arXiv:2606.15504v1 Announce Type: new Abstract: In recent years, the advances of large language models and autonomous agents have revolutionized the healthcare field, facilitating diagnosis and improving treatment results. However, most existing AI systems rely on pre-trained knowledge and predefined pipelines, which struggle to learn dynamically from the interactive chat session history that contains patient outcomes and past failures. To address this limitation, we propose VIBEMed, a multi-agent framework with a built-in self-evolution mechanism and architecture-level safety sandbox for robu
Advances in large language models and autonomous agents are converging, enabling more sophisticated and adaptive AI systems for specialized applications like clinical decision support.
This development represents a significant step towards AI systems that can learn dynamically from real-world interactions rather than relying solely on static pre-trained data, enhancing reliability and efficacy in healthcare.
AI systems for clinical decision support will evolve from static knowledge bases to self-evolving agents, capable of adapting based on patient outcomes and interactive session history, leading to more personalized and effective treatment recommendations.
- · Healthcare providers
- · Patients
- · AI healthcare developers
- · Biomedical research
- · Traditional diagnostic companies
- · AI systems lacking adaptive learning
- · Static medical textbook publishers
Improved diagnostic accuracy and personalized treatment plans in healthcare.
Accelerated drug discovery and therapeutic development through advanced simulation and analysis.
Potential for an 'AI doctor' capable of continuous learning and autonomous decision-making in complex medical scenarios, shifting the role of human clinicians.
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Read at arXiv cs.AI