Imagine this: A patient calls for her annual skin cancer check. A voice-activated artificial intelligence (AI) tied to your back-office operations answers, sets up the appointment or triages the call. The patient arrives to an appointment for total body imaging with an automated system, coupled with a machine learning system designed to identify suspicious skin lesions that need further single lesions assessment. Following individual lesion assessment, the decision to reassure the patient, monitor the lesion with close-up and dermascopic images or perform a skin biopsy is rendered.
“This initial process of total body evaluation is performed in parallel with a dermatologist, followed by individual lesion examination by both AI-based devices and by clinical inspection, which are ultimately complementary,” says Clara N. Curiel-Lewandrowski, M.D., professor of dermatology at the University of Arizona (UA) and director of the Multidisciplinary Cutaneous Oncology Program, UA Skin Cancer Institute.
This, Dr. Lewandrowski says, is a dynamic process upon which the human brain and computer systems continue to learn from one another.
Some might view the use of non-human intelligence in dermatology as a threat.
“However, it is up to us as dermatologists to transform this technological shift into an opportunity to ‘augment’ our practice with dermatologists at the helm of this paradigm,” says Dr. Lewandrowski.
“With the expected AI revolution in front of us, it is of paradigm importance for dermatologists to familiarize themselves with the ABCDs of machine learning to help guide further development and implementation,” she says.
People might use machine learning, artificial intelligence and augmented intelligence interchangeably, but each is different and important for the future of patient care.
Studies suggest augmented intelligence and machine learning have the potential to be at least as accurate as dermatologists for diagnosing melanoma, says Art Papier, M.D., associate professor of dermatology and medical informatics at the University of Rochester.
Using large image datasets to train softwaer algorithms to pick up on features of skin lesions through pattern recognition isn’t entirely new to the specialty, but it’s a rapidly evolving field in dermatology and other visual specialties, says Dr. Papier, who is CEO of VisualDx, a healthcare informatics company that develops digital health products to enhance diagnostic accuracy.
The technologies will need more than proven efficacy to penetrate dermatology and other specialties, he says. “I don’t know if dermatologists and the medical-legal system are ready for software to help diagnose melanoma, for example. If you have software that’s aiding the diagnosis of melanoma, and the software makes a mistake, who is responsible? It’s not really clear. There isn’t a comfort level yet, though the technologies are getting better and better,” Dr. Papier says.
“I believe looking out five or 10 years into the future you could easily imaging arrays of cameras taking pictures of patients, tracking their changes in their nevi and detecting melanoma much more accurately than people. That’s how fast technology is evolving, but the healthcare system – the legal system – both on the reimbursement side and the medical-legal side need to catch up with these technologies once they’re really proven.
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