In a recent experiment, they set out to determine how reliable LMMs are in medical diagnosis — asking both general and more specific diagnostic questions — as well as whether models were even being evaluated correctly for medical purposes.
Curating a new dataset and asking state-of-the-art models questions about X-rays, MRIs and CT scans of human abdomens, brain, spine and chests, they discovered “alarming” drops in performance.
They used one to create the dataset for their experiments:
In their experiments, they introduced a new dataset, Probing Evaluation for Medical Diagnosis (ProbMed), for which they curated 6,303 images from two widely-used biomedical datasets. These featured X-ray, MRI and CT scans of multiple organs and areas including the abdomen, brain, chest and spine.
GPT-4 was then used to pull out metadata about existing abnormalities, the names of those conditions and their corresponding locations. This resulted in 57,132 question-answer pairs covering areas such as organ identification, abnormalities, clinical findings and reasoning around position.
The seven models tested included GPT-4V, Gemini Pro and the open-source, 7B parameter versions of LLaVAv1, LLaVA-v1.6, MiniGPT-v2, as well as specialized models LLaVA-Med and CheXagent. These were chosen because their computational costs, efficiencies and inference speeds make them practical in medical settings, researchers explain.
It seems like this is a case of “they just aren’t using AI right, if they used it right it works” when it sure looks like they are using the models intended for these specific medical tasks.
Those are not the sort of model anybody in the field would use (medical CV with deep learning based analysis is a vibrant field with many breakthroughs in recent years). These are the sort of models tech bros are trying to sell to the public as general AI. There is a world of difference.
They did not use a LLM.
What a joke, a few generic LLMs making a judgement call about all AI models.
They used one to create the dataset for their experiments:
It seems like this is a case of “they just aren’t using AI right, if they used it right it works” when it sure looks like they are using the models intended for these specific medical tasks.
Those are not the sort of model anybody in the field would use (medical CV with deep learning based analysis is a vibrant field with many breakthroughs in recent years). These are the sort of models tech bros are trying to sell to the public as general AI. There is a world of difference.