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.
amelius · 7m ago
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.
sungam · 24m ago
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 · 1h ago
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
cjbgkagh · 11m ago
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.
jjallen · 2h ago
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 · 2h ago
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.
rfrey · 1h ago
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 · 21m ago
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
vindex10 · 1h ago
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.
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!
lazarus01 · 44m ago
What you created is a version of “am I hot or not” for skin cancer. The idea is constrained to the limitations of your programming capability. Showing a photo and creating 3 buttons with a static response is not very helpful. These are the limits of vibe coding.
I was thinking to train a convnet to accurately classify pictures of moles as normal vs abnormal. The user can take a photo and upload it to a diagnostic website and get a diagnosis.
It doesn’t seem like an overly complex model to develop and there is plenty of data referring to photos that show normal vs abnormal moles.
I wonder why a product hasn’t been developed, where we are using image detection on our phones to actively screen for skin cancer. Seems like a no brainer.
My thinking is there are not enough deaths to motivate the work. Dying from melanoma is nasty.
cjbgkagh · 2m ago
“am I hot or not” is a great paradigm for many things, is it porn or not etc. 3 buttons are perfectly sufficient for getting this information from the users for rating systems in general. This is not a rating system as samples are labeled from actual test results.
sungam · 27m ago
The goal of my app is to educate patients so that they recognise that they need to take further action.
Regarding AI-assisted skin cancer diagnosis: This is a huge area that started with the publication of Esteva et al (https://www.nature.com/articles/nature21056) and there have been hundreds of publications since. There are large publicly available datasets that anyone can work with (https://challenge.isic-archive.com/).
I have no doubt that it will be possible to train an AI model to perform at the same level as a dermatologist and AI models will become increasingly relevant. The main challenge at the moment is navigating uncertainty / liability since a very small proportion of moles / skin lesions that appear entirely harmless both the naked eye and with the dermatoscope (skin microscope) are cancerous.
lazarus01 · 9m ago
Thanks for including those information resources. This is something I’m interested in digging deeper into.
nlawalker · 38m ago
I disagree, I found this very helpful. In a very short amount of time I was granted the insight, in a very clear way, that I am not very good at determining whether moles need treatment based on how they look.
raincole · 35m ago
I really don't think you can publish the app you described in any developed country without an army of lawyers. And this army had better be prepared to lose many battles.
thimkerbell · 26m ago
Could it be built from an island off Costa Rica?
g-mork · 36m ago
You're talking down to a technically unskilled dermatologist for successfully producing a useful app without the help of an engineer? Curious behaviour! This is far from the first story like this, in combination they're a potent bellwether for the future of our little corner of the universe, engaging in denials really doesn't help anyone
lazarus01 · 23m ago
I wouldn’t call it “successful” or “useful”. It was a low effort attempt to make something interesting and it wasn’t. It’s a response to the hype of vibe coding. Lowers the bar for what good software really is.
Perhaps you may want to question your bias and ability to process criticism.
Anyone who shares their ideas publicly will receive criticism. Not only is it ok, it’s helpful to expand the discussion beyond your bias.
hombre_fatal · 17m ago
Every dermatologist (and developer with a dermatologist relative) in the world has had that app idea since most of your daily checkups are moles that you categorize in seconds.
The app already exists btw. Did nobody in this thread google it before saying it couldn't work?
rogerrogerr · 40m ago
We need liability reform - any app in the US would either tell you ~everything is skin cancer, or it would show one false negative and get sued into oblivion.
i000 · 9m ago
What an utterly disappointing comment. FWIW I spent 15min on the app, and found it very helpful to see examples of the various kinds of skin lesion - it will likely motivate me to see a doctor when I see a similar malignant skin lesion. Educating people is very helpful.
hedgehog · 36m ago
Developing a model like that, and evaluating it with practicing doctors, is a good learning project.
omer9 · 56m ago
Every image with a pen marking is dangerous/cancer. Check.
sungam · 13m ago
Haha not all of them - but actually this is an important observation because when training convnets for skin cancer diagnosis the presence of the pen marking can be an important confounding factor that needs to be accounted for
DrewADesign · 2h ago
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.
SilentM68 · 15m ago
Interesting. I wish there existed an app for actually finding a cure for every killer decease.
dhruvbird · 29m ago
This is awesome! After about 50 attempts, I have a much better sense of what to look out for when I see something. I wish there were more such focused apps. for specific specific health related things.
sungam · 25m ago
Thanks, I am pleased you found it useful. This is exactly how a dermatologist learns to recognise skin cancer - by making decisions and then getting feedback. I think anyone can improve dramatically with an hour or so of practice and then this skill is useful lifelong.
andreasgl · 1h ago
I like the project! Congrats on the launch.
As I understand it, size is one of the key indicators of melanoma. But in some of these images, it’s difficult to tell whether the mole is 1 mm or 10 mm. I assume your image set doesn’t include size information. If you can find sources with rulers or some kind of scale, that would be very helpful.
jampekka · 3h ago
To my eye most of the basal cell carsinomas looked like everyday rashes, pimples or scratches. My correct rate was under chance. This could be hypochondria inducing for many?
sungam · 2h ago
Basal cell carcinomas can look very similar to other harmless skin lesions. The key thing is that they will not resolve with time and will slowly grow whereas a rash, pimple or scratch will resolve over a few months.
Fortunately basal cell carciomas are very slow growing and do not spread elsewhere in the body or cause other health issues and a delay of a few months in diagnosis does not have a big impact on outcome.
lukko · 2h ago
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!
orliesaurus · 31m ago
What did you use to build this? Where did you deploy?
sungam · 10m ago
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.
agnishom · 3h ago
This is a good use of vibecoding. The main "algorithm" to be implemented is very straightforward , and for the hard stuff, we have an expert.
sungam · 2h ago
Yes I think so - it's a very simple application but I would never have had the time to do it myself.
If anyone is interested: 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.
bobmcnamara · 26m ago
I'm around 75%
Idea: distribution of player scores
I'm going to get some models checked out.
sungam · 24m ago
Great idea! Will aim to add to the next version
rcruzeiro · 1h ago
I’ve learned that basal cell carcinoma can look scarily unremarkable!
Would be useful to add some explanation on the defining features that would give it away to a dermatologist.
redox99 · 1h ago
Are there an equal amount of cancer and non cancer images? In my case the vast majority (I'd say around 75%) are cancerous.
y-curious · 2h ago
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 · 2h ago
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.
Classically, BCC's have a pearly surface and 'rolled' edges, which differentiates them from pimples.
bix6 · 41m ago
The two links in your menu don’t work but otherwise this is awesome!
ziptron · 1h ago
Thank you for making this.
My dad passed away from squamous cell carcinoma in 2010. In retrospect, through my casual research into the space and tools like this one, it occurs to me that the entire event was likely preventable and occurred merely because we did not react quickly enough to the cancer’s presence.
toledocavani · 2h ago
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.
scotty79 · 2h ago
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.
minton · 1h ago
The zoomed in view is great if you’re commonly examining under magnification, but perhaps a slightly less zoomed view (or ability to switch between each) might make this more practical for common folks.
pama · 1h ago
Thanks for the reminder to schedule the annual dermatology appointment.
nextworddev · 58m ago
Can people sue you for malpractice if something goes wrong?
leetrout · 3h ago
Why do the images get a weird offset slice effect on safari on mobile after submitting a guess with the buttons?
sungam · 2h ago
No idea, I will look into this
childintime · 1h ago
would a tool that can take a truly tiny sample out of the lesion be a valuable complement? so we can send it in (with the tool) and get a lab test done?
aegypti · 3h ago
Basal Cell Carcinoma is very gross!
Think a set number of questions to start with would be good. Not sure if there’s an end point, I drifted off after ~20 or so
sungam · 2h ago
Good idea will implement this is a future version
nasir · 2h ago
Learned quite a bit and seems like a basic but necessary thing to know about!
sungam · 2h ago
Thanks, I'm glad you found it useful. My patients were constantly asking for a way to learn what skin cancer looked like beyond the ABCDE rule and I wanted to try and introduce a gamification aspect to it.
NoiseBert69 · 2h ago
What happens if I make a picture of my cat with it?
sungam · 2h ago
Not sure how you would do this but feel free to try!
hopelite · 1h ago
Today I learned most things are cancer
lvl155 · 34m ago
Dude, everything is “I am concerned.”
k2xl · 3h ago
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 · 2h ago
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.
quantummagic · 2h ago
Nice Job. This really highlights that people who obsess in telling us that "AI hallucinates", and "AI isn't intelligent", are missing the point. At the end of the day, it's simply useful, and incredibly empowering.
sungam · 2h ago
Yes, without AI this app definitely would not exist as I would not have had time to make it. I think that this will apply to multiple other areas within the economy.
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.
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.
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
I would love to see more of such classifiers for other medical conditions, googling for images tends not to produce a representative sample.
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.
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.
@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.
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
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!
I was thinking to train a convnet to accurately classify pictures of moles as normal vs abnormal. The user can take a photo and upload it to a diagnostic website and get a diagnosis.
It doesn’t seem like an overly complex model to develop and there is plenty of data referring to photos that show normal vs abnormal moles.
I wonder why a product hasn’t been developed, where we are using image detection on our phones to actively screen for skin cancer. Seems like a no brainer.
My thinking is there are not enough deaths to motivate the work. Dying from melanoma is nasty.
Regarding AI-assisted skin cancer diagnosis: This is a huge area that started with the publication of Esteva et al (https://www.nature.com/articles/nature21056) and there have been hundreds of publications since. There are large publicly available datasets that anyone can work with (https://challenge.isic-archive.com/).
My lab has previously trained / evaluated convnets for diagnosis of skin cancer e.g. see this publication: https://pubmed.ncbi.nlm.nih.gov/32931808/
I have no doubt that it will be possible to train an AI model to perform at the same level as a dermatologist and AI models will become increasingly relevant. The main challenge at the moment is navigating uncertainty / liability since a very small proportion of moles / skin lesions that appear entirely harmless both the naked eye and with the dermatoscope (skin microscope) are cancerous.
Perhaps you may want to question your bias and ability to process criticism.
Anyone who shares their ideas publicly will receive criticism. Not only is it ok, it’s helpful to expand the discussion beyond your bias.
The app already exists btw. Did nobody in this thread google it before saying it couldn't work?
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.
As I understand it, size is one of the key indicators of melanoma. But in some of these images, it’s difficult to tell whether the mole is 1 mm or 10 mm. I assume your image set doesn’t include size information. If you can find sources with rulers or some kind of scale, that would be very helpful.
Fortunately basal cell carciomas are very slow growing and do not spread elsewhere in the body or cause other health issues and a delay of a few months in diagnosis does not have a big impact on outcome.
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!
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.
If anyone is interested: 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.
Idea: distribution of player scores
I'm going to get some models checked out.
Would be useful to add some explanation on the defining features that would give it away to a dermatologist.
Photos of basal cell carcinoma (no affiliation): https://dermnetnz.org/topics/basal-cell-carcinoma
My dad passed away from squamous cell carcinoma in 2010. In retrospect, through my casual research into the space and tools like this one, it occurs to me that the entire event was likely preventable and occurred merely because we did not react quickly enough to the cancer’s presence.
Think a set number of questions to start with would be good. Not sure if there’s an end point, I drifted off after ~20 or so
One or two seemed quite obvious to me as concerning or not but turned out to be the other way