This dataset is made through a python system called the Medical Reasoning Generator (MedRGen). This system produces AI-driven synthetic medical cases. By providing the system certain parameters, I am able to generate cases of a specific type and theme. This system uses an AI “roleplaying” technique, which causes the LLMs used in the system to generate realistic text which is saved in a formatted manner. This system is similar to my maternal health and financial use case generators, however the twist with this generator is that the reasoning behind the cases is also captured within the dataset. Additionally, the reasoning logic is validated by using a reasoning LLM.
MedRGen is capable of creating datasets with the following medical focuses:
Family medicine
Emergency Medicine
Internal Medicine
Cardiovascular
The system is able to focus on the following themes:
Gastrointestinal
Cardiovascular
Musculoskeletal
Respiratory
Neurological
This purchase contains a `*.zip` file that contains 50 different medical cases in `*.json` format with AI-driven reasoning. The dataset provides the conversations between the doctor and the patient that lead to a diagnosis by the simulated professional. The dataset contains patient demographics and meta-data.
This dataset is created using an automated synthetic data producing system (described above). This system uses commercial LLM models (example: 4.1-mini) to generate realistic conversations and medical reasoning logic given a systematically generated medical case with a certain focus and theme. As mentioned above, the medical reasoning is reviewed by a reasoning model (example: o3-mini). This system produces both LLM reviewed and unreviewed samples for further usage.
This dataset is fit for being used in the following use cases:
Clinical Reasoning/Diagnostic Decision Making
Doctor Patient Conversation Modeling
Structured Medical Reasoning Agents
Clinical QA/Case-Based Retrieval
Niche Cases:
Medical Reasoning chain fine-tuning
Triage decision simulation
Evaluation of benchmark for clinical LLMs
This dataset is synthetic, and mostly AI-driven. I am not a medical professional, so I am unable to give a skilled review of the dataset before sale. I am not responsible for any further use of the dataset (including any evil AI models) after purchase.
If you decide to use this dataset, please use this information for citation.
@dataset{gbl-MedRGen-neurological-IM-50-07312025,
title={Grandma's Boy Labs: MedRGen - Neurological Internal Medicine 07/31/2025 - 50pack},
author={Emmitt J Tucker},
year={2025},
publisher={Grandma's Boy Labs},
url={https://grandmasboylabs.com}
}Add the fine-tuning guide to your cart to learn how to use these datasets!
This dataset is made through a python system called the Medical Reasoning Generator (MedRGen). This system produces AI-driven synthetic medical cases. By providing the system certain parameters, I am able to generate cases of a specific type and theme. This system uses an AI “roleplaying” technique, which causes the LLMs used in the system to generate realistic text which is saved in a formatted manner. This system is similar to my maternal health and financial use case generators, however the twist with this generator is that the reasoning behind the cases is also captured within the dataset. Additionally, the reasoning logic is validated by using a reasoning LLM.
MedRGen is capable of creating datasets with the following medical focuses:
Family medicine
Emergency Medicine
Internal Medicine
Cardiovascular
The system is able to focus on the following themes:
Gastrointestinal
Cardiovascular
Musculoskeletal
Respiratory
Neurological
This purchase contains a `*.zip` file that contains 50 different medical cases in `*.json` format with AI-driven reasoning. The dataset provides the conversations between the doctor and the patient that lead to a diagnosis by the simulated professional. The dataset contains patient demographics and meta-data.
This dataset is created using an automated synthetic data producing system (described above). This system uses commercial LLM models (example: 4.1-mini) to generate realistic conversations and medical reasoning logic given a systematically generated medical case with a certain focus and theme. As mentioned above, the medical reasoning is reviewed by a reasoning model (example: o3-mini). This system produces both LLM reviewed and unreviewed samples for further usage.
This dataset is fit for being used in the following use cases:
Clinical Reasoning/Diagnostic Decision Making
Doctor Patient Conversation Modeling
Structured Medical Reasoning Agents
Clinical QA/Case-Based Retrieval
Niche Cases:
Medical Reasoning chain fine-tuning
Triage decision simulation
Evaluation of benchmark for clinical LLMs
This dataset is synthetic, and mostly AI-driven. I am not a medical professional, so I am unable to give a skilled review of the dataset before sale. I am not responsible for any further use of the dataset (including any evil AI models) after purchase.
If you decide to use this dataset, please use this information for citation.
@dataset{gbl-MedRGen-neurological-IM-50-07312025,
title={Grandma's Boy Labs: MedRGen - Neurological Internal Medicine 07/31/2025 - 50pack},
author={Emmitt J Tucker},
year={2025},
publisher={Grandma's Boy Labs},
url={https://grandmasboylabs.com}
}Add the fine-tuning guide to your cart to learn how to use these datasets!
