So Many Cases, So Few Dermatologists
In dermatology, AI devices have the potential to streamline the crushing burden of diagnosing skin cancer, said Yun Liu, PhD, a senior staff scientist at Google Research, Mountain View, California, who’s worked on developing machine-learning tools in dermatology among other medical fields. “Many people cannot access dermatology expertise when they most need it, ie, without waiting a long time. This causes substantial morbidity for patients,” Dr. Liu said in an interview.
courtesy Dr. Yun Liu
Dr. Yun Liu
His own research of an AI-based tool to help primary care physicians and nurse practitioners in teledermatology practices diagnose skin conditions documented the shortage of dermatologists to triage lesions, including a finding that only about one quarter of skin conditions are seen by a specialist and that nonspecialists play a pivotal role in the management of skin lesions.
The Centers for Disease Control and Prevention reports that about 6.1 million adults are treated for BCC and SCCs each year. The American Medical Association estimates that 13,200 active dermatologists practice in the United States.
Overcoming Barriers to AI in Dermatology
Before AI makes significant inroads in dermatology, clinicians need to see more verifiable data, said Roxana Daneshjou, MD, PhD, assistant professor of biomedical data science and dermatology at Stanford University, Stanford, California. “One of the challenges is having the availability of models that actually improve clinical care because we have some very early prospective trials on different devices, but we don’t have large-scale randomized clinical trials of AI devices showing definitive behaviors such as improved patient outcomes, that it helps curb skin cancer, or it catches it like dermatologists but helps reduce the biopsy load,” she said. “You need good data.”
courtesy Dr. Roxana Daneshjou
Dr. Roxana Daneshjou
Another challenge she noted was overcoming biases built into medicine. “A lot of the image-based models are built on datasets depicting skin disease on White skin, and those models don’t work so well on people with brown and black skin, who have historically had worse outcomes and also have been underrepresented in dermatology,” said Dr. Daneshjou, an associate editor of NEJM AI.
There’s also the challenge of getting verified AI models into the clinic. “Similar to many medical AI endeavors, developing a proof-of-concept or research prototype is far easier and faster than bringing the development to real users,” Dr. Liu said. “In particular, it is important to conduct thorough validation studies on various patient populations and settings and understand how these AI tools can best fit into the workflow or patient journey.”
A study published in 2023 documented progress Google made in deploying AI models in retina specialty clinics in India and Thailand, Dr. Liu noted.
Another challenge is to avoid overdiagnosis with these new technologies, Dr. Hartman said. Her group’s study showed the DermaSensor had a positive predictive value of 16% and a negative predictive value of 98.5%. “I think there’s some question about how this will factor into overdiagnosis. Could this actually bombard dermatologists more if the positive predictive value’s only 16%?”
One key to dermatologists accepting AI tools is having a transparent process for validating them, Dr. Lee said. “Even with FDA clearance, we don’t have the transparency we need as clinicians, researchers, and advocates of machine learning and AI in healthcare.”
But, Dr. Lee noted, the FDA in June took a step toward illuminating its validation process when it adopted guiding principles for transparency for machine learning–enabled devices. “Once we can get more access to this information and have more transparency, that’s where we can think about actually about making the decision to implement or not implement into local healthcare settings,” she said. The process was further enabled by a White House executive order in October 2023 on the safe, secure, and trustworthy development and use of AI.
The experience with telehealth during the COVID-19 pandemic, when patients and providers quickly embraced the technology to stay connected, serves as a potential template for AI, Dr. Lee noted. “As we’d seen with telehealth through the pandemic, you also need the cultural evolution and the development of the infrastructure around it to actually make sure this is a sustainable implementation and a scalable implementation in healthcare.”
Dr. Lee had no relevant relationships to disclose. Dr. Hartman received funding from DermaSensor for a study. Dr. Witkowski is a cofounder of Sklip. Dr. Liu is an employee of Google Research. Dr. Daneshjou reported financial relationships with MD Algorithms, Revea, and L’Oreal.
A version of this article first appeared on Medscape.com.