arXiv:2605.29368v1 Announce Type: cross Abstract: The intricate nature of modern surgical care necessitates intelligent systems that can synthesize extensive patient records, support collaborative decision-making, and provide transparent, auditable reasoning across the entire perioperative workflow. Although web-based Large Language Models (LLMs) possess advanced reasoning capabilities, they are ill-equipped for surgical applications due to critical limitations: input length constraints, incomplete memory management, and limited traceability. To address this issue, we present SURGENT, a surgic
The increasing complexity of modern surgical procedures and the advancements in large language models make the development of AI-driven surgical assistance systems timely.
This development indicates a tangible application of AI agents in a high-stakes, knowledge-intensive domain, demonstrating their potential to improve efficiency and safety in critical operations.
The paradigm for surgical support could shift from manual information retrieval and human collaboration to an AI-augmented, multi-agent system providing real-time, traceable assistance.
- · Healthcare providers (hospitals, clinics)
- · AI software developers
- · Patients
- · Medical technology companies
- · Inefficient manual surgical support systems
- · Companies relying on outdated digital health records
Improved surgical outcomes and reduced medical errors due to enhanced decision support and information synthesis.
Accelerated adoption of AI systems in other complex medical fields, leading to a broader transformation of healthcare delivery.
Potential for new legal and ethical frameworks to govern autonomous AI agents in critical human-centric environments.
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Read at arXiv cs.AI