A LoRA adapter fine-tuned on google/medgemma-4b-it for generating patient-friendly health reports that communicate diabetic retinopathy screening results using probabilistic framing and health-literate language.

Model Description

This adapter was trained as Model 2 in a dual-adapter pipeline for the MedGemma Impact Challenge:

Model 1 (DR Detection): Community LoRA → retinal image → P(DR) grade probabilities
Model 2 (This adapter): P(DR) + clinical labs + CGM data → patient-friendly report

What It Does

Given structured clinical context (DR probability, labs, CGM metrics, demographics), this adapter generates a ~5,000-character patient report that:

Communicates risk using natural frequencies ("about 8 out of 10 people...")
Clearly distinguishes screening vs diagnosis
Provides urgency-appropriate recommendations (routine / moderate / urgent)
Connects eye health to glucose control using the patient's own data
Uses 8th-grade reading level language

Training Details

Parameter	Value
Base model	`google/medgemma-4b-it`
PEFT method	LoRA
Rank	16
Alpha	32
Dropout	0.1
Target modules	q_proj, k_proj, v_proj, o_proj, gate_proj, up_proj, down_proj
Trainable params	0.76% of base model
Training data	43 participants from AI-READI dataset
Training targets	GPT-5.2 generated reports with probabilistic framing
Hardware	Apple Silicon M4 (MPS backend), float32
Epochs	~55 steps with gradient accumulation=4

Evaluation

Evaluated on 7 held-out test participants using GPT-5.2 as automated judge (1-5 scale):

Criterion	Fine-tuned (this)	Base MedGemma	Improvement
Probability communication	4.3	3.3	+1.0
Actionability	3.7	2.6	+1.1
Clinical accuracy	3.0	3.0	0
Readability	4.1	4.1	0
Completeness	5.0	4.9	+0.1
Overall quality	3.4	3.1	+0.3
Overall mean	3.9/5	3.5/5	+0.4

Usage

python

from transformers import AutoModelForCausalLM, AutoProcessor
from peft import PeftModel

base_model = AutoModelForCausalLM.from_pretrained("google/medgemma-4b-it")
model = PeftModel.from_pretrained(base_model, "drdavidl/medgemma-stage2-report")
processor = AutoProcessor.from_pretrained("google/medgemma-4b-it")

prompt = """You are a health communication specialist...
SCREENING RESULTS:
- Probability of diabetic retinopathy: 42.0% (about 4 out of 10)
...
"""

messages = [{"role": "user", "content": prompt}]
inputs = processor.apply_chat_template(messages, add_generation_prompt=True,
                                        tokenize=True, return_tensors="pt")
outputs = model.generate(**inputs, max_new_tokens=2048, temperature=0.3)
report = processor.decode(outputs[0], skip_special_tokens=True)

Privacy & Compliance

Trained under AI-READI Data Use Agreement (DUA Section 3.D)
LoRA adapters contain <1% of base model parameters — minimizes memorization risk
All participant IDs anonymized (P1000–P1054)
Numeric values jittered by 2% during training
No patient data is stored in adapter weights

Part Of

Multimodal Health Risk Communicator — GitHub Repository

MedGemma Impact Challenge submission by David Liebovitz, MD

License

This adapter follows the Health AI Developer Foundations Terms of Service, consistent with the base MedGemma model license.

dliebovi/medgemma-stage2-report

MedGemma Stage 2 — Probabilistic Health Report Generator