license:mit region:us
---
license: mit
---
---
library_name: peft
base_model: microsoft/phi-2
---
## Model Details

### Model Description
We have developed a finetuned AI model designed to revolutionize how medical reports are generated. This model, built upon the robust foundation of Microsoft's Phi Large Language Model (LLM), leverages the specialized MedMCQA dataset, sourced from the OpenLifeSciences AI initiative on Hugging Face. The integration of these powerful tools enables our model to interpret and analyze complex medical data with unparalleled precision and depth.

### Dataset Overview
MedMCQA Dataset: At the heart of our model's training lies the MedMCQA dataset, a comprehensive collection of over 194,000 multiple-choice questions (MCQs) derived from AIIMS & NEET PG medical entrance exams. This dataset spans an impressive array of 21 medical subjects, covering 2,400 healthcare topics, making it an invaluable resource for developing an AI adept at understanding and generating medical content. Each question in the MedMCQA dataset is meticulously designed to test a model's reasoning across various medical fields, providing not only the questions and correct answers but also detailed explanations and alternative options, enriching the model's learning process.


### Use Cases:
Application in Medical Report Generation
HL7 Medical Reports: Our AI model is fine-tuned to assist healthcare professionals by automating the creation of HL7 medical reports. HL7 (Health Level Seven) is a set of international standards for the transfer of clinical and administrative data between software applications used by various healthcare providers. By harnessing the MedMCQA dataset, our model is trained to understand the nuances and complexities of medical data, enabling it to generate detailed, accurate, and comprehensible medical reports.
Diagnostic Support: Our AI model can analyze patient interactions and relevant health data to provide preliminary diagnostic insights, aiding doctors in their decision-making process.
Medical Education: The model can serve as an advanced tool for medical students and professionals, offering detailed explanations and reasoning, akin to those found in the MedMCQA dataset, to enhance learning and understanding.
Research and Analysis: By generating comprehensive reports, the model can assist medical researchers in compiling and analyzing data, facilitating advancements in medical research.