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Development and validation of VentPilot: an AI-based recommendation system for mechanical ventilation

Background Mechanical ventilation requires repeated adjustment to changing patient physiology, but consistent individualized management remains challenging. We developed VentPilot, an artificial intelligence-based system for recommending ventilator settings, and evaluated it in multicenter retrospective validation cohorts. Methods VentPilot was developed using offline reinforcement learning on high-resolution physiologic and ventilator trajectories from Seoul National University Hospital (SNUH), with a reward co…

By the Truvace desk1 min read1 sourcePeer-reviewedTRV-2026-1243· cite & verify
Development and validation of VentPilot: an AI-based recommendation system for mechanical ventilation
Ventconstraint1 by Balaji.md au. CC BY-SA 4.0 · https://creativecommons.org/licenses/by-sa/4.0

In brief

Researchers developed VentPilot, an AI-based recommendation system for mechanical ventilation, and tested it retrospectively in cohorts from Seoul National University Hospital and Mayo Clinic totaling 4296 mechanically ventilated adults. Using fitted Q-evaluation and concordance analyses, they estimated higher returns for VentPilot than observed clinician behavior and found higher concordance linked to more ventilator-free days.

The association between AI-concordant ventilation and improved outcomes suggests potential clinical value for individualized ventilator management, but because all results are retrospective and based on estimated returns and concordance, prospective evaluation is still needed to confirm safety, usability, and actual impact on patient care.

Main points

  1. VentPilot was developed using offline reinforcement learning on high-resolution physiologic and ventilator trajectories from Seoul National University Hospital
  2. Evaluation included 4296 mechanically ventilated adults: 3002 derivation, 283 internal validation at SNUH, and 1011 external validation at Mayo Clinic
  3. Reward combined ventilator-free days and intensivist-derived preference feedback, assessed via fitted Q-evaluation versus observed clinician behavior
  4. Inverse probability-weighted analysis compared outcomes between patients with higher versus lower concordance with VentPilot recommendations

The gain

In multicenter retrospective validation, greater concordance between clinician ventilator settings and VentPilot AI recommendations was associated with more ventilator-free days within 28 days, lower 28-day mortality, and shorter ICU length of stay.

The rundown

VentPilot was trained with offline reinforcement learning on high-resolution physiologic and ventilator trajectories from Seoul National University Hospital, using a reward combining ventilator-free days and intensivist-derived preference feedback.

Validation used fitted Q-evaluation to estimate return under the prespecified reward and an inverse probability-weighted analysis of concordance, reporting mean differences in ventilator-free days of 5.0 days internally and 3.1 days externally, plus associations with mortality and ICU length of stay among 4296 adults.

Sources

  1. Peer-reviewedJournal of Intensive Care2026-10-01

The debate