Show HN: I'm a dermatologist and I vibe coded a skin cancer learning app

Posted by sungam 9/7/2025

Show HN: I'm a dermatologist and I vibe coded a skin cancer learning app(molecheck.info)

Coded using Gemini Pro 2.5 (free version) in about 2-3 hours.

Single file including all html/js/css, Vanilla JS, no backend, scores persisted with localStorage.

Deployed using ubuntu/apache2/python/flask on a £5 Digital Ocean server (but could have been hosted on a static hosting provider as it's just a single page with no backend).

Images / metadata stored in an AWS S3 bucket.

429 points | 259 commentspage 3

edimoldovan 9/13/2025|

Pretty nice work.

One of the problems I encountered in my career was to understand the actual need. You have that entirely.

AI coming in and giving you the ability to do what you and others need and most devs couldn't do without you explainin the need and requirement in detail, is pretty amazing.

Maybe you teach this to some non-dev peers of yours?

noisy_boy 9/8/2025||

This just highlights that domain experts became even more valuable with LLMs. Not only they know the domain, now they realise their ideas too with minor effort. Doesn't bode well for programmers in non-tech areas.

sungam 9/8/2025|

This is true, however I am sure that AI-based image analysis will also replace some of the work of dermatologists eventually so nobody is safe...

y-curious 9/7/2025||

Half of these basal cell carcinomas look like picked pimples. Are there any sort of protocols for self screening for carcinomas a la self-testing ones testicles? I've never heard of anything other than the ABCDE for moles

sungam 9/7/2025||

Look for any new skin lesion that is not resolving with time especially if persisting for a number of months. You can take photos of different body sites and repeat every couple of months and then put the two photos side by side on a computer screen to look for any difference. If unsure about the lesions that are present then worth getting a full skin check with a dermatologist as a baseline so that you then just need to look for new/changing lesions.

Photos of basal cell carcinoma (no affiliation): https://dermnetnz.org/topics/basal-cell-carcinoma

lukko 9/7/2025||

Classically, BCC's have a pearly surface and 'rolled' edges, which differentiates them from pimples.

toledocavani 9/7/2025||

Is there any reputable (reviewed, endorsed) AI model to detect skin cancer? I have a lot of similar moles, and playing with this app make me concern about all of them.

sungam 9/7/2025||

Lots of models out there but I would not trust any for diagnosis without review of a dermatologist yet. The challenge is unanticipated edge cases and managing risk/liability/regulation. I have no doubt that if a major AI company focused on this problem then these issues could be overcome with current technology but perhaps the market is not big enough to justify the investment required.

scotty79 9/7/2025||

I heard that the good rule of thumb is to be concerned about unique ones. It much less probable that you develop exactly same looking cancer in two unrelated spots.

sungam 9/7/2025||

Yes we call this the "Ugly Duckling" sign

zoeey 9/8/2025||

This project really resonates with me. I have a few friends in healthcare who had great ideas for patient tools, but without a technical partner and no budget for a development team, nothing ever came of them.

Seeing someone actually build something like this, even if it's not perfect, gives me a sense of hope. When you combine domain expertise with some AI tools, you don’t have to wait around for someone else. You can just start.

k2xl 9/7/2025||

Wow this game just proves to me how difficult your job is. I am basically getting 50%.

One or two seemed quite obvious to me as concerning or not but turned out to be the other way

sungam 9/7/2025|

It can be challenging but the large majority of skin cancers are fairly obvious and the main reason people don't spot them is because they are not checking their skin regularly and don't have any idea what to look for. Hopefully this app will help patients to learn the basic things to look for.

cjbgkagh 9/7/2025||

This is great, I had no idea how off base I was with my assumptions. It’ll be interesting to keep the usage data to find out what kinds of images people have the most trouble with. As in what kind of mole is the most likely to be missed. Though perhaps dermatologist already know that answer well enough.

I would love to see more of such classifiers for other medical conditions, googling for images tends not to produce a representative sample.

sungam 9/7/2025|

Thanks, I'm really pleased that people have found it useful! Wasn't expecting much from the app just coded it in an evening as it's something I've been thinking about for years

reilly3000 9/8/2025||

There may be an interesting opportunity to gather data on the accuracy of guesses per image. You could use something like Google analytics, but simple server-side logging is more private and keeps the page light.

The question could be: What images are most often mistaken? What characteristics do they share? Knowing the highest false negative images would be really valuable people to know what not to ignore.

thr0waway001 9/8/2025||

This was always the dream for people with real ideas for an app but lacked the knowhow to build it.

Namely: “I have an idea for an app but I don’t know how to do it and I don’t want to spend all my time and resources on the app either. The app is a means to an end, not the end itself.”

We are now living in a time where getting to the end is much more possible.

redox99 9/7/2025|

Are there an equal amount of cancer and non cancer images? In my case the vast majority (I'd say around 75%) are cancerous.

sungam 9/7/2025|

You are right - the distribution is not equal largely because the dataset that I used had less pictures of harmless moles but I will aim to make it 50:50 in the next version

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