MedicalAgentsBench for Complex Medical Reasoning: Comparing Internalized Reasoning Models versus Externalized Agent-based Frameworks
arXiv:2503.07459v3 Announce Type: replace Abstract: Complex medical reasoning requires integrating heterogeneous clinical evidence across multiple inference steps. Large language models (LLMs) now approach this through two routes: internalized reasoning and externalized agent scaffolding (frameworks that decompose problems collaboratively amongst multiple LLMs). To determine whether these routes are exclusive or complementary, we introduce MedicalAgentsBench, a filtered benchmark of 862 complex clinical questions drawn from the union of eight medical datasets via difficulty-aware curation and
The proliferation of advanced LLMs necessitates nuanced methods for evaluating their capabilities, especially in complex, high-stakes domains like medicine, driving the creation of specialized benchmarks.
This benchmark provides critical insights into the architectural efficacy of AI in medical reasoning, differentiating between internal model improvements and external agentic frameworks, which will guide future AI development and application in healthcare.
The explicit comparison of internalized reasoning versus agent-based frameworks in complex medical tasks informs strategic choices in AI model design and deployment for critical applications.
- · AI healthcare developers
- · Medical research institutions
- · Patients receiving AI-augmented care
- · AI models without robust reasoning capabilities
- · Traditional diagnostic methods
Improved accuracy and reliability of AI diagnostic and research tools.
Acceleration of drug discovery and personalized medicine through more capable AI agents.
Enhanced global health outcomes and reduced healthcare costs due to efficient AI integration.
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Read at arXiv cs.CL