40 Years of the ABCDs: Darrell Rigel, MD, MS, Reflects on the Evolution of Global Melanoma Detection

Four decades ago, a deceptively simple mnemonic transformed how clinicians, and eventually the public, recognized melanoma. The ABCDs of melanoma, now embedded in medical education and clinical practice worldwide, began not as a grand initiative but as a practical teaching solution developed by 3 dermatologists at New York University (NYU) seeking to clarify what early melanoma actually looks like.

Dermatology Times sat down for an exclusive interview with Darrell Rigel, MD, MS, a clinical professor of dermatology at NYU Grossman School of Medicine, an adjunct clinical professor at the University of Texas Southwestern Medical Center, and a dermatology consultant at Cooper Clinic in Dallas, Texas, who developed the ABCD method in 1985 with his colleagues Robert “Bob” Friedman, MD, and Alfred “Al” Kopf, MD. At the time, Rigel and Friedman were young attendings working alongside their mentor, Kopf, a pioneering figure in melanoma research at NYU. The initial idea emerged from a straightforward question: How could clinicians teach pattern recognition for a disease where early detection is absolutely critical?

The Need for a Teaching Tool

To better understand how experienced clinicians made these determinations, the trio reviewed 20 archival Kodachrome slides from the NYU dermatology photo library and presented them to senior faculty members. When asked to identify the lesions, the faculty consistently recognized melanoma immediately. Yet when asked to articulate the diagnostic criteria guiding their judgment, the answers were less precise—often simply that the lesion just “looked like a melanoma.”

This exercise confirmed the need for a more systematic approach to describing the visual hallmarks of early melanoma. Rigel and Friedman’s solution was a simple mnemonic framework based on key morphological features: asymmetry, border irregularity, and color variation.

“Once you’ve had enough exposure, you can easily know what melanoma looks like,” Rigel told Dermatology Times. “But how do you get to that level? We decided it had to be as easy as ABC.”

The Birth of the ABCDs

Early melanomas frequently lack the symmetry seen in benign nevi. They often display uneven, poorly defined borders and contain multiple colors rather than a uniform pigment pattern. When Rigel and Friedman presented the idea to Kopf, he recommended adding an objective size parameter to help differentiate potentially concerning lesions from smaller benign findings. In the NYU melanoma database, approximately 95% of melanomas measured at least 6 mm in diameter.

This observation led to the final component of the framework, and thus, the 4 features—asymmetry, border irregularity, color variation, and diameter—became known as the ABCDs of melanoma, later published in CA: A Cancer Journal for Clinicians.¹

Global Impact and Evolution

The framework quickly gained traction. Its simplicity allowed it to be adopted across medical disciplines and incorporated into educational materials and public health campaigns. Rigel recounted anecdotes from patients and providers whom he’s met all around the world, thanking him for the simplicity of this alphabetical structure. “It’s in every medical school curriculum now. It’s in every residency training program, even beyond dermatology,” he said. “It really took off and spread across the world. I’ve got newspaper clippings in other languages.”

As knowledge of melanoma biology evolved, clinicians recognized that dynamic changes in lesions over time also provided critical diagnostic information. This insight led to the addition of a fifth component: evolution.

“We know that early melanomas grow,” Rigel said. “They grow disproportionately faster than surrounding pigmented lesions. So the concept behind ‘E’ was that relative growth is an important sign.”

The Next Era of Detection

Although the ABCDE criteria remain foundational, melanoma diagnostics have entered a new technological era, explored further in CA’s latest publication, “Advances in the Noninvasive Diagnosis of Melanoma—40 Years Beyond the ABCDs.”² Over the past several decades, dermatology has seen the emergence of multiple noninvasive diagnostic technologies designed to assist clinicians in evaluating pigmented lesions.

Early devices sought to improve melanoma detection through advanced imaging and analytical techniques, including electrical impedance spectroscopy and algorithm-based pattern recognition systems. One early device, MelaFind, demonstrated very high sensitivity but limited specificity, identifying most melanomas while generating a substantial number of false positives.

“All these different devices are trying to do the same thing,” Rigel said. “Can you, in fact, look at an image and say, ‘Is it melanoma—or a high probability of melanoma—without doing a biopsy?’”

More recently, artificial intelligence (AI) has become a central focus of diagnostic innovation. AI-based systems analyze digital images of pigmented lesions using algorithms that can evaluate complex patterns and variables that may not be perceptible to the human eye. These tools are designed not to replace clinicians but to augment diagnostic decision-making.

In comparative studies, the best diagnostic performances often occur when radiologists use AI as a complementary tool rather than as a stand-alone diagnostic method.³ Rigel believes this collaborative model will likely define the future of dermatology bringing us to the “next level in noninvasive diagnosis.”

Building a Lasting Legacy

Reflecting on the past 4 decades, Rigel emphasizes that the ABCDs of melanoma represent more than a mnemonic—they embody a broader shift toward early detection, public education, and collaborative prevention strategies. Both of his coauthors, Kopf and Friedman, have since passed away, but Rigel believes that the enduring success of the ABCDs lies in their simplicity, making the tool one of the most widely recognized diagnostic frameworks in all of medicine.

“You always want to make a difference in your life, and I’ve been very blessed to do so many things,” Rigel said. “But this is something that Bob, Al, and I made a real difference with. We never had any idea that it would catch on this much.”

Since this achievement 40 years ago, Rigel has authored more than 300 research articles, given more than 1000 presentations at medical conferences worldwide, won numerous awards, and served in presidencies and leadership positions with the American Academy of Dermatology, the American Board of Dermatology, and other organizations. But when asked to define his professional legacy, Rigel did not hesitate.

“If I had to pick one thing for my legacy, I’d pick this,” he said fondly. “I'm so proud that this happened and that we saved so many lives. We were a great team.”

References

1. Friedman RJ, Rigel DS, Kopf AW. Early detection of malignant melanoma: the role of physician examination and self-examination of the skin. CA Cancer J Clin. 1985;35(3):130-151. doi:10.3322/canjclin.35.3.130

2. Burshtein J, Witkowski A, Zakria D, et al. Advances in the noninvasive diagnosis of melanoma—40 years beyond the ABCDs. CA Cancer J Clin. 2026;76(1):e70065. doi:10.3322/caac.70065

3. Nadour N, Duguet T, Zahedi S, Figoni H, Liard R. Diagnostic accuracy of artificial intelligence compared to family physicians and dermatologists for skin conditions: a systematic review and meta-analysis. BMC Prim Care. 2025;26(1):384. doi:10.1186/s12875-025-03073-9