u/manuspresso

Recently I've come across this query on many platform.

Here is what I think:

First of all, healthcare data is a completely different beast. Building an AI solution for medical data quality isn't just about fixing duplicate records or filling in missing values. To build an AI-powered model to turn messy data into clean and accurate training data, you need a large volume of representative and relevant medical data.

There are challenges involved in collecting medical data for research, analytics, and AI models. Here are some of the biggest ones:

• You need access to large, diverse, and representative patient datasets from different hospitals, regions, and healthcare systems to build a reliable model.
• Clinical notes tend to be messy -- doctors' handwriting, abbreviations, and local terminology can make identification and standardization extremely difficult.
• Medical coding standards also evolve regularly, so your system has to keep up with those changes.
• And because healthcare is heavily regulated, handling sensitive patient information means de-identification, privacy, and compliance aren't optional but crucial.
• Staggering ambiguities in clinical data still require domain experts to validate and resolve.

These are areas where healthcare data annotation companies, who work with AI companies, have already invested heavily.

Give it a thought when you are looking to build a model.

What do you guys have to say?

While I’m not 100% sure whether they are a scam, they are far from accurate, let alone infallible. Here are my two cents:

Generative AI models are trained on a large volume of data from the internet – much like students learn from many books before answering questions in an exam.

LLMs use a combination of statistical, probabilistic, and neural network-based techniques to generate realistic content.

• Neural networks, particularly transformer models, learn relationships between words and sentences from their training data.
• During training, models learn which words usually follow others based on their frequency and context. They don’t understand meaning in the same way humans do.
• Accordingly models uses statistical patterns to predict and generate next word or token that sounds natural.

For example, if your input prompt is:
“A cat usually likes to chase.....”
The model output is likely to be:

• mice (70% probability)
• birds (15%)
• balls (8%)
• toys (5%)
• others (2%)

It picks one and continues predicting the next word, assembling grammatically and contextually correct sentences, paragraphs, and blogs.

How do AI detectors work?

• Since, LLMs create concise and grammatically correct content, any such content makes AI detectors suspicious .
• Detectors calculate the probability of the next word in a sequence (predictable language patterns). High predictability is flagged as AI-generated.
• Word selection and style: AI models learn from from books, encyclopedias, high-quality news, and academic journals written by or under supervision of domain experts, often in polished language. Impeccable grammar, structured sentences, logical structure, seamless flow, and industry terminology – this combination gets flagged as AI-generated.

But this raises an important question

Can’t experienced and skilled humans write such content, given that AI models themselves are trained execlusively on human-created text? So are AI detector tools a scam?

Feedback and corrections are most welcome.