ClinicalAgents: Multi-Agent Orchestration for Clinical Decision Making with Dual-Memory

ClinicalAgents: Multi-Agent Orchestration for Clinical Decision Making with Dual-Memory | Signal Canvas | ScienceToStartup

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Canonical route: /signal-canvas/clinicalagents-multi-agent-orchestration-for-clinical-decision-making-with-dual-memory

stale

Proof freshness: stale
Proof status: unverified
Display score: 7/10
Last proof check: 2026-03-30
Score updated: 2026-04-02
Score fresh until: 2026-05-02
References: 69
Source count: 3
Coverage: 50%

This page is showing the last landed evidence receipt and score bundle because the latest proof data is outside the freshness window.

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Canonical ID clinicalagents-multi-agent-orchestration-for-clinical-decision-making-with-dual-memory | Route /signal-canvas/clinicalagents-multi-agent-orchestration-for-clinical-decision-making-with-dual-memory

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curl https://sciencetostartup.com/api/v1/agent-handoff/signal-canvas/clinicalagents-multi-agent-orchestration-for-clinical-decision-making-with-dual-memory

MCP example

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source_context

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Evidence Receipt

Route status: building

Claims: 8

References: 69

Proof: Verification pending

Freshness state: computing

Source paper: ClinicalAgents: Multi-Agent Orchestration for Clinical Decision Making with Dual-Memory

PDF: https://arxiv.org/pdf/2603.26182v1

Source count: 3

Coverage: 50%

Last proof check: 2026-03-30T22:23:25.368Z

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Watch and verify: ClinicalAgents: Multi-Agent Orchestration for Clinical Decision Making with Dual-Memory

/buildability/clinicalagents-multi-agent-orchestration-for-clinical-decision-making-with-dual-memory

Watchwatch

Subject: ClinicalAgents: Multi-Agent Orchestration for Clinical Decision Making with Dual-Memory

Verdict

Watch

Verdict is Watch because viability or proof quality is intermediate and should be re-evaluated before execution.

Preparing verified analysis

GitHub Code Pulse

No public code linked for this paper yet.

Claim map

Strong 8Mixed 0Weak 0

Evidencepartial
Unlike rigid sequential chains, ClinicalAgents employs a dynamic orchestration mechanism modeled as a Monte Carlo Tree Search (MCTS) process.
Implicationpartial
The abstract explicitly states the use of MCTS for orchestration.
Verificationpartial
partial
Evidencepartial
Central to this framework is a Dual-Memory architecture: a mutable Working Memory that maintains the evolving patient state for context-aware reasoning, and a static Experience Memory that retrieves clinical guidelines and historical cases via an active feedback loop.
Implicationpartial
The abstract clearly describes the Dual-Memory architecture.
Verificationpartial
partial
Evidencepartial
Extensive experiments demonstrate that ClinicalAgents achieves state-of-the-art performance, significantly enhancing both diagnostic accuracy and explainability compared to strong single-agent and multi-agent baselines.
Implicationpartial
The abstract claims state-of-the-art performance and mentions extensive experiments.
Verificationpartial
partial
Evidencepartial
Extensive experiments demonstrate that ClinicalAgents achieves state-of-the-art performance, significantly enhancing both diagnostic accuracy and explainability compared to strong single-agent and multi-agent baselines.
Implicationpartial
The abstract explicitly states the improvement in diagnostic accuracy.
Verificationpartial
partial
Evidencepartial
Extensive experiments demonstrate that ClinicalAgents achieves state-of-the-art performance, significantly enhancing both diagnostic accuracy and explainability compared to strong single-agent and multi-agent baselines.
Implicationpartial
The abstract explicitly states the improvement in explainability.
Verificationpartial
partial
Evidencepartial
Existing methods typically rely on static, linear mappings from symptoms to diagnoses, failing to capture the iterative, hypothesis-driven reasoning inherent to human clinicians.
Implicationpartial
The abstract clearly identifies a limitation of existing methods.
Verificationpartial
partial
Evidencepartial
While Large Language Models (LLMs) have demonstrated potential in healthcare, they often struggle with the complex, non-linear reasoning required for accurate clinical diagnosis.
Implicationpartial
The abstract directly states a challenge faced by LLMs in healthcare.
Verificationpartial
partial
Evidencepartial
As illustrated in Figure 1, the framework comprises three core components: • Agent Pool: This component contains specialized agents representing specific medical roles and tasks. • Clinical Orchestrator: Serving as the executive cont
Implicationpartial
The abstract outlines the main components of the framework.
Verificationpartial
partial

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ClinicalAgents: Multi-Agent Orchestration for Clinical Decision Making with Dual-Memory

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