2019 AAD: Disadvantages and Potential Improvements of Artificial Intelligence in Skin Cancer Detection


Key Points

  • Potential issues with the use of AI in skin cancer detection include interpretation of skin cancer scores, training of AI systems, and teaching algorithms to detect skin cancer in people of color.
  • Session participants agreed that dermatologists helping to shape this technology in its early stages will contribute to ensuring that patients receive the best care possible.

While artificial intelligence (AI) systems for skin cancer detection have shown promise in research settings, there is still a lot of work to be done before the technology is appropriate for real-world use. This was the topic of a scientific session at the 2019 American Academy of Dermatology (AAD) Annual Meeting (D004).

“AI systems for skin cancer detection are still in very early stages,” said Roger S. Ho, MD, MPH, FAAD, Assistant Professor in the Ronald O. Perelman Department of Dermatology at NYU Langone Health. “Nothing is 100% clear-cut yet.”

Issues With AI

One murky area is the skin cancer scores that AI algorithms assign to suspicious lesions. According to Dr. Ho, it’s not yet clear how a dermatologist would interpret those numbers.

The training of AI systems presents an even larger barrier. Hundreds of thousands of photos that have been confirmed as benign or malignant are used to teach the technology to recognize skin cancer, but not all of these images were captured in optimal conditions, said Dr. Ho.

“Just because the computer can read these validated data sets with near-100% accuracy doesn’t mean they can read any image,” he said. “Everyone has a different phone, lighting, background.”

Adewole Adamson, MD, MPP, FAAD, Assistant Professor in the Division of Dermatology at UT Austin Dell Medical School, finds it troubling that the images used so far in training AI systems are almost exclusively of light-skinned patients.

“The algorithm is only as good as what you’ve taught it to do,” he explained. “If you’ve not taught it to diagnose melanoma in skin of color, then you’re at risk of not being able to do it when the algorithm is complete.”

Although skin cancer is more common in people with lighter skin tones, people of color can also develop the disease, and they tend to be diagnosed at later stages, when it’s more difficult to treat. Moreover, Dr. Adamson said, the images used to train AI systems for the most part haven’t included lesions on the palms of hands and soles of feet—places where people of color are disproportionately affected.

“We already know there’s a disparity in how likely you are to have late-stage melanoma depending on skin type,” he said. “That disparity could potentially widen if AI systems are not trained properly.”

Potential Paths to Improvement

Dr. Ho agrees that the training data need to include more racial diversity, as well as a variety of age groups. He doesn’t think AI will ever get to the point of being 100% accurate in skin cancer detection, but like Dr. Adamson, he hopes dermatologists can help shape the technology in its early stages so patients get the best care possible.

Dr. Ho said he would like to see educational content built into skin cancer detection smartphone apps, reminding users that this technology cannot replace a visit with a dermatologist. Dr. Adamson agreed: “Board-certified dermatologists have years of training and experience in recognizing skin cancer, so their judgment should still supersede whatever an algorithm tells you.”

Unlike AI technology, board-certified dermatologists don’t just look at one mole to determine whether it’s problematic. They consider several additional factors, including the other spots on the patient’s body and the evolution of the lesion in question, as well as the individual’s skin type, skin cancer history/risk factors, and sun protection habits.

“Patients need to know that AI is not a perfect system, and it will never be perfect,” Dr. Ho said. “From a dermatologist’s standpoint, we need to know these apps are out there and the technology will continue to grow, so it’s important that we continue to embrace it.”

“I don’t think the ‘man vs machine’ framing of AI and machine learning is correct,” Dr. Adamson added. “It’s going to be more like AI will support the dermatologist and make the dermatologist even better.”

Disclosure: The session participants’ full disclosures can be found at

The content in this post has not been reviewed by the American Society of Clinical Oncology, Inc. (ASCO®) and does not necessarily reflect the ideas and opinions of ASCO®.