Update README.md

README.md

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 ## Model Details
 
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+## Introduction
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+Healthcare communication and health literacy is a large gap that exists in the healthcare industry between physicians and patients. I often run into the issue of reading long doctor’s notes that are uploaded to my patient portal which I cannot understand. This is definitely a frustrating experience as a patient because the notes are not only long, but also include a lot of technical jargon surrounding the diagnosis which is overwhelming. I often look to doctors in my family to translate doctor’s notes and understand if / what the next steps are. Instead of having a middle man translate the notes, I’m hoping that the LLM can take doctor’s notes as input and summarize them into short, simple notes. I think current LLMs do need training in this as it is a niche topic and I would want to ensure that accuracy and key details are preserved when providing simple summaries to users/patients. Current LLMs may brush over key details if they haven’t been trained specifically in clinical/doctor’s notes or a large enough dataset to understand the context and style of writing. In my own experience, LLMs are great at summarizing, but can lack specificity or leave out information at times. As mentioned in Medium post by Sahil Ahmed (Data Scientist), LLMs, in general, as well as ones that implement RAG systems are not without their disadvantages. Ahmed notes one such failure point as “context limitation” which happens when many documents are passed through the LLM model which forces the system to “consolidate them to fit the LLM’s input limits, which may lead to truncation or selective prioritization, potentially leaving out crucial information” (Sahin Ahmed, 2024). In this medical use case, it is extremely important to maintain the accuracy for the patient such that key details are not brushed over so the model’s summarized output can be relied on for next steps. To ensure this accuracy, I think developing a LLM that is dedicated to this use case and has been trained specifically on doctor’s notes and summaries is key. This way, any noise from other unrelated training data can be avoided. I think current LLMs do need training in this as it is a niche topic and I would want to ensure that accuracy and key details are preserved when providing simple summaries to users/patients. Current LLMs may brush over key details if they haven’t been trained specifically in clinical/doctor’s notes or a large enough dataset to understand the context and style of writing. In my own experience, LLMs are great at summarizing, but can lack specificity or leave out information at times. As mentioned in Medium post by Sahil Ahmed (Data Scientist), LLMs, in general, as well as ones that implement RAG systems are not without their disadvantages. Ahmed notes one such failure point as “context limitation” which happens when many documents are passed through the LLM model which forces the system to “consolidate them to fit the LLM’s input limits, which may lead to truncation or selective prioritization, potentially leaving out crucial information” (Sahin Ahmed, 2024). In this medical use case, it is extremely important to maintain the accuracy for the patient such that key details are not brushed over so the model’s summarized output can be relied on for next steps. To ensure this accuracy, I think developing a LLM that is dedicated to this use case and has been trained specifically on doctor’s notes and summaries is key. This way, any noise from other unrelated training data can be avoided. 
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 ### Model Description
 
 <!-- Provide a longer summary of what this model is. -->