1. General information

1.0.1 Version of the Summary: 1.0

1.0.2 Last update: 24-Nov-2025

1.1 Model Developer Identification

1.1.1 Model Developer name and contact details: Microsoft Corporation at One Microsoft Way, Redmond, WA 98052. Tel: 425-882-8080.

1.2 Model Identification

1.2.1 Versioned model name(s): LLaVA-Rad (7B)

1.2.2 Model release date: 20-Mar-2025

##1.3 Overall training data size and characteristics

1.3.1 Size of dataset and characteristics

1.3.1.A Text training data size: Less than 1 billion tokens

1.3.1.B Text training data content: 400K image-text pairs

1.3.1.C Image training data size: Less than 1 billion tokens

**1.3.1.D Image training data content: GPT-4 and rule-based pre-processed MIMIC-CXR reports are publicly available: LLaVA-Rad MIMIC-CXR Annotations (PhysioNet)

Open-I dataset is publicly accessible at doi.org/10.93/jamia/ocv080

CheXpert CXR images and reports are publicly accessible at doi.org/10.71718/6nvz-pm34

US-CXR dataset is a private collection of images and reports and cannot be made publicly available due to privacy restrictions. Interested parties should contact Segmed, Inc (https://segmed.ai) to inquire about access to the dataset, subject to Segmed’s applicable ethical and legal requirements.

1.3.1.E Audio training data size: Not applicable

1.3.1.F Audio training data content: Not applicable

1.3.1.G Video training data size: Not applicable

1.3.1.H Video training data content: Not applicable

1.3.1.I Other training data size: Not applicable

1.3.1.J Other training data content: Not applicable

1.3.2 Latest date of data acquisition/collection for model training: 01-Mar-2025

1.3.3 Is data collection ongoing to update the model with new data collection after deployment? No

1.3.4 Date the training dataset was first used to train the model: 05-Jan-2024

1.3.5 Rationale or purpose of data selection: Datasets of chest X-rays with associated reports or labels were selected to train a specialized radiology multimodal model for generating accurate CXR findings. The collection spans 697K image-text pairs from diverse sources and geographies to improve robustness and factual correctness, with GPT-4 used to translate, structure, and synthesize reports where needed to enhance data quality

2. List of data sources

2.1 Publicly available datasets

2.1.1 Have you used publicly available datasets to train the model? Yes

2.2 Private non-publicly available datasets obtained from third parties

2.2.1 Datasets commercially licensed by rights holders or their representatives

2.2.1.A Have you concluded transactional commercial licensing agreement(s) with rights holder(s) or with their representatives?

This information cannot be provided due to confidentiality restrictions

2.2.2 Private datasets obtained from other third-parties

2.2.2.A Have you obtained private datasets from third parties that are not licensed as described in Section 2.2.1, such as data obtained from providers of private databases, or data intermediaries? This information cannot be provided due to unavailability of the underlying data (e.g., loss, corruption, or other access limitations)

2.3 Personal Information

2.3.1 Was personal data used to train the model? Microsoft follows all relevant laws and regulations pertaining to personal information

2.4 Synthetic data

2.4.1 Was any synthetic AI-generated data used to train the model? Yes

3. Data processing aspects

3.1 Respect of reservation of rights from text and data mining exception or limitation

3.1.1 Does this dataset include any data protected by copyright, trademark, or patent? Microsoft follows all required regulations and laws for processing data protected by copyright, trademark, or patent

3.2 Other information

3.2.1 Does the dataset include information about consumer groups without revealing individual consumer identities? Microsoft follows all required regulations and laws for protecting consumer identities

3.2.2 Was the dataset cleaned or modified before model training? Yes