transformers pytorch safetensors llama text-generation biology medical en license:mit autotrain_compatible text-generation-inference endpoints_compatible region:us
---
language:
- en
library_name: transformers
license: mit
tags:
- biology
- medical
---
# Model Card for Model ID

<!-- Provide a quick summary of what the model is/does. -->

This model aims to be a base template for new models. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/modelcard_template.md?plain=1).

## Model Details
Model Name: Medical Diagnosis Model - Fine-tuned LLaMA 2
Model Version: v1.0
Description: This model is fine-tuned from the LLaMA 2 architecture for medical diagnosis purposes. It leverages large-scale medical datasets to enhance its understanding and accuracy in diagnosing various diseases from text inputs.
Author: Jai Akash
Contact: jaiakash2393@gmail.com

### Model Description

<!-- Provide a longer summary of what this model is. -->
This model is intended for use in medical diagnosis and analysis. It can be used to assist healthcare professionals in diagnosing diseases based on text inputs and potentially image inputs in the future.
It is designed to provide insights and suggestions but should not be solely relied upon for critical medical decisions without professional oversight.
Training Data:

The model is fine-tuned using a few datasets.
The training data includes text from various medical domains to ensure comprehensive knowledge coverage.
Training Process:

The fine-tuning process involved supervised training on annotated medical data.
Techniques such as learning rate scheduling, early stopping, and data augmentation were employed to improve model performance and generalization.
Evaluation:

The model was evaluated using a separate validation set of medical records and research papers.
Performance metrics include accuracy, precision, recall, and F1 score, with a particular focus on diagnostic accuracy.

Limitations:
While the model is trained on extensive medical data, it is not infallible and may produce incorrect or incomplete diagnoses.
It should be used as a supplementary tool in conjunction with professional medical advice.

Future Work:
Future iterations of the model will include integration with image recognition features to analyze medical images and further enhance diagnostic capabilities.
Continuous updates with new medical research and publications will be incorporated to keep the model up-to-date.we will give more data including various books and esaerch papers for training that is basically an advanced version.

- **Developed by:** Jai Akash

- **Model type:** Fine-tuned Large Language Model (LLM) based on LLaMA 2
- **Language(s) (NLP):** English
- **License:** MIT
- **Finetuned from model [optional]:** LLAMA 2



<!-- Provide the basic links for the model. -->

- **Repository:** https://huggingface.co/peteparker456/medical_diagnosis_llama2


## Uses

<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
The Medical Diagnosis LLaMA-2 Model is designed for use in medical and healthcare applications, specifically for diagnosing various diseases and conditions based on text inputs. The model can analyze patient symptoms, medical histories, and other relevant data to provide diagnostic suggestions and recommendations.

Intended Users
   Medical Professionals: Doctors, nurses, and other healthcare providers can use the model to assist in diagnosing patients, cross-referencing with known conditions, and suggesting potential treatments.
   Medical Researchers: Researchers can utilize the model to analyze medical data, identify patterns, and generate insights for further studies.
   Medical Students: Students in the medical field can use the model as a learning tool to better understand diagnostic processes and improve their clinical decision-making skills.
   Healthcare Organizations: Hospitals, clinics, and other healthcare institutions can integrate the model into their systems to enhance diagnostic accuracy and efficiency.

Affected Parties
   Patients: Improved diagnostic accuracy and speed can lead to better patient outcomes and experiences.
   Healthcare Providers: The model can reduce the workload on medical professionals and assist in making more informed decisions.
   Medical Industry: The model can contribute to advancements in medical AI and support the development of new diagnostic tools and technologies.
Potential Applications
   Clinical Decision Support: Assisting healthcare providers with diagnostic decisions based on patient data.
   Telemedicine: Enhancing remote diagnosis and consultations by providing AI-driven diagnostic support.
   Medical Education: Serving as an educational tool for medical students and trainees.

Remember it is just a prototype!
Always consult Doctor!

### Direct Use

<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->

The Medical Diagnosis LLaMA-2 Model can be used directly for various tasks without the need for additional fine-tuning or integration into larger systems. Here are some examples of its direct use:

Medical Query Analysis: The model can analyze and respond to medical queries, providing diagnostic suggestions and relevant medical information based on the input text.
Symptom Checker: Users can input symptoms, and the model can suggest possible conditions or diseases that match the symptoms, providing a preliminary diagnosis.
Patient Data Analysis: Directly analyze patient data inputs, including symptoms, medical history, and test results, to generate diagnostic suggestions.
Educational Tool: Used by medical students and professionals for educational purposes, providing explanations and diagnostic reasoning for various medical conditions.

These direct uses allow healthcare providers, researchers, and students to benefit from the model's capabilities without additional modifications or complex integrations.

### Downstream Use [optional]

<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->

[More Information Needed]

### Out-of-Scope Use

<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->

Self-Diagnosis: The model should not be used by individuals to self-diagnose medical conditions without consulting a qualified healthcare provider. Misinterpretation of the model's suggestions could lead to harmful outcomes.
Emergency Medical Situations: The model is not suitable for use in emergency medical situations where immediate professional medical attention is required.
Legal or Medical Advice: The model should not be used as a substitute for professional legal or medical advice. Users should always consult professionals in these fields for advice and decisions.
Personal Data Analysis: Analyzing personal health data without proper consent and adherence to data privacy regulations is outside the scope of this model. The model should be used responsibly with consideration for patient privacy and data protection laws.
Non-Medical Queries: The model is specifically fine-tuned for medical diagnosis and should not be expected to perform well on non-medical queries or general-purpose language tasks.
Malicious Use: Any use of the model to generate harmful, misleading, or malicious content is strictly prohibited. This includes generating false medical information, promoting fraudulent medical practices, or any other use that can harm individuals or public health.

By outlining these out-of-scope uses, we aim to prevent misuse and ensure that the model is used responsibly and ethically in appropriate contexts.

## Bias, Risks, and Limitations

<!-- This section is meant to convey both technical and sociotechnical limitations. -->

Bias
    Training Data Bias: The model is trained on a diverse set of medical texts, but the underlying training data may contain biases. This can result in the model generating biased or skewed information based on race, gender, age, or socioeconomic status.
    Representation Bias: Certain medical conditions, demographics, or regions might be underrepresented in the training data, leading to less accurate or comprehensive outputs for those areas.
Risks
    Misdiagnosis: The model's suggestions are based on patterns learned from the training data and are not a substitute for professional medical advice. There's a risk of misdiagnosis if the model's outputs are taken at face value without professional interpretation.
    Over-Reliance: Users might over-rely on the model's outputs, potentially leading to neglect of professional medical consultation and advice.
    Data Privacy: When using the model, especially in applications dealing with personal health information, there is a risk of data breaches and privacy violations if proper security measures are not implemented.
Limitations
    Accuracy: While the model is fine-tuned for medical diagnosis, it is not perfect and may produce inaccurate or incomplete results. It should be used as a supplementary tool rather than a definitive source.
    Context Understanding: The model may lack the ability to fully understand the context or nuances of complex medical cases, which can lead to incorrect or irrelevant responses.
    Update Frequency: Medical knowledge evolves rapidly, and the model's training data may become outdated. Regular updates and re-training with the latest medical information are necessary to maintain accuracy.
    Language Support: The model primarily supports English. Non-English queries may not yield accurate results, limiting its utility in multilingual contexts.
    Ethical and Responsible Use: Users must ensure ethical use of the model, particularly in contexts that involve patient care and medical decision-making. The model should not be used to justify decisions that could harm individuals or violate ethical standards.

### Recommendations

<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->

Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
Professional Consultation: Always consult a licensed medical professional before making any health-related decisions based on the model's outputs. The model is intended to assist, not replace, professional judgment.


Bias Mitigation: Conduct regular audits to identify and address any biases in the model's training data. Implement strategies to reduce these biases and ensure diverse and representative training datasets.
Contextual Awareness: Encourage users to provide as much context as possible when using the model. Detailed input can help the model generate more accurate and relevant outputs.
User Training: Educate users on the proper use of the model, including its limitations and the importance of not relying solely on its outputs for critical medical decisions.
Ethical Use: Develop and enforce guidelines for the ethical use of the model. Ensure that it is used in ways that prioritize patient safety, privacy, and well-being.
Security Measures: Implement robust data security measures to protect patient information and prevent data breaches. Ensure compliance with relevant regulations such as HIPAA for handling medical data.
Transparency: Maintain transparency about the model's development, training data, and known limitations. Provide clear documentation and disclaimers to help users understand the scope and constraints of the model.
## How to Get Started with the Model

Use the code below to get started with the model.

```python
from transformers import pipeline, AutoTokenizer, AutoModelForCausalLM

model_name = "peteparker456/medical_diagnosis_llama2" 
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

pipe = pipeline(task="text-generation", model=model, tokenizer=tokenizer, max_length=400) 

medical_keywords = ["symptom", "diagnosis", "treatment", "medicine", "disease", "condition", "health", "therapy","suffer"]

def is_medical_query(query):
    """Check if the query contains medical-related keywords."""
    return any(keyword in query.lower() for keyword in medical_keywords)

print("Welcome to the medical information assistant. Please ask your medical questions or type 'exit' to end the conversation.")

while True:
    user_input = input("You: ")

    if user_input.lower() == 'exit':
        print("Goodbye!")
        break

    if is_medical_query(user_input):
        # Generate response based on user input
        prompt = f"<s>[INST] {user_input} [/INST]"
        result = pipe(prompt)
        generated_text = result[0]['generated_text']
    else:
        generated_text = "Sorry, it is out of my knowledge. Please ask anything about the medical field."

    print("Bot:", generated_text)




## Model Card Contact

jaiakash2393@gmail.com