—it's the data you train on and how precise the data is.
Overcoming Employment Barriers for Lasting Social Impact: Freedom House 2.0 and Pathways to Work
April 16th 2024To help celebrate and recognize National Minority Health Month, we are bringing you a special month-long podcast series with our Strategic Alliance Partner, UPMC Health Plan. Welcome to our second episode, in which we learn all about Freedom House 2.0 and the Pathways to Work program.
Listen
Dr Michael Farwell on FDG PET/CT Imaging to Predict Immunotherapy Response in Advanced Melanoma
April 15th 2024Michael Farwell, MD, associate professor of radiology at the Hospital of the University of Pennsylvania, provides insights into a study on the benefits of using 18F-fluorodeoxyglucose (FDG) PET/CT imaging to detect metabolic tumor changes in skin cancer.
Read More
Making Giant Strides in Maternity Health Through Baby Steps
April 9th 2024To help celebrate and recognize National Minority Health Month, we are kicking off a special month-long podcast series with our strategic alliance partner, UPMC Health Plan. Welcome to our first episode, which is all about the Baby Steps Maternity Program and its mission to support women throughout every step of their pregnancy journey.
Listen
Delays in HS Diagnosis, Treatment Shed Light on Provider Education Gaps
April 5th 2024Patients with hidradenitis suppurativa (HS) in Canada often wait long periods of time to receive an HS diagnosis and receive evidence-based therapy, highlighting the need for increased interdisciplinary education on HS management.
Read More