MLOps in Healthcare: Challenges and Innovations in Deploying AI Model

N. Kannan

Authors

N. Kannan India Author

Keywords:

AI, Healthcare, MLOps, Data Privacy, Explainable AI, Resource Optimization, Patient Care

Abstract

Examining the integration of AI models into healthcare, this exploration delves into challenges and innovations through the lens of MLOps (Machine Learning Operations). The multifaceted challenges include issues related to data privacy, regulatory compliance, model interpretability, and ethical considerations. In response, MLOps innovations have emerged to streamline development, deployment, and maintenance processes. Techniques such as data preprocessing, model validation, and continuous monitoring ensure reliability in clinical settings. Advancements in explainable AI enhance transparency and trust. Real-world case studies underscore MLOps' transformative potential in optimizing resource utilization, reducing time-to-deployment, and improving patient care. As AI's role in healthcare expands, insights for researchers, practitioners, and policymakers are provided to navigate challenges and maximize AI's impact on healthcare delivery.

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