AI in Healthcare Series: introduction terms and definitions.
Artificial Intelligence (AI) is a broad field of computer science focused on creating machines that can perform tasks that typically require human intelligence. This includes learning, problem-solving, decision-making, and understanding language.
Natural Language Processing (NLP) is a specialized branch of AI that enables computers to interact with human language. Think of it as teaching computers to "read" and "understand" text or "listen" and "comprehend" speech.
Here's how it generally works:
1. Data Collection:
NLP starts by gathering large amounts of text or speech data (e.g., patient notes, medical literature, spoken conversations).
2. Preprocessing:
This raw data is often "cleaned" and prepared for analysis. This involves:
* Tokenization: Breaking text into smaller units like words or phrases.
* Lemmatization/Stemming: Reducing words to their root form (e.g., "running," "ran," "runs" all become "run").
* Stop Word Removal: Eliminating common words that don't add much meaning (e.g., "the," "is," "a").
3. Feature Extraction: The preprocessed text is then converted into numerical representations that computers can understand. This involves identifying various linguistic features.
4. Model Training (Machine Learning/Deep Learning):
* Machine Learning (ML): Algorithms are trained on vast datasets to identify patterns and relationships within the language. For example, they can learn to distinguish between a patient's name and a symptom.
* Deep Learning: A subset of ML that uses neural networks (inspired by the human brain) to learn complex patterns. Large Language Models (LLMs) like those powering chatbots are a type of deep learning model that can generate human-like text.
5. Understanding & Generation:
* Natural Language Understanding (NLU): Focuses on interpreting the meaning, context, and intent behind human language, even with grammatical errors or slang.
* Natural Language Generation (NLG): Focuses on turning structured data into human-like text, enabling machines to respond in a natural way.
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#AI #LLM #prior #authorization
Prior authorization seems to piss off some patients and the physicians. Do we have to have it? Can we do without it? Would it make healthcare to expensive or would it just mean less profits for insurance companies?
I just realized this is a whole new field to encourage my children to study.
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