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Prolonged mechanical ventilation (PMV), affecting 5–15% of ICU patients, is associated with high mortality (30–50%), long-term disability, and substantial healthcare costs exceeding $100,000 per admission. These patients often require extended respiratory support beyond 14–21 days and consume significant ICU resources. Current weaning strategies rely on fixed spontaneous breathing trial (SBT) criteria (e.g., RSBI thresholds, oxygenation, respiratory rate), which fail to account for the heterogeneous and evolving physiology of PMV patients. This reduces weaning to discrete events rather than a continuous adaptive process. We propose reinforcement learning from human feedback (RLHF) as a superior framework for weaning, enabling AI systems to learn sequential decision-making policies from clinician preferences across patient trajectories. Traditional protocols ignore temporal dependencies such as prior SBT outcomes, sedation exposure, and respiratory muscle trends. While standard reinforcement learning supports sequential optimization, it depends on difficult-to-define reward functions. RLHF overcomes this by learning reward signals directly from clinician comparisons, aligning model behavior with real-world clinical judgment. Research should shift toward RLHF-based dynamic weaning policies rather than static prediction models. Clinical stakeholders should support data collection and prospective evaluation of RLHF-guided weaning versus standard protocols. RLHF offers a necessary advancement for personalized PMV weaning, addressing limitations of rigid protocols and improving alignment with clinical decision-making.