MedCollab: IBIS-Guided Multi-Agent Collaboration with Hierarchical Disease Relation Chains for Clinical Diagnosis
arXiv:2603.01131v3 Announce Type: replace-cross Abstract: Clinical diagnosis is a gradual process of evidence integration, in which physicians move from symptoms and medical history to examinations, competing hypotheses, disease relations, and treatment decisions. Large language models have advanced medical text understanding and generation. Yet their clinical use remains limited by weak evidence grounding, opaque reasoning, and inconsistent links among differential diagnosis, final diagnosis, diagnostic basis, and treatment planning. We introduce MedCollab, a multi-agent framework for full-cy
Advances in large language models are enabling more sophisticated multi-agent frameworks, making complex clinical diagnosis a ripe area for AI application.
This development represents a significant step towards more reliable and transparent AI applications in high-stakes fields like medicine, potentially augmenting human expertise.
The transparency, reasoning, and evidence-grounding of AI in clinical diagnosis are being addressed, moving beyond mere text generation to integrated diagnostic processes.
- · Healthcare providers
- · Patients
- · AI developers
- · Medical technology sector
- · AI solutions with opaque reasoning
- · Traditional diagnostic software
AI-assisted clinical diagnosis becomes more widespread and trusted across healthcare systems.
Reduced misdiagnosis rates and more personalized treatment plans become achievable due to sophisticated AI analysis.
The role of human physicians evolves towards oversight, complex case consultation, and compassionate care, supported by AI-driven insights.
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Read at arXiv cs.AI