Toward Clinically Actionable Explainable AI in Pulmonary Arterial Hypertension: Endpoints, Calibration, and External Validation. Comment on Ledziński et al. Personalized Medicine in Pulmonary Arterial Hypertension: Utilizing Artificial Intelligence for Death Prevention. J. Clin. Med. 2025, 14, 8325

Q: What does this research mean for the field?

A machine-learning model (XGBoost) can effectively estimate mortality risk in adult patients with pulmonary arterial hypertension (PAH) while providing interpretable predictions through an SHAP-based strategy. Novelty: ClaimNovelty.NOVEL_FINDING. Consensus alignment: ConsensusAlignment.NEUTRAL.

Gianluca Pagnoni; Aurora Vicenzi; Francesca Coppi

doi:10.3390/jcm15051719

February 25, 2026Journal of Clinical MedicineOpen Access

Toward Clinically Actionable Explainable AI in Pulmonary Arterial Hypertension: Endpoints, Calibration, and External Validation. Comment on Ledziński et al. Personalized Medicine in Pulmonary Arterial Hypertension: Utilizing Artificial Intelligence for Death Prevention. J. Clin. Med. 2025, 14, 8325

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Key Points

To evaluate a machine-learning model for predicting mortality risk in patients with pulmonary arterial hypertension using registry data.
Utilized registry data from patients with pulmonary arterial hypertension.
Applied the XGBoost machine-learning model for risk estimation.
Incorporated an SHAP-based strategy for interpretability of model predictions.
The machine-learning model provides insights into mortality risk factors for individual patients.
SHAP analysis clarifies which determinants contribute to mortality predictions.

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Implication

Comment discusses a machine-learning model estimating mortality risk in pulmonary arterial hypertension, suggesting clinical implications.

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Cite This Study

Pagnoni et al. (2026) studied this question.

synapsesocial.com/papers/699fe41d95ddcd3a253e851e https://doi.org/10.3390/jcm15051719

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Toward Clinically Actionable Explainable AI in Pulmonary Arterial Hypertension: Endpoints, Calibration, and External Validation. Comment on Ledziński et al. Personalized Medicine in Pulmonary Arterial Hypertension: Utilizing Artificial Intelligence for Death Prevention. J. Clin. Med. 2025, 14, 8325

Key Points

No abstract yet

Implication

Discussion

Cite This Study

Toward Clinically Actionable Explainable AI in Pulmonary Arterial Hypertension: Endpoints, Calibration, and External Validation. Comment on Ledziński et al. Personalized Medicine in Pulmonary Arterial Hypertension: Utilizing Artificial Intelligence for Death Prevention. J. Clin. Med. 2025, 14, 8325

Key Points

No abstract yet

Implication

Discussion

Cite This Study

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