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422 points sungam | 1 comments | | HN request time: 0.206s | source

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).

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jacquesm ◴[] No.45160857[source]
Nice job. Now you really need to study up on the statistics behind this and you'll quickly come to the conclusion that this was the easy part. What to do with the output is the hard part. I've seen a start-up that made their bread and butter on such classifications, they did an absolutely great job of it but found the the problem of deciding what to do with such an application without ending up with net negative patient outcomes to be far, far harder than the classification problem itself. The error rates, no matter how low, are going to be your main challenge, both false positives and false negatives can be extremely expensive, both in terms of finance and in terms of emotion.
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sungam ◴[] No.45161691[source]
Thanks for your comment - the purpose of this app is patient education rather than diagnosis but I will definitely have a look at the relevant stats in more detail!
replies(2): >>45161870 #>>45163114 #
1. thebeardisred ◴[] No.45163114[source]
To that end I quickly learned something that AI models would as well (which isn't your intention):

Pictures with purple circles (e.g. faded pen ink on light skin outlining the area of concern) are a strong indicator of cancer. :wink: