Show HN: I'm a dermatologist and I vibe coded a skin cancer learning app
114 comments
·September 7, 2025jmull
sungam
Yes I agree - I could probably have worked out how to do it myself but it would have taken weeks and realistically I would never have had the time to finish it.
jmkni
Same, I've had ideas rattling around in my brain for years which I've just never executed on, because I'm 'pretty sure' they won't work and it's not been worth the effort
I've been coding professionally for ~20 years now, so it's not that I don't know what to do, it's just a time sink
Now I'm blasting through them with AI and getting them out there just in case
They're a bit crap, but better than not existing at all, you never know
amelius
Well, image classification tasks don't require coding at all.
You just need one program that can read the training data, train a model, and then do the classification based on input images from the user.
This works for basically any kind of image, whether it's dogs/cats or skin cancer.
yread
Why? I know tons of coding MDs. Pathologist hacking the original Prince and adding mods also just in assembly. Molecular pathologist organizing their own pipelines and ETLs.
Lots of people like computers but earn a living doing something else
jonahx
He wasn't saying no coding MDs existed. Just that, generally speaking, most MDs would have had to partner with a technical person, which is true. And is now less true than it was before.
haspok
A few years ago there used to be an ML-based app for Android that could classify photos of lesions that you took with your phone and could recommend you a visit to the dermatologist (or not). Unfortunately it seems to be removed now, the webpage is still live (somewhat): https://emdee.ai/
It was done by a small team in Hungary, with the support of MDs of course. (I would guess that the majority of the work was coordinating with MDs, getting them to teach the software... and collecting photos of lesions. Must have been fun!)
They probably could not monatize it (or were not interested, or it was just too much work for a side hustle)... the sad reality of living in Eastern Europe.
I do think that the idea is perfect, it is non-invasive, but could warn you of a potentially very dangerous condition in time. You don't have to wait for the doctor, or unnecessarily visit them. I would actually pay for this as a service.
lelele
There is another one such app: https://www.skinvision.com/
meindnoch
Is this really "invasive melanoma"? https://drmagnuslynch.s3.eu-west-2.amazonaws.com/isic-images...
sungam
According to the metadata supplied with the dataset yes
Could definitely be a misclassification, however a small proportion of moles that look entirely harmless to the naked eye and under the dermatoscope (skin microscope) can be cancerous.
For example, have a look at these images of naevoid melanoma: https://www.google.com/search?tbm=isch&q=naevoid+melanoma
This is why dermatology can be challenging and why AI-based image classification is difficult from a liability/risk perspective
I was previously clinical lead for a melanoma multidisciplinary meeting and 1-2 times per year I would see a patient with a melanoma that presented like this and looking back at previous photos there was no features that would have worried me.
The key thing that I emphasise to patients is that even if a mole looks harmless it is important to monitor for any signs of change since a skin cancer will almost always change in appearance over a period of several months
jonahx
Yeah that seems likely to be a misclassification...
null
jjallen
Very cool. I learned a lot as a non dermatologist but someone with a sister who has had melanoma at a very young age.
I went from 50% to 85% very quickly. And that’s because most of them are skin cancer and that was easy to learn.
So my only advice would be to make closer to 50% actually skin cancer.
Although maybe you want to focus on the bad ones and get people to learn those more.
This was way harder than I thought this detection would be. Makes me want to go to a dermatologist.
sungam
Thanks, this is a good point - I think a 50:50 balance of cancer versus harmless lesions would be better and will change this in a future version.
Of course in reality the vast majority of skin lesions and moles are harmless and the challenge is identifying those that are not and I think that even a short period of focused training like this can help the average person to identify a concerning lesion.
jonahx
Cool project, and helpful for learning.
One concern:
I don't believe the rates that you see "concerning" vs "not-concerning" in the app match the population rates. That is, a random "mole-like spot or thingy" on a random person will have have a much lower base rate of being cancerous than the app would suggest.
Of course, this is necessary to make the learning efficient. But unless you pair it with base rate education it will create a bias for over-concern.
sungam
Yes you are right - the representation is biased due to the image dataset that I have used.
I don't think it would be useful to match the population distribution since the fraction of skin cancers would be tiny (less than 1:1000 of the images) so users would not learn what a skin cancer looks like, however in the next version I will make it closer to 50:50 and highlight the difference from the population distribution.
vindex10
Hi! That's really useful tool!
I wish it also explained the decision making process, how to understand from the picture what is the right answer.
I'm really getting lost between melanoma and seborrheic keratosis / nevus.
I went through ~120 pictures, but couldn't learn to distinguish those.
Also, the guide in the burger menu leads to a page that doesn't exist: https://molecheck.info/how-to-recognise-skin-cancer
sungam
This is very helpful feedback. I will add some more information to help with the diagnosis and add an article in the burger menu with detailed explanation.
Being honest I didn't expect anyone apart from a few of may patients to use the app and certainly did not expect front page HN!
mustaphah
cool, very nice. The real test starts when the first dependency gets deprecated.
sungam
No dependencies - single page app with no backend including all html/css/js
owenversteeg
(spoilers!) here's how to win: everything is cancer, except the common moles and the keratoses.
OP, what are some of the other common options for a spot on the body aside from common moles, cancer, and keratoses? Solar lentigines, freckles, bug bites, eczema? I'm also curious what the actual chance of cancer is given a random mole anywhere on the body, obviously a more involved question.
sungam
Good observations! But hopefully you learned something in coming to those conclusion...
The chance of a random skin lesion being skin cancer is extremely low. Apart from the appearance key things to look for are a lesion that is not going away particularly if it is changing in appearance.
Here are some other common skin lesions: - Dermatofibroma (harmless skin growth) - Actinic keratosis (sun damage) - Milium - Comedome - Acne pustule / nodule - Viral wart - Molluscum contagiousum (harmless viral growth) - Cherry angioma (harmless blood vessel growth) - Spider naevus (another type of blood vessel growth)
There are more than 2000 diagnoses in dermatology so not an exhaustive list!
rfrey
Perfect use of AI assisted coding - a domain expert creating a focused, relatively straightforward (from a programming perspective) app.
@sungam, if your research agenda includes creating AI models for skin cancer, feel free to reach out (email in profile), I make a tool intended to help pure clinical researchers incorporate AI into their research programmes.
sungam
Thanks, I am not currently doing research in this area - my lab-based research is mainly focused on the role of fibroblasts in skin cancer development
saulpw
The hamburger menu "About" and "How To Recognize Skin Cancer" both go to a 404 page that's a copy of a company website called "Revessa Health". Is this your company?
DrewADesign
This is awesome. Great use of AI to realize an idea. Subject matter experts making educational tools is one of the most hopeful things to come out of AI.
It’s just a bummer that it’s far more frequently used to pump wealth to tech investors from the entire class of people that have been creating things on the internet for the past couple of decades, and that projects like this fuel the “why do you oppose fighting cancer” sort of counter arguments against that.
sungam
Thanks for your comment - I'm pleased that people have found it useful and definitely only possible because of AI coding. I agree that this is likely to be applicable to non-experts in many different areas.
DrewADesign
Absolutely. I hope you’ll encourage your colleagues to follow suit!
lukko
I'm a doctor too and would love to hear more about the rationale and process for creating this.
It's quite interesting to have a binary distinction: 'concerned vs not concerned', which I guess would be more relevant for referring clinicians, rather than getting an actual diagnosis. Whereas naming multiple choice 'BCC vs melanoma' would be more of a learning tool useful for medical students..
Echoing the other comments, but it would be interesting to match the cards to the actual incidence in the population or in primary care - although it may be a lot more boring with the amount of harmless naevi!
sungam
Thanks for your comment. The main motivation for me in developing the app was that lots of my patients wanted me to guide them to a resource that can help them improve their ability to recognise skin cancer and, in my view, a good way to learn is to be forced to make a decision an then receive feedback on that decision.
For the patient I think the decision actually is binary - either (i) I contact a doctor about this skin lesion now or (ii) I wait for a bit to see what happens or do nothing. In reality most skin cancers are very obvious even to a non-expert and the reason they are missed are that patients are not checking their skin or have no idea what to look for.
I think you are right about the incidence - would be better to be a more balanced distribution of benign versus malignant, but I don't think it would be good to just show 99% harmless moles and 1% cancers (which is probably the accurate representation of skin lesions in primary care) since it would take too long for patients to learn the appearance of skin cancer.
I kind of love the diy aspect of ai coding.
A dermatologist a short while ago with this idea would have to find a willing and able partner to do a bunch of work -- meaning that most likely it would just remain an idea.
This isn't just for non-tech people either -- I have a decades long list of ideas I'd like to work on but simply do not have time for. So now I'm cranking up the ol' AI agents an seeing what I can do about it.