MedGemma-4B 1.5 for structured lab-report extraction: quantization reliability, latency on-prem, and validation guidance

HAI-DEF
1

Hi Medgemma team,

We’re building a local, on-prem clinical decision-support pipeline that extracts structured findings from lab/pathology reports and grounds interpretation against region-specific clinical guidelines via RAG — MedGemma is used narrowly for extraction and plain-language narration, with all numeric thresholds and clinical logic computed deterministically in code, not by the model

  1. On structured-output reliability under quantization: We’ve seen medgemma-1.5-4b-it reliably return schema-conforming JSON for structured extraction at BF16, but a quantized Q8_0 build (medgemma-1.5-4b-it-Q8_0.gguf) of the same weights failed to produce valid JSON across all 3 retries specifically on a denser input page (8 structured fields in one call), while succeeding on simpler pages. Is this a known failure mode of quantization affecting structured-output/JSON-mode reliability specifically (vs. general accuracy), and is there a recommended minimum precision or quantization scheme for structured extraction tasks specifically, as opposed to free-text generation?
  2. On intended use vs. our actual use— validating our extraction/narrowed role approach: Your model card’s document-understanding evaluation reports Macro/Micro F1 on PDF-to-JSON lab report extraction (e.g., the Mendeley Clinical Laboratory Test Reports benchmark). Is there guidance on what F1 threshold or evaluation protocol would be considered sufficient validation for a developer’s own extraction pipeline, especially when — per the terms — that extraction output feeds a code decides, model only extracts architecture rather than being trusted directly?
  3. On latency/deployment guidance for on-prem, resource-constrained settings: For a clinic-scale deployment (single machine, local inference via Ollama, no dedicated GPU server), we’re seeing multi-minute latency on multi-page documents even after quantization tuning. Does HAI-DEF have deployment guidance or benchmarks for MedGemma-4B specifically in resource-constrained, on-prem clinical settings (vs. cloud/TPU-serving assumptions), and are there recommended serving configurations (batching, concurrency, quantization) for this class of hardware?
  4. On known failure modes / fine-tuning provenance for population-specific guidelines: We ground MedGemma’s output against India-specific clinical guidelines (RSSDI/ICMR) via RAG rather than relying on the model’s own training knowledge for thresholds. Is there documentation on what population/geographic distribution MedGemma’s training and evaluation data represents, and any known failure modes when applied against guideline systems or reference ranges outside that distribution?" is this what you intend

regards

2

Hi Medgemma Team,

Any replies for the questions shared. Awaiting your insights

3

Hi @kkdu ,

Thank you for the detailed description of your pipeline. Here are the few clarifications and pointers.

1. On structured-output reliability under quantization:

The root cause of schema failure on dense pages stems from attention precision loss on delimiters: in smaller parameter models (4B), uniform quantization like Q8_0 introduces slight numerical drift in attention logits over long prefill sequences. On dense multi-field pages, the model loses precision specifically on structural syntax tokens (curly braces { }, quotation marks ", commas ,), causing JSON syntax degradation even while the model’s semantic medical understanding remains intact.

Additionally, MedGemma is primarily fine-tuned for clinical dialogue, multimodal reasoning, and document understanding rather than native tool-calling/function-calling. Because JSON generation relies on in-context prompting rather than native function-calling weights, unquantized BF16 has sufficient floating-point precision to follow formatting examples, whereas raw quantized weights alone cannot guarantee deterministic schema adherence under heavy token density.

Instead of relying on the model to spontaneously output valid JSON characters, you can try passing a strict JSON schema to the inference engine which you are using similar to like this.

# Define your exact 8-field lab schema
schema = {
    "type": "object",
    "properties": {
        "patient_id": {"type": "string"},
        "test_name": {"type": "string"},
        "hemoglobin": {"type": "number"},
        "wbc_count": {"type": "number"},
        "platelets": {"type": "number"},
        "fasting_glucose": {"type": "number"},
        "hba1c": {"type": "number"},
        "creatinine": {"type": "number"}
    },
    "required": ["test_name", "hemoglobin", "wbc_count"]
}

Alternatively, rather than extracting all 8+ disparate lab fields in a single prompt pass, break the prompt down by sub-panel (e.g., CBC Panel , Lipid Panel, Renal Function).

2. On intended use vs. your actual use: Your architectural approach “code decides, model only extracts” aligns directly with the HAI-DEF Terms of Use and intended use principles:

“MedGemma is not intended to autonomously diagnose or suggest treatment for any disease or condition, and should not be used for primary medical advice.”

By constraining MedGemma purely to extraction while delegating clinical thresholding and decision-making to deterministic code, you keep the system compliant and safe.

While the model card reports Macro/Micro F1 on PDF-to-JSON benchmarks (like Mendeley), standard NLP F1 primarily measures token/grammatical similarity. In a clinical setting, standard F1 can produce misleading scores. So rather than relying solely on generic text F1, could you please explore with these metrics as well Exact Numeric & Unit Accuracy,Critical / Panic Value Recall, Schema Completeness Rate for your usecase.

3.On latency/deployment guidance for on-prem, resource-constrained settings: As MedGemma 4B uses a SigLIP (Vision Transformer) encoder, and each high-resolution page image is broken into hundreds of image patches 256 to 512 tokens per page.

A 4-page lab report injects 1,500 to 2,000 +multimodal tokens into the context window. On CPU (which lacks matrix tensor accelerators like CUDA/Tensor Cores), performing quadratic self-attention over thousands of multimodal tokens causes multi-minute prefill times before generating the very first token.

Could you try: For typed digital PDFs or scanned lab reports, extract the raw text using a lightweight local CPU engine (such as PyMuPDF, Tesseract, or PaddleOCR), which takes under 150 ms. Feed the extracted text to MedGemma in text-only mode. Processing around 400 text tokens on CPU takes only 1 to 2 seconds, reducing your end-to-end latency from minutes down to a few seconds.

The official medgemma serving containers python/serving and Model are designed with GPU/TPU acceleration in mind (e.g., NVIDIA A10G/L4/A100 with vLLM/Triton), where multimodal prefill is completed in sub-seconds.

4. On known failure modes / fine-tuning provenance for population-specific guidelines: Grounding against local clinical guidelines via RAG rather than trusting the model’s internal parametric memory is the exact recommended pattern for regional deployments. The data provenance and geographic representation are documented in the MedGemma Model Card.

Thank You

4

Thank you for the detailed and specific answers — very helpful, especially
the mechanistic explanation on quantization and the metrics pushback.
Following up on each:

1. Structured-output reliability under quantization : Good catch on separating syntax-level vs. schema-level failure — thatbprompted us to check our actual error more carefully. We already pass`“format”: “json”` to Ollama (grammar-constrained decoding), so the
failure we saw wasn’t malformed syntax (braces/quotes) — `json.loads()`
succeeded on the output, it was just missing our required key. So the
failure mode looks more structural/semantic than syntactic in our case.

Given that, your suggestion to pass a full JSON Schema (rather than the
bare `“json”` string) to the inference engine looks like the right fix —
we’ll try constraining to our exact field schema rather than generic
valid-JSON and report back what we find.

2. Validation metrics: Agreed on moving past generic token-F1 — Exact Numeric & Unit Accuracy,
Critical/Panic Value Recall, and Schema Completeness Rate are a much
better fit for what actually matters in this use case (a missed
abnormal finding matters more than aggregate similarity). We’ll add
these to our own structural test suite.

3. Latency/deployment — one clarification, since this is where we most
want to make sure we’re solving the right problem :

Our current phase is text-only. We extract report text via `pdfplumber`
(for digital PDFs) or Tesseract OCR (for photo uploads) *before* anything
reaches MedGemma — the model only ever receives plain extracted text in
our pipeline right now, never a raw page image. We’re deliberately
holding off on the multimodal/vision path (feeding MedGemma page images
directly) until we’ve got the text-only extraction path solid — so the
SigLIP/vision-token explanation likely doesn’t apply to the latency we
measured.

Given that, and your own reference point (~400 text tokens taking 1-2s
on CPU) — does multi-minute latency still track as expected for
text-only extraction, at a larger prompt size than that example (our
system prompt plus a full page of report text plus a 2048-token output
budget, per pass, two passes per page)? We also currently request a
fixed `num_ctx: 16384` on every call regardless of actual content size —
is that itself a meaningful source of per-call overhead on CPU
independent of prompt length, or is context window size effectively free
until you approach the limit?

4. Population/geographic distribution:

Understood — we’ll continue treating the RSSDI/ICMR RAG grounding as the
primary safeguard here rather than the model’s parametric knowledge,
consistent with your recommendation. Noting for our own records that the
model card’s listed training/eval sources appear concentrated in
US/EU/Brazil/Colombia/Australia, with AfriMed-QA as the one clearly
non-Western text benchmark — no explicit South Asian representation
called out. We’ll treat any RSSDI/ICMR-specific claim as needing our own
RAG-grounding to hold up the burden, not the base model.

One more question, prompted by something else in the model card: it
notes MedGemma “may be more sensitive to the specific prompt used than
Gemma 3.” Our extraction prompts are fairly long (explicit anti-
fabrication instructions, exact schema, several paragraphs of guardrails)
— is there guidance on prompt length/instruction density thresholds
where that sensitivity becomes more pronounced, or documented best
practices for prompt engineering specifically for MedGemma’s structured-
extraction use case?

Thanks again for the thorough response — will report back once we’ve
tested the JSON Schema constraint.

regards

Vijay

5

Hi @LK_Kadali , Medgemma Team, your insights on the discussion will be useful for my work.