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.
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.
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Focusing on "but security lol" is a bad take, IMO. Every early attempt is bad at something. Be it security, or scale, or any number of problems. Validating early is good. Giving non-tech people a chance is good. If an idea is worth pursuing, you can always redo it with "experts". But you can't afford experts (hell, you can't even afford amateurs) for every idea you want put into an MVP.
True productivity is when what is produced is of benefit.
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 don't agree. I think because of llm/vibe coding my random ideas I've actually wasted more time then if I did them manually. The vibe code as you said is often crap and often after I spend a lot of time on it. Realize that there are countless subtle errors that mean its not actually doing what I was intending at all. I've learned nothing and made a pointless app that does not even do anything but looks like it does.
Thats the big allure that has been keeping "AI" hype floating. It always seems so dang close to being a magic wand. Then upon time spent reviewing and a critical eye you realize it has been tricking you like a janitor that is just sweeping dirt under the rug.
At this point I've relegated LLM to advanced find replace and Formatted data structuring(Take this list make it into JSON) and that's about it. There are basically tools that do everything else llms do that already exist and do it better.
I can't count at this point how many times "AI" has taken some sort of logic I want then makes a bunch of complex looking stuff that takes forever to review and I find out it fudged the logic to simply always be true/false when its not even a boolean problem.
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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.
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Lots of people like computers but earn a living doing something else
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.
If I were to code this for "real training" of a dermatologist, I'd make this closer to "real world" training rate. As a dermatologist, I'll imagine that probably just 1 out of 100 (or something like that) skin lesions that people could imagine are cancerous, actually are so.
With the current dataset, there're just too many cancerous images. This makes it kind of easy to just flag something as "cancerous" and still retain a good "score" - but the point is moot, if as a dermatologist you send _too many_ people without cancer to do further exams, then you're negating the usefulness of what you're doing.
And then once they have learned you get progressively harder and harder. Basically the closer to 50% you are the harder it will be to have a score higher than chance/50%.
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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!
Thanks for making this! A bit more polish and this is something I’d make sure everyone in my family has played with.
Imagine a world where every third person is able to recognise worrying skin lesions early on.
Asymmetry: One half of the spot is unlike the other half.
Border: The spot has an irregular, scalloped, or poorly defined border.
Color: The spot has varying colors from one area to the next
Diameter: melanomas are usually greater than 6 millimeters, or about the size of a pencil eraser
Evolving: Changing in size, shape, color, or new symptoms (itching, bleeding)
[0] https://www.aad.org/public/diseases/skin-cancer/find/at-risk...
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!
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 am a skin cancer doctor in Queensland and all I do is find and remove skin cancers (find between 10 and 30 every day). In my experience the vast majority of cancers I find are not obvious to other doctors (not even seen by them), let alone obvious to the patient. Most of what I find are BCCs, which are usually very subtle when they are small. Even when I point them out to the patient they still can't see them.
Also, almost all melanomas I find were not noticed by the patient and they're usually a little surprised about the one I point to.
In my experience the only skin cancers routinely noticed by patients are SCCs and Merkel cell carcinomas.
With respect, if "most skin cancers are very obvious even to a non-expert" I suggest the experts are missing them and letting them get larger than necessary.
I realise things will be different in other parts of the world and my location allows a lot more practice than most doctors would get.
Update: I like the quiz. Nice work! In case anyone is wondering, I only got 27/30. Distinguishing between naevus and melanoma without a dermatoscope on it is sometimes impossible. Get your skin checked.
They will go through your images until they get a good score, believe themselves and expert and proceed to diagnose themselves (and their friends).
By the time you have an image set that is representative and that will actually educate people to the point where they know what to do and what not to do you've created a whole raft of amateur dermatologists. And the result of that will be that a lot of people are going to knock on the doors of real dermatologists who might tell them not to worry about something when they are now primed to argue with them.
I've seen this pattern before with self diagnosis.
Pictures with purple circles (e.g. faded pen ink on light skin outlining the area of concern) are a strong indicator of cancer. :wink:
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.
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
That is very scary.
So the only way to be sure is to have everything sent to the lab. But I'm guessing cost/benefit of that from a risk perspective make it prohibitive? So if you're an unlucky person with a completely benign-presenting melanoma, you're just shit out of luck? Or will the appearance change before it spreads internally?
"idk but that's what it says" somehow this does not inspire confidence in the skin cancer learning app.
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