Artificial Intelligence (AI) refers to the use of software to simulate human intelligence in programming software. The subcategory of machine learning (ML) is highly relevant to our field of dermatology, due to significant progress in this and all the image specialties, including radiology, ophthalmology, and pathology.
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In dermatology, a current significant focus is on machine learning, using image sets and data associated with those images in order to train software to detect patterns in an image. The goal is that in the clinical setting, dermatologists could, for example, capture an image of a suspicious pigmented lesion. Software could scan that image and detect patterns to derive a “score” that indicates the likelihood that the lesion is a melanoma. Based on his/her in vivo assessment, the dermatologist could use that score to
decide to watch the lesion or intervene. Software could also be used more broadly to take an image of a rash, for instance, and generate a short list of potential diagnoses. In either case, software will not actually diagnose a disease; rather it will help guide the human physician to a diagnosis. As such, I prefer to think of AI as Augmented Intelligence, rather than Artificial Intelligence.
The goal is not to replace human intelligence but to augment knowledge and support clinical decision-making.
With that in mind, the following is an update on the current state of AI, what it is, what it isn’t, and how it may impact care.
1. AI WON’T REPLACE DERMATOLOGISTS
While it is true that dermatologic diagnoses can be missed by non-dermatologists, the reality is that dermatologists can also miss diagnoses. Consider the challenge of diagnosing amelanotic melanoma, for instance. AI, through machine learning, has the potential in the future to provide a second opinion right in the exam room. There is no standard to the way that dermatologists screen for skin cancer. In fact, there is a range of behavior between dermatologists, where some are incredibly thorough and others not. Here, AI has great potential in the near term. The use of digital photography combined with machine learning could create an objective approach that helps dermatologists to detect something that might not have been seen with the naked eye.
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Read the full interview with Dr. Papier in Practical Dermatology.
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