license:openrail region:us
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license: openrail
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### 🩺 Description:
The knowrohit07/know_medical_dialogues dataset is a collection of conversational exchanges between patients and doctors on various medical topics. It aims to capture the intricacies, uncertainties, and questions posed by individuals regarding their health and the medical guidance provided in response.

### 🎯 Intended Use:
This dataset is crafted for training Large Language Models (LLMs) with a focus on understanding and generating medically-informed dialogue. It's ideal for LLM applications aiming to provide medical information or insights, especially for scenarios with limited access to healthcare resources.

❗ Limitations:
While this dataset includes diverse interactions, it doesn't cover every medical scenario. Models trained on this data should be viewed as an additional resource, not a substitute for professional medical consultation.

📌 Data Source:
Conversational seed tasks or exchanges were collected from anonymized patient-doctor interactions and synthetically made using GPT4.

📋 Collection Methodology:
The data was meticulously curated to ensure no personally identifiable information remained. All conversations are representative of general concerns and advice, without specific case details.

### Data Fields:
description: A brief summary of the conversation's context.
utterances: Contains a sequential list of statements between the patient and doctor.

### 🌟 Advantages of the Dataset:
Broad Spectrum: The dataset encompasses a wide array of medical queries and advice, making it valuable for general medical conversational AI.

Diverse Interactions: It captures everything from symptom queries to post-care instructions.

Training Potential for LLMs: Specifically tailored for fine-tuning LLMs for medical conversations, enhancing the resultant model's capability in this domain.

⚖️ Ethical and Impact Considerations:
Positive Impact: Utilizing LLMs trained on this dataset can be invaluable for healthcare professionals, especially in regions with limited medical datasets. When deployed on affordable local devices, doctors can leverage an AI-assisted tool, enhancing their consultation and decision-making processes.

Potential Risks: There's an inherent risk of the model providing guidance that may not match the latest medical guidelines or specific patient requirements. It's crucial to clarify to users that outputs from the LLM should complement professional medical opinions.

Recommendation: Encourage healthcare professionals to use this tool as an initial point of reference and not as the primary foundation for medical decisions.
