Consensus Framework

Overview

The Consensus Mechanism Framework integrates multiple specialized medical "expert" models, enhancing clinical decision support through collaborative deliberation. It operates by orchestrating inputs from distinct, configurable medical specialty models to synthesize a unified, well-supported clinical decision.

This document details the Consensus Mechanism’s modular architecture, expert model integration, and its evaluation methodologies.

Core Architecture

System Design

The Consensus Mechanism comprises:

• Triage Model: Identifies the clinical task type and selects appropriate medical specialty models.

• Expert Model Block: Consists of independent expert agents that evaluate tasks from specialized clinical perspectives, each generating probability distributions over potential solutions.

• Consensus Aggregation Model: Synthesizes expert inputs into a final clinical decision, leveraging nuanced evaluation and probabilistic weighting.

Expert-Based Decomposition

The Triage Model evaluates clinical queries (Q) and assigns domain-specific models based on identified medical specialties:

LLM_triage(Q) ⇒ (Task Type, Specialties)
Specialties = {s₁, s₂, ..., sₙ}

Each specialist model (LLM_sᵢ) generates an independent response and a probability distribution:

R_expert^(i) = LLM_sᵢ(Q, Task Type)

The final aggregated output is determined through:

O_final = f(R_expert^(1), ..., R_expert^(n))
A_final = LLM_consensus(O_final)

Probability Weighting

To reflect clinical uncertainty, the Consensus Mechanism aggregates expert probabilities using a Weighted Log Opinion Pool (WLOP):

P_combined(X) = ∑ wᵢ log pᵢ(X)
P_normalized(X) = exp(P_combined(X)) / ∑ exp(P_combined(Xⱼ))

This approach moderates expert biases, rewarding consensus while accommodating individual expert uncertainties.

Cascade Boosting for Probabilities

To further refine probability assessments, Cascade Boosting amplifies the likelihood of answers based on frequency and rank across experts:

• Establish frequency fₓ,ᵣ for answer X at rank r.

• Apply cascade weighting (θ) progressively decreasing for lower ranks.

Boosted scores calculated:

BoostedScore(X) = P_normalized(X) + λ_boost · ∑(fₓ,ᵣ × θᵣ)

The final boosted probability:

P_final(X) = exp(BoostedScore(X)) / ∑ exp(BoostedScore(Xⱼ))

Reaching Consensus

The final decision integrates each expert’s analysis through structured deliberation, examining not only aggregated probabilities but also expert reasoning and justification.

The Consensus Model explicitly factors nuanced clinical reasoning, allowing a sophisticated, clinically coherent final recommendation beyond numerical probability alone.

Evaluation Methodologies

Benchmarking Approach

Consensus Mechanism performance was evaluated using three standard medical benchmarking datasets: MedMCQA, MedQA, MedXpertQA, and a differential diagnosis dataset, DDX+. Each dataset measures unique aspects of medical decision-making, including specialized knowledge, diagnostic accuracy, and clinical reasoning skills. The combination of datasets provides a comprehensive evaluation of the Consensus Mechanism’s performance across diverse medical scenarios and complexities.

Accuracy Assessment

• Raw accuracy: Overall accuracy is measured along with a detailed breakdown of performance specific to medical specialties and body systems to pinpoint strengths and weaknesses.

  • Key Results: Across all benchmarks the Consensus Mechanism achieved higher overall accuracy, notably (61.2%) compared to O3-high (53.0%) on MedXpertQA, demonstrating improvement on complex clinical reasoning tasks.

• Top-K performance: Evaluates how frequently the correct answers appear within the top-ranked probabilities, which is crucial for scenarios where exact matches are less certain but narrowing down possibilities significantly improves clinical utility.

  • Key Results: On differential diagnosis tasks, Consensus demonstrated superior accuracy, achieving a top-1 accuracy of 52.0% compared to O3-high’s 45.2%.

• Reliability analysis: Calibration assessments are performed to ensure that confidence intervals provided by the model reliably correlate with the observed accuracy, thus confirming the trustworthiness of generated recommendations.

  • Key Results: The Consensus Mechanism showed better calibration than O3-high, significantly reducing overconfidence and providing more trustworthy clinical predictions.

Future Enhancements

• Enhanced specialization: Expand domain-specific judge models for nuanced evaluation across diverse clinical domains.

• Predictive quality assessment: Implement proactive predictions about output quality to allow preemptive corrections and quality assurance.

• Cross-modal evaluation: Assess consistency and coherence across different clinical data modalities, including text, imaging, and structured medical data.

Conclusion

The Consensus Mechanism provides a robust, flexible decision-support framework suitable for adaptive clinical environments, significantly enhancing diagnostic accuracy, reducing biases, and improving clinical reliability. Its modularity and ongoing enhancements ensure long-term adaptability to evolving clinical standards and technological advancements.