Dermatology: Practical and Conceptual


Editorial  |  Dermatol Pract Concept 2018;8(4):1 249

DERMATOLOGY PRACTICAL & CONCEPTUAL
www.derm101.com

Having spent most of our waking hours of the last decades 

working on, thinking and writing about, teaching, and dis-

cussing skin cancer diagnosis, this is the first time that we 

feel compelled to address a question that is so often asked: 

Are we (doctors) going to be left out of the game by artificial 

intelligence (AI)?

This question is usually asked in combination with an 

expression strongly suggestive of an underlying feeling: fear. 

This is absolutely understandable, to be honest. What kinds of 

feelings should we expect of a doctor who has spent a lifetime 

trying to improve his/her capacity to diagnose melanoma and 

who then reads that “machines perform at expert level or 

above” in recognizing melanoma? Joy? Happiness? Hope? 

Let’s be realistic—they are confusion, fear, and anger.

The resistance of professionals to technological develop-

ments that threaten to replace them might sound similar to 

what happened at the time of the Industrial Revolution. One 

could argue that now, like then, if machines can indeed per-

form better than humans, the reluctance of doctors to accept 

this will not be reason enough to delay the advance of this 

new development.

Going back to the key question about whether doctors 

will be replaced by automated algorithms for skin cancer 

diagnosis, our honest answer is, we don’t know. We don’t 

think so, but we are not sure. In contrast, many other scien-

tists are much more confident: Melanoma in the future will 

be diagnosed by AI, the only question is how soon it will 

happen. But with all the tremendous efforts of scientists and 

the investments of huge companies, it should be quite soon.

In fact, never before have we seen so many researchers 

in our field focusing their efforts on the same topic. Never 

before have we seen so many studies published in such a short 

period of time with the same aim: to develop algorithms that 

diagnose melanoma equally as or better than doctors. Most 

of them succeed in demonstrating this is so.

Without aiming to shake the confidence of those who 

foresee algorithms replacing doctors, we think that they might 

benefit by taking into account the following considerations:

1. What has been shown to date: All studies comparing 

melanoma diagnosis by AI with diagnosis by humans have 

been conducted in an experimental setting. To be clear, 

these studies were conducted in front of a computer, tablet, 

or smartphone and were based on evaluation of images. 

None of these algorithms has been tested in a real clinical 

setting (though the reason is not known). Comparing a 

game with images played in front of a computer with a 

diagnosis made in a clinical setting is like comparing a car 

racing video game with a live Formula 1 race. The clinical 

Artificial intelligence and melanoma diagnosis: 
ignoring human nature may lead to  

false predictions
Aimilios Lallas1, Giuseppe Argenziano2

1 First Department of Dermatology and Venereology, Aristotle University, Thessaloniki, Greece

2 Dermatology Unit, University of Campania, Naples, Italy

Citation: Lallas A, Argenziano G. Artificial intelligence and melanoma diagnosis: ignoring human nature may lead to false predictions. 
Dermatol Pract Concept. 2018;8(4):249-251. DOI: https://doi.org/10.5826/dpc.0804a01

Published: October 31, 2018

Copyright: ©2018 Lallas et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, 
which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Corresponding Author: Aimilios Lallas, MD, First Department of Dermatology and Venereology, Aristotle University, 124 Delfon Street, 
54623, Thessaloniki, Greece. Email: emlallas@gmail.com



250 Editorial  |  Dermatol Pract Concept 2018;8(4):1

and nuclear weapons was a great innovation that killed 

millions of people. It goes without saying that these 2 dis-

coveries did not produce results with the same value. In 

fact, the results were polar opposites: extremely beneficial 

and extremely detrimental. If we can agree that the princi-

pal value of our ethical system is human life, then the value 

of research is assessed by the impact on it. In our field, for 

example, the research on epidemiology of melanoma has 

been of high value because it identified high-risk groups, 

positively affecting humanity. The research on histopathol-

ogy of melanoma has been of high value because it identi-

fied prognostic factors, positively affecting humanity. The 

research on dermoscopy of melanoma has been of high 

value because it allowed for earlier detection, positively 

affecting humanity. The research on genetics of melanoma 

has been of high value because it identified mutations that 

might be potential targets of drugs. The research on drugs 

for metastatic melanoma has been of high value because 

it prolonged the survival of patients. The research on 

melanoma diagnosis with AI has not, as of this date, had a 

positive effect on humans. The only system that attempted 

to enter the clinical practice (that one with 98.5% sensitiv-

ity but 10% specificity) was never adopted by clinicians 

because it would have resulted in the unnecessary excision 

of millions of nevi [2]. What is good and what is bad is 

decided by the ethics of a society and not by mathematical 

models. AI recognizes no ethics—only mathematical mod-

els. Are we so sure that we are (or will be) able to protect 

our ethics if AI takes the lead?

5. The nature of humans: Humans like to interact with humans, 

in general, and even more in medicine. The moment that a 

physician examines a patient is a unique interaction during 

which a human being uses all available knowledge and 

mental effort to help another human being. There is a lot of 

interchange of energy in this procedure. This is very much 

superior to a simple judgment on the benign or malignant 

nature of a lesion. The result of a medical consultation is not 

measured only by the absolute improvement of the patient’s 

physical health. Think for a moment about patients with 

end-stage metastatic cancer, those not responsive to treat-

ment, or patients with diseases with no available treatments. 

They build a strong relationship with their doctor, which is 

not measurable or explainable by the absolute improvement 

of their physical health. To think that medical care can be 

simply conducted by mathematical models is tantamount 

to ignoring human nature.

Even if someday it will be possible to satisfactorily address 

points 1 through 4 made above, ignoring human nature and 

investing against it is very likely a prelude to failure.

We are convinced that AI has the potential, in several 

ways, to become an additional precious tool in the hands of 

doctors struggling to reduce melanoma mortality. The main 

examination of patients is a much more multifactorial, 

unpredictable, and complicated process, as compared to 

evaluation of a clinical or dermoscopic photograph in 

front of a computer. This is quite clear for any doctor who 

works in clinical practice even for a short period.

2. The accuracy of diagnosis: Most of the algorithms are 

fed by images of histopathologically diagnosed lesions. 

Therefore, their great performance always has histopa-

thology as a reference point and presupposes that the 

histopathological diagnosis was correct. However, it has 

been well documented that the interpretation of histopa-

thology, especially for “borderline” melanocytic lesions, is 

far from what we would expect from a “gold standard” 

method. Last year, in The BMJ, Elmore et al reported a 

disagreement rate among different pathologists up to 75% 

(!) when trying to differentiate between nevi with moder-

ate to severe dysplasia and early melanoma [1]. Therefore, 

AI algorithms are supplied with images of melanomas that 

might have been diagnosed as nevi by another pathologist 

and nevi that might have been diagnosed as melanomas by 

another pathologist. Can we imagine the potential effects 

of this? Clinicians are able to deal with this problem 

because they are aware of this limitation. They also know 

that morphology (clinical, dermoscopic, histopathological) 

does not always accurately predict biology. Melanomas 

that look like nevi do exist and the inverse is true as well. 

For this reason, clinical management decisions are not 

made only on the basis of morphology. For AI, all this is 

not comprehensible. AI requires clear endpoints (benign-

malignant). “Don’t know” does not exist for AI, and this 

creates a huge risk.

3. The usefulness of mistakes: Throughout history, scientific 

knowledge has improved principally by learning from 

human error. Humans learn from their mistakes, and this 

has proved to be the foundation for progress. As soon as 

a doctor identifies a mistake, he/she tries to explain why it 

happened and what should be done to avoid repeating it. 

AI systems train themselves, without human guidance. They 

learn fast—much faster than we do—and we are unable 

to fully understand the way they become so accurate so 

quickly. As much as we fail to understand how an algorithm 

is able to accurately classify a lesion when humans do not, 

we also fail to adequately explain why AI is wrong when 

it is wrong. Therefore, we will never really know why a 

mistake happened, and it is quite likely that AI will repeat 

the same mistakes because we cannot train it to avoid them.

4. The value of research: Innovation is great, but the value of 

research is not measured by how innovative it is or by the 

impact factor of the journal in which it is published. The 

value of research is measured by its impact on humans. 

The discovery of penicillin was a great innovation that 

saved millions of people. The discovery of atomic bombs 



Editorial  |  Dermatol Pract Concept 2018;8(4):1 251

2. Monheit G, Cognetta AB, Ferris L, et al. The performance of 

MelaFind: a prospective multicenter study. Arch Dermatol. 

2011;147(2):188-194.

Aimilios Lallas, MD

Deputy Editor

Giuseppe Argenziano, MD

Editor-in-Chief

obstacles of this goal are the misconceptions about our role 

as doctors. Maybe by rereading and rethinking what Hip-

pocrates said 2,500 years ago we could find the way.

References
1. Elmore JG, Barnhill RL, Elder DE, et al. Pathologists’ diagnosis 

of invasive melanoma and melanocytic proliferations: observer 

accuracy and reproducibility study. BMJ. 2017 Jun 28;357:j2813.