MedOrch: Medical Orchestration Platforms

• MedOrch is a multi-agent framework that integrates modular architectures with LLM and VLM capabilities to coordinate complex, multimodal medical workflows.
• It employs standardized action interfaces and tool registries to enable plug-and-play extensibility, transparent reasoning, and auditable clinical decision-making.
• Empirical evaluations demonstrate enhanced diagnostic accuracy and workflow robustness across domains like radiology, robotic surgery, and therapeutic design.

MedOrch refers to a class of medical orchestration platforms and frameworks that leverage multi-agent, modular architectures—often powered by LLMs and vision-LLMs (VLMs)—to coordinate complex, multimodal, and tool-augmented medical decision-making or workflow automation. MedOrch systems are characterized by their extensibility, transparency, and capacity for integrating heterogeneous agents (or tools) to address the demands of domains ranging from clinical diagnostics and patient workflow management to therapeutic design and medical imaging. The term has been used both as a concrete system name and as a generic shorthand for such agent-based orchestration paradigms.

1. Architectural Foundations of MedOrch Systems

MedOrch systems universally adopt a hierarchical or modular multi-agent architecture, where a central orchestrator or mediator coordinates specialized agents or tools that may use domain-specific models, external APIs, or simulated environments.

• Central Orchestrator or Mediator: Functions as the workflow authority, managing task decomposition, agent selection, and coordination. This is either an LLM agent (e.g., in medical VA QA and patient workflow), a finite-state machine (e.g., in drug discovery), or a hybrid planner-executor interface (Chen et al., 8 Aug 2025, Park et al., 10 Nov 2025, Suzuki et al., 25 Dec 2025).
• Specialist Agents: Represent distinct capabilities, such as:
  • Information retrieval (EHR, lab, pathology queries)
  • Imaging interpretation (VLM-based QA, DICOM viewers)
  • Structured tool invocation (e.g., SQL, clinical calculators, PBPK simulators)
  • Multi-modal data manipulation (e.g., 3D model rendering, CT navigation, as in robotic surgery) (Park et al., 10 Nov 2025, He et al., 30 May 2025).

Agent interactions follow well-defined protocols, often realized as a registry of tools with formal input/output schemas. The system's central model (LLM) interleaves chain-of-thought reasoning with explicit tool calls—invoking callable code, web services, or other LLM- or VLM-based agents as needed (He et al., 30 May 2025).

The illustration below summarizes these core patterns:

Component	Role	Example Systems
Orchestrator/Mediator	Workflow logic, coordination, agent selection	(Chen et al., 8 Aug 2025, Suzuki et al., 25 Dec 2025, Park et al., 10 Nov 2025)
Specialist Agents/Tools	Task execution, data access/manipulation	(He et al., 30 May 2025, Park et al., 10 Nov 2025)
Judge/Verifier Agent	Aggregates results, final decision	(Chen et al., 8 Aug 2025)

2. Agent and Tool Integration Paradigms

Agent and tool integration in MedOrch systems is achieved by modular registries and standardized action interfaces:

• Tool Registry: Tools are described by identifiers, input/output schemas, and descriptions. Adding a new tool consists solely of extending the registry; the orchestrator or core LLM can invoke any registered tool via structured tokens or calls (He et al., 30 May 2025).
• Action Determination: Each agent implements a standardized interface (e.g., LLM-based prompt + overlay operator). Agents may be general-purpose or highly domain-specific (e.g., an ADMET property predictor, robotic navigation agent, SQL interface) (Park et al., 10 Nov 2025, Suzuki et al., 25 Dec 2025).
• Plug-and-Play Extensibility: Clinical workflows can be extended by adding new agent classes (e.g., ultrasound control, infusion pump management) without altering orchestrator logic (Park et al., 10 Nov 2025).

For example, in a diagnostic workflow, the orchestrator parses natural-language commands and delegates them to agents capable of structured data retrieval or image interpretation, while logging all tool invocations for traceability (He et al., 30 May 2025).

3. Orchestration Protocols and Reasoning

The core orchestration loop typically involves:

1. Receiving a user prompt or clinical trigger.
2. Summarizing the system state (e.g., medical history, data context).
3. Prompting the LLM (or mediator) with this state, candidate agent functions, and decision rules.
4. The LLM emits the next agent or tool to invoke, providing the required arguments in structured format.
5. Each agent executes the requisite function, returning results to the orchestrator.
6. The orchestrator aggregates, logs, and repeats until a terminal workflow state is reached (e.g., diagnosis completion or agent “EndWorkflow” token) (Park et al., 10 Nov 2025, He et al., 30 May 2025, Chen et al., 8 Aug 2025).

MedOrch systems frequently embed chain-of-thought and self-reflection primitives, particularly in mediator-guided multi-agent frameworks, where an LLM mediator explicitly prompts agents to resolve uncertainties or contradictions, leveraging Socratic dialogue to iteratively improve outputs (Chen et al., 8 Aug 2025).

4. Evaluation, Robustness, and Empirical Results

MedOrch frameworks have been evaluated across diverse clinical domains, including robotic surgery, radiology, pathology VQA, diagnostic reasoning, and therapeutic design. Evaluation protocols typically utilize staged accuracy metrics, workflow-level success rates, and inter-category or multi-modality measures.

• Stage-Level Accuracy and Success Metrics: Systems are assessed on component stages, such as speech-to-text (STT), correction/validation, command reasoning, action determination, and overlay function. Multi-level Orchestration Evaluation Metrics (MOEM) quantify robustness at both command and workflow levels (Park et al., 10 Nov 2025).
• MedOrch (multimodal VQA): Achieves 66.98% average accuracy using three 32B VLMs, outperforming standalone agents by over 3 points, with additional substantial per-modality gains (e.g., +28.4 points in fundus photography QA) (Chen et al., 8 Aug 2025).
• MedOrch (tool-augmented reasoning): In Alzheimer's diagnosis, delivers 93.26% accuracy, surpassing previous state-of-the-art by four percentage points; in chest X-ray classification, Macro AUC of 61.2% (He et al., 30 May 2025).
• Hybrid MedOrchestra (privacy-preserving): Reaches 70.21% accuracy in free-text pancreatic cancer staging, outperforming both local-only LLMs (56.59%) and clinical experts (59.57–65.96%) (Lee et al., 27 May 2025).
• Surgical Agent Orchestration Platform (SAOP): Workflow-level multi-pass success rate of 95.8%, with agent-specific success exceeding 94% (Park et al., 10 Nov 2025).
• MedicalOS: Attains overall diagnosis accuracy of 90.24% in a multi-specialty agent-augmented clinical OS context (Zhu et al., 15 Sep 2025).

5. Transparency, Safety, and Auditable Reasoning

Transparency is a defining feature of MedOrch systems:

• Traceable Reasoning Chains: All intermediate chain-of-thought steps, agent/tool calls, and outputs are recorded in an append-only audit trail, enabling post-hoc clinical audit and regulatory compliance (He et al., 30 May 2025, Zhu et al., 15 Sep 2025).
• Interpretable Decision Paths: In complex scenarios, the platform can surface alternative, evidence-backed hypotheses by rendering multiple reasoning trajectories, each mapping data access to intermediate and final conclusions (He et al., 30 May 2025).
• Safety and Human Oversight: Formal policy layers and schema validation enforce adherence to clinical guidelines. Any deviation or failed validation triggers “review_required” states, escalating to human supervisors (Zhu et al., 15 Sep 2025). No direct code execution by LLM agents is permitted; agent-generated actions are parsed and validated before execution.

6. Privacy, Operational Principles, and Extensibility

MedOrch supports deployment in highly regulated healthcare environments by design:

• Data Privacy Guarantees: Hybrid architectures (e.g., MedOrchestra) confine all patient health information and inference to on-premise LLMs. Cloud-based planners never access real data, satisfying HIPAA and GDPR requirements (Lee et al., 27 May 2025).
• Extensibility and Multi-specialty Adaptation: Systems are designed for plug-and-play agent augmentation, with modular addition of workflow agents covering novel modalities (e.g., digital pathology, infusion pumps) and localization for national language or clinical subdomain adaptation (Park et al., 10 Nov 2025).
• Human-in-the-Loop Flexibility: In drug discovery (OrchestRA), expert users steer the orchestration process by selecting targets, modifying optimization criteria, or demanding intermediate reports, with orchestration logic adaptively looping across agents on demand (Suzuki et al., 25 Dec 2025).

7. Limitations and Prospective Directions

Although successful across diverse tasks, current MedOrch systems face several limitations:

• Latency: Multi-agent dialogue rounds increase inference time, which may prove problematic for real-time settings (Chen et al., 8 Aug 2025).
• Reliance on Model Internal Knowledge: Absence of retrieval-augmented generation or external diagnostic tools can limit performance in domains requiring the latest clinical findings (Chen et al., 8 Aug 2025).
• Generalization: Many deployments are validated in limited institutions or disease areas—scalability to broader clinical contexts remains an open research frontier (Lee et al., 27 May 2025).
• Prompt Tuning and Error Recovery: Imperfect prompt designs can lead to suboptimal reasoning or wasted dialogue rounds; future MedOrch systems are anticipated to self-evolve by logging failures, retraining routing modules, and dynamically refining prompts (Park et al., 10 Nov 2025).

Prospective work includes integration of retrieval-augmented agents, tool-augmented vision and LLMs, uncertainty-calibrated decision logic, and “self-evolving orchestration” adaptive to changing medical device and terminological standards (Park et al., 10 Nov 2025, Chen et al., 8 Aug 2025, Suzuki et al., 25 Dec 2025).