Challenges and Future Directions

Abstract

Machine learning (ML) and optimization have the potential to transform healthcare, but several challenges hinder their widespread adoption. This chapter explores the limitations in ML and optimization, including data quality issues, model generalization, and computational constraints. Key challenges include the integration of diverse datasets from electronic health records (EHRs), wearable devices, and imaging, and the need for models that generalize across populations. The chapter focuses the importance of interpretable models for clinicians and addresses ethical concerns. Emerging trends offer solutions, such as multimodal data integration, explainable AI, federated learning, hybrid models, and real-time edge computing. The chapter also highlights the focus on personalized healthcare and predictive modeling while emphasizing ethical, social, equity consideration. Chapter concludes with future directions include innovations in model architecture, quantum computing, and interdisciplinary collaboration to ensure the responsible development of ML and optimization in healthcare.

Ähnliche Arbeiten