Human-in-the-Loop Multi-Agent Ventilator Decision Support with Contextual Bandit Preference Learning
arXiv:2605.23320v1 Announce Type: new Abstract: Ventilator decision support requires sequential decisions that track evolving physiology and disease trajectories while respecting safety boundaries and clinician specific tuning styles. Rule based approaches rarely generalize personalization, and end to end reinforcement learning or single large language model systems remain difficult to control and audit. We propose the Ventilator Decision Support System (VDSS), a human in the loop multi agent framework that coordinates modular decision components through contract driven structured interfaces a
The proliferation of advanced AI research and increasing demand for robust and auditable AI systems in critical applications like healthcare is driving innovation in human-in-the-loop decision support.
This development represents a significant step towards deploying AI in high-stakes environments, addressing key concerns around safety, control, and personalization in medical and other critical decision-making processes.
Traditional rule-based or purely autonomous AI systems for critical support are being augmented by more sophisticated multi-agent, human-in-the-loop architectures that allow for dynamic clinician input and auditable processes.
- · Healthcare providers
- · Patients requiring critical care
- · AI ethics and safety researchers
- · Medical technology companies
- · Developers of uninterpretable black-box AI systems
- · Legacy medical device manufacturers resistant to AI integration
Improved patient outcomes and more personalized ventilator management reducing clinician burden.
Expansion of similar human-in-the-loop multi-agent AI frameworks into other critical industrial and medical sectors.
New regulatory frameworks and certification standards emerging for auditable and safe AI systems in life-critical applications.
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Read at arXiv cs.AI