[Paper] Multi-LLM Collaboration for Medication Recommendation

Source: arXiv - 2512.05066v1

Overview

The paper explores how multiple large language models (LLMs) can work together—rather than in isolation—to produce safer, more reliable medication recommendations from short clinical vignettes. By treating the interaction between models as a “chemistry” problem, the authors show that carefully orchestrated ensembles can reduce hallucinations and improve consistency, a crucial step toward trustworthy AI assistants in healthcare.

Key Contributions

• LLM Chemistry Framework: Extends the authors’ previous “LLM Chemistry” concept to quantify and optimize collaborative compatibility among heterogeneous LLMs.
• Interaction‑Aware Ensemble Design: Introduces a systematic way to combine models that balances complementary strengths while suppressing error amplification.
• Real‑World Clinical Evaluation: Tests the Chemistry‑guided multi‑LLM system on authentic patient scenarios, demonstrating measurable gains in recommendation quality and stability.
• Calibration & Stability Metrics: Proposes new evaluation metrics (e.g., inter‑model agreement, calibration error) tailored to the safety‑critical domain of medication prescribing.
• Open‑Source Baseline: Releases code and prompts used for the experiments, enabling reproducibility and rapid iteration by the developer community.

Methodology

1. Model Pool Selection: The authors assemble a diverse set of LLMs (e.g., GPT‑4, Claude, LLaMA‑2) that differ in size, training data, and prompting style.
2. Chemistry‑Inspired Interaction Modeling:
   • Each model’s output is encoded as a vector representation.
   • Pairwise “affinity” scores are computed using a similarity function that captures how well the models’ reasoning aligns.
   • High‑affinity pairs are encouraged to collaborate, while low‑affinity pairs are down‑weighted to avoid destructive interference.
3. Collaborative Prompting Pipeline:
   • A primary model generates an initial medication recommendation.
   • Secondary models critique, refine, or corroborate the suggestion based on the affinity scores.
   • A final aggregation step selects the most consensual answer, applying a calibration layer that penalizes outlier suggestions.
4. Evaluation Setup: The system is run on a curated dataset of de‑identified clinical vignettes covering common conditions (e.g., hypertension, diabetes). Ground‑truth recommendations are derived from established clinical guidelines.

The approach is deliberately modular, allowing developers to plug in new LLMs or swap out the affinity metric without redesigning the whole pipeline.

Results & Findings

• Effectiveness: The Chemistry‑guided ensemble outperforms both individual models and a simple majority‑vote ensemble, closing the gap to expert‑level recommendations.
• Stability: Agreement among models rises significantly, indicating that the system produces more consistent outputs across runs.
• Safety: Hallucination (i.e., suggesting an inappropriate medication) drops to a single‑digit percentage, a critical improvement for clinical adoption.

The authors note that the gains are most pronounced when the model pool includes both high‑capacity (e.g., GPT‑4) and more specialized, smaller models, confirming the value of complementary expertise.

Practical Implications

• Clinical Decision Support (CDS) Tools: Developers can embed the Chemistry‑guided ensemble as a backend service for electronic health record (EHR) systems, offering clinicians a second opinion that is less prone to hallucination.
• Regulatory Compliance: Improved calibration and reduced error amplification help meet emerging AI‑in‑healthcare standards (e.g., FDA’s Good Machine Learning Practice).
• Rapid Prototyping: The modular pipeline enables teams to experiment with new LLMs as they become available, without re‑engineering the entire recommendation engine.
• Cross‑Domain Transfer: The interaction‑aware ensemble concept can be adapted to other safety‑critical domains such as legal advice, financial risk assessment, or autonomous vehicle decision‑making.
• Developer Tooling: The released open‑source library includes utilities for computing affinity scores, managing prompt orchestration, and visualizing model agreement—useful building blocks for any multi‑LLM application.

Limitations & Future Work

• Dataset Scope: The evaluation uses a limited set of common conditions; rare diseases and polypharmacy scenarios remain untested.
• Latency Overhead: Coordinating multiple LLM calls introduces inference latency that may be prohibitive for real‑time bedside use.
• Affinity Metric Simplicity: Current similarity measures are based on surface text embeddings; richer semantic or causal reasoning metrics could further improve collaboration.
• Human‑in‑the‑Loop Validation: The study stops at automated metrics; extensive clinician user studies are needed to assess trust and usability in practice.

Future research directions include scaling the framework to larger model pools, optimizing the orchestration for low‑latency environments, and integrating explicit uncertainty quantification to surface confidence levels to end‑users.

Authors

• Huascar Sanchez
• Briland Hitaj
• Jules Bergmann
• Linda Briesemeister

Paper Information

• arXiv ID: 2512.05066v1
• Categories: cs.LG, cs.AI, cs.CL
• Published: December 4, 2025
• PDF: Download PDF