BlixtAI logo
Sign Up

Taming LLMs for Infectious-Disease Lab Reports: From Risk to Reliability

In this blog post, we will explore how to transform a generative AI system into a safe, consistent, and up-to-date engine for infectious-disease lab test reports by addressing hallucination, non-determinism, and out-of-date knowledge with retrieval-augmented generation and living reference pipelines.

Cover Image for Taming LLMs for Infectious-Disease Lab Reports: From Risk to Reliability

Taming LLMs for Infectious-Disease Lab Reports: From Risk to Reliability

Generating infectious-disease lab test reports is a precision task. Yet out-of-the-box large language models (LLMs) can hallucinate, vary across runs, and fall behind fast-moving guidelines—three failure modes that are unacceptable in clinical settings. Below is a pragmatic blueprint to address each shortcoming with retrieval-augmented generation (RAG), deterministic engineering, and a living data pipeline anchored to authoritative sources.

1) Hallucination: Ground the model with authoritative sources (RAG)

The problem: LLMs can fabricate drug names or cite non-existent regimens. In lab reports, that’s more than a nuisance—it’s a patient-safety hazard.

The fix: Bind the model to trusted references via RAG and require provenance in every recommendation.

Build a source-of-truth corpus:

  • Labeling and drug facts: Use regulatory endpoints and bulk downloads to verify drug existence, ingredients, and labeling. Pair with current package-inserts for latest data.
  • Global/US treatment guidance: Include WHO’s antibiotic classification and CDC’s clinical guidance as clinical policy anchors. Where relevant, add specialist society practice guidelines.

Enforce retrieval-first generation:

  • For each organism + susceptibility profile, retrieve the top K passages from the corpus; only then generate. Require the LLM to quote the specific source lines (ID, section, date).
  • Add post-generation checks: confirm that any antibiotic mentioned appears in the validated set and is classified appropriately. Fail closed if not found.

2) Non-determinism: Make recommendations consistent, not creative

The problem: The same inputs can yield different outputs—fine for brainstorming, unsafe for reports.

The fix: Engineer for repeatability across retrieval, decoding, and policy application.

  • Deterministic decoding: Prefer greedy or beam search (do_sample=False) and temperature=0 to remove stochasticity. Pair with seeding and framework-level deterministic modes.
  • Canonical retrieval: Keep RAG inputs stable by freezing retriever settings (embedding model/version, top-K, filters, ranking). Cache “canonical context bundles” per test archetype.
  • Policy via expert database: Externalize recommendations (dose, duration, alternatives, contraindications) into a versioned expert knowledge base distilled from guidance. The LLM then renders this policy table for the patient context rather than inventing regimens.
  • Structured classification before generation: Use a robust classifier (e.g., BART) to map lab results to a treatment class (uncomplicated cystitis, PID, ESBL-E bacteremia, etc.), then apply the appropriate policy template.
  • Response caching: Cache identical queries (and near-duplicates via semantic caching) so routine cases return the same, verified text and citations.

3) Out-of-date knowledge: Build a living pipeline that updates itself

The problem: Base LLM knowledge is always months behind—dangerous when dosing or resistance guidance changes.

The fix: Automate a data refresh → index → retrieve loop tied to authoritative endpoints.

A practical pipeline:

  1. Scheduled ingestion (e.g., cron/Airflow/GitHub Actions).
    • Package-inserts: daily/weekly/monthly dumps of all labels.
    • Regulatory APIs/data: download zipped datasets for updates.
    • Clinical guidance: use content APIs to keep guidance in sync.
    • Specialist society updates: monitor major guideline updates (e.g., antimicrobial-resistance treatment).
  2. Normalize & version. Parse to a common schema (drug → ingredient(s) → dose forms → indications → contraindications) and stamp the source date so every recommendation can be reproduced “as of” a specific day. (Provenance is your audit trail.)
  3. Chunk, embed, and index. Use a vector store such as FAISS, Milvus, or pgvector; choose ANN indexes (HNSW/IVF) appropriate for corpus size and latency.
  4. Retrieval policy. At runtime, retrieve only documents newer than the last guideline update and filter by jurisdiction (e.g., US labeling for US patients). Include the “as-of” date in the report footer.
  5. Human-in-the-loop & safety checks. Route novel or conflicting cases to stewardship pharmacists/ID specialists; log every source snippet and model hash for QA.

Final thoughts

For infectious-disease lab reports, accuracy, consistency, and currency are non-negotiable. Grounding with RAG, taming generation with deterministic settings and classification, and operating a living data pipeline transform LLMs from risky free-form generators into traceable clinical summarizers that cite and conform to regulatory and society sources. Always treat the model as a documentation assistant, not an autonomous prescriber, and keep a clinician in the loop for exceptions and governance.

References

  1. „Reducing hallucinations in structured outputs via Retrieval-Augmented Generation”, ArXiv 2024.
  2. „Understanding retrieval-augmented generation (RAG): a response to hallucinations”, Ontoforce Blog.
  3. „How to Prevent LLM Hallucinations: 5 Proven Strategies”, Voiceflow Blog.
  4. „Outpatient Clinical Care for Adults | Antibiotic Prescribing and Use”, CDC.
  5. „Infectious Diseases Society of America 2024 Guidance on the Treatment of Antimicrobial-Resistant Gram-Negative Infections”, IDSA.
  6. „Core Elements of Hospital Antibiotic Stewardship Programs”, CDC.

Disclaimer: This post describes engineering patterns for report generation and is not medical advice. Clinical decisions must be made by licensed professionals using full patient context.