And a calculator outperforms LLMs at multiplication and division...

This isn't really surprising. LLMs are intended to be generally intelligent. They are spiky on the things frontier labs tend to benchmark against (software/math) but are not intended or expected to be great at everything. The general intelligence unlocks new workflows, it does not perfect existing ones.

That being said, I'm sure a frontier LLM could make some improvements to your CNN.

I'm sorry but as an MLE this is not a remotely useful article, and does not belong on the front page.

It's comparing a fine-tuned CNN to general purpose models on a tiny dataset.

fyi CNN: convolutional neural network

This doesn't seem that surprising to me? One is tasked with a simple very narrow task. The other has a corpus including billions of other images and pieces of information. It'd seem to me that instinctively one would outperform the other, regardless of age.

This is an example of why I'm a little stumped about why people are excited about Jev-style decision models. For any application at scale it'd be way more accurate to have a dedicated classifier using the right architecture (neural net, bayesian, etc.) LLMs drive the cost of making those models down to near zero.

Just use the LLM to make a thing that's actually good at what you need it to do.

Which CNN? A fine-tuned version of Inception v3, fine-tuned on 68,011 dermatology images

Which LLM? GPT-6 Astra and Claude Fable 5.1, not specified exactly which version

Obviously this is a bit like comparing an 100 iq radiologist, 20 years experience, with a just-tested-at-iq-130 6 year old and observing the radiologist beat the crap out of the kid. Was anybody really expecting a different outcome? Let's see after fine tuning.

Btw: the actual lesson from that network when it came out, which is really important is that Inception v6, the Google trained version (ie. can recognize cats, halloween masks and airplanes) beat, with a wide margin, the version that was trained from scratch on clinical images (and still didn't do so well, 62%. Not even really good enough for a first triage).

My dog outperforms the world's top researchers in an unassisted 100 meter sprint.

LLMs shouldn't be treated as if they were purpose-built for specific cases just because they generally output a plausible-looking answer, and this is not something that was discovered only recently

I'd like to see it compared to chinese models like DeepSeek, Kimi, GLM, etc just for the sake of research

I found SkinGPT-4 when I was looking into this article. From the nature paper "among the 150 cases, a significant percentage of SkinGPT-4’s diagnoses (80.63%) were evaluated as correct or relevant by board-certified dermatologists." Which does outperform the CNN in this article.

I remember being blown away by CNNs and GANs when they first gained popularity. I do think, with skin cancer diagnosis, a transformer architecture is going to be the better bet.

> I used Inception v3 pretrained on everyday photographs and then fine-tuned all layers on 68,011 dermoscopic and clinical images from the ISIC Archive on a single H100 GPU.

>These 200 test lesions, and every other photograph of the same lesion or patient, were removed from its data (including the validation set) before training, so it had never seen them, and they were not used to choose the model or set its threshold.

This is just benchmaxxing. A benchmaxxed model will always beat an unbenchmaxxed model, but it will be brittle.

You can always beat a specific dataset with a small model by finetuning on a train/test split. But it doesn't generalize well to 'wild' images, because they are out-of-domain.

This isn't surprising but it's good to have reporting to point to. The only way a general purpose LLM like these will match the performance of a bespoke classifier is if it has access to such a model.

Yeah obviously. Xgboost outperforms llms in algorithmic trading, even applying the token prediction method to markets xgboost is effectively better.

Llms have their applications. Other models still do.

It seems one is more specialized for the purpose and compare it against a general purpose LLM instead of a specalized/focused LLM?

This is like saying I compared this against photos for sure stored in a home swimming pool (CNN), compared to photos presumably stored in an ocean (generalized LLM).

I use LLMs where I can, but I'm no automatic fan of LLMs, a healthier comparison here seems to be what if a general purpose LLM was provided those CNN images, and perhaps what does a fine tuned local model with the CNN images perform like?

It still might not be better, but I would suspect it's closer, and a more realistic apples to apples comparison.

General assistant models compared to task specific models seem to be a well known spot where things don't always fit, but might be improving.

Here's a good heuristic for predicting the ability of a modern frontier LLM at any non-automatable task: Is it hugely overrepresented in their pre-training dataset (e.g. code), and were they specifically post-trained on it using reinforcement learning (e.g. coding)? If the answer to one or both is yes, you should expect them to be very good at it. Otherwise, you should expect them to be, at best, as good as an average human who's read a few books on the subject and is good at bullshitting.

It's cool that there's an in-browser skin classifier...but it says "It is not a state-of-the-art model". The obvious question is, is there a free in browser state-of-the-art model?