mnbvcxzz4869/medgemma-tb-it-lora-tb is a LoRA fine-tuned vision adapter built on top of MedGemma-4B-IT, designed to improve the model’s ability to analyze chest X-ray images for Tuberculosis (TB)-related radiographic patterns.

The fine-tuning process is applied exclusively to the vision component of the multimodal model. The language model and its general medical knowledge remain unchanged. Multimodal reasoning (text + image) is achieved at inference time through prompt engineering, not through multimodal retraining.

This model is intended for research and clinical decision support system (CDSS) prototyping, particularly for TB screening workflows.
This model is derived from MedGemma and is subject to the Health AI Developer Foundations terms of use governing the base model.

Developed by: mnbvcxzz4869
Model type: Vision-Language Model (LoRA adapter for vision encoder)
Base model: google/medgemma-4b-it
License: Apache 2.0 (adapter weights); base model terms apply
Fine-tuning method: LoRA (vision-only)

Uses

Direct Use

Analysis of chest X-ray images with a focus on identifying visual patterns associated with pulmonary tuberculosis
Research and prototyping of AI-assisted TB screening tools
Integration into chatbot-based CDSS systems using prompt-based multimodal reasoning

Downstream Use

Clinical decision support research
Educational tools for radiology and infectious disease
Integration into web-based medical AI applications (e.g., Streamlit-based systems)

Out-of-Scope Use

Automated or standalone medical diagnosis
Treatment recommendation or prescription
Use for diseases outside tuberculosis without further validation
Clinical deployment or patient management without regulatory approval

Bias, Risks, and Limitations

The model is trained on public chest X-ray datasets, which may not represent all populations, imaging devices, or clinical environments.
Radiographic findings alone are not sufficient for definitive TB diagnosis and must be interpreted alongside clinical and laboratory data.
The model may generate inaccurate or incomplete interpretations, especially for low-quality or out-of-distribution images.
As this model builds upon MedGemma, it inherits the base model’s limitations, including sensitivity to prompt formulation and lack of clinical validation. The model has not been evaluated for multi-image or multi-turn clinical reasoning scenarios.

Evaluation Results

Metric	Value
Eval Loss	2.2045
Eval Model Preparation Time	0.0197
Eval Runtime	94.6703
Eval Samples Per Second	8.8730
Eval Steps Per Second	1.1090

How to Get Started with the Model

python

import torch
from transformers import AutoProcessor, AutoModelForImageTextToText
from peft import PeftModel

base_model_id = "google/medgemma-4b-it"
adapter_id = "mnbvcxzz4869/medgemma-tb-it-lora-tb"

processor = AutoProcessor.from_pretrained(base_model_id)

model = AutoModelForImageTextToText.from_pretrained(
    base_model_id,
    torch_dtype=torch.bfloat16,
    device_map="auto"
)

model = PeftModel.from_pretrained(model, adapter_id)