From Disease-Specific Models to Broad Clinical Utility: A Perspective on AI Hybrid Ensemble Frameworks.

Area of Science:

• Computational science
• Medical informatics
• Artificial intelligence

Background:

• Current artificial intelligence (AI) models in medicine often lack generalizability and face challenges in balancing interpretability with accuracy.
• Existing AI models are frequently disease-specific, rely on single algorithms, have limited external validation, and suffer from biased feature importance estimation.

Purpose of the Study:

• To address the limitations of current AI models in clinical practice.
• To propose a principled artificial intelligence hybrid ensemble framework for robust and interpretable medical predictive modeling.
• To bridge the gap between advanced computational methods like automated machine learning (AutoML) and neural architecture search (NAS) and current clinical modeling practices.

Main Methods:

• Developed a hybrid ensemble framework for artificial intelligence in medicine.
• Integrated diverse learners within the framework.
• Implemented consensus-driven validation across independent cohorts.
• Utilized Shapley Additive exPlanations (SHAP) for transparent feature attribution.

Main Results:

• The proposed framework emphasizes methodological robustness and interpretability.
• It aims for cross-disease applicability, moving beyond disease-specific models.
• The framework facilitates the translation of AI models into clinical practice.

Conclusions:

• The artificial intelligence hybrid ensemble framework offers a path toward more reliable and generalizable AI tools in healthcare.
• This approach addresses key challenges in AI adoption, including interpretability and validation.
• Enhanced AI frameworks are crucial for advancing predictive modeling in diverse clinical settings.