Pulmonary disease imposes one of the largest and fastest-growing economic burdens in modern healthcare, yet the pricing of respiratory care remains largely opaque to the patients who bear its costs. This review examines how machine learning (ML) can simultaneously optimize provider revenue and reduce patient financial burden in pulmonary medicine, advancing the non-zero- sum framing of AI-driven pricing as a hypothesis to be tested rather than a demonstrated result. We synthesize evidence across four domains: cost prediction and risk stratification, revenue cycle management, patient-facing transparency tools, and the surrounding ethical and regulatory landscape. Ensemble methods such as XGBoost and random forest consistently achieve areas under the receiver operating characteristic curve (AUC) of 0.80–0.88 for identifying high-cost respiratory patients from routinely collected claims data, and severe chronic obstructive pulmonary disease (COPD) exacerbation prediction now reaches an AUC of 0.866 on retrospective data, identifying the high-risk patients who could be targeted for interventions against hospitalizations costing $18,000–$32,000 per episode, though no prospective study has yet confirmed that such targeting produces net savings. Natural language processing is progressively automating clinical coding, and 80% of health systems now report exploring or implementing generative AI in revenue cycle operations. In contrast, patient-facing transparency applications remain underdeveloped, and cost-based prediction targets risk encoding racial and socioeconomic bias into pricing decisions. We conclude that the strongest current evidence supports COPD cost prediction, exacerbation prevention, and coding automation, and we outline research priorities including federated learning, prospective validation, and routine equity auditing of pricing algorithms.

Keywords: Machine Learning; Artificial Intelligence; Pulmonary Medicine; Healthcare Costs; Price Transparency; Revenue Cycle Management; COPD; Financial Toxicity; Algorithmic Bias

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