AI-Powered Surgical Optimization Could Save Billions, Transform Care for Millions

A new peer-reviewed publication released today in Cureus delivers a powerful message: artificial intelligence (AI) holds the key to transforming the efficiency and effectiveness of surgical care across global health systems. The systematic review, titled "Natural Language Processing (NLP)- and Machine Learning (ML)-Enabled Operating Room Optimization: A Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) Systematic Review Anchored in Project Planning Theory," was authored by Balaiah Chamarthi, Omkar Reddy Polu, Sathish Krishna Anumula, Azhar Ushmani, Pratik Kasralikar, and Abdul Aleem Syed.

By applying the PRISMA methodology, the authors meticulously synthesized findings from a wide range of studies involving Natural Language Processing (NLP) and Machine Learning (ML) applications in the context of surgical scheduling, operating room (OR) resource use, and perioperative workflow optimization. The paper concludes that AI-driven solutions can significantly enhance hospital efficiency, reduce medical delays, and improve overall patient outcomes-particularly in high-volume surgical environments. The review estimates that full-scale implementation of such technologies could positively impact over 50 million surgical patients each year while reducing national healthcare expenditure in the United States by more than $15 billion annually through minimized cancellations, improved staffing strategies, and predictive resource allocation.

"This paper offers far more than a technical summary-it provides a strategic, evidence-based blueprint for using AI to solve one of healthcare's most pressing operational challenges," said Lukas Meier, Senior Journalist at Alpine Vision Media. "The authors' integration of project planning theory bridges the gap between innovation and application, ensuring their recommendations are both scalable and sustainable." The review's interdisciplinary strength lies in its alignment of AI innovation with real-world hospital management frameworks. Rather than discussing artificial intelligence in isolation, the authors explore how these technologies can be implemented using structured project planning principles to ensure successful deployment across health systems of varying size, funding, and complexity.

The publication has also drawn the attention of prominent global health experts. Dr. Nurhayati Hassan of Cyberjaya University Medical Centre in Malaysia and Prof. Tawanda Chikowore of the National University of Science and Technology in Zimbabwe-both known for their work in digital health innovation-have previously emphasized the urgent need for adaptable and affordable technologies in developing nations. This review supports their perspective by emphasizing how NLP and ML can be leveraged in cost-sensitive and resource-constrained settings to improve surgical access and efficiency.

The study contributed a valuable strategic dimension to the work, helping anchor the technological analysis within the framework of project planning theory. His efforts underscore a broader movement in the AI and healthcare community-one focused on building not just smart systems, but practical and implementable solutions. "As we emerge from a pandemic that strained surgical services globally, this research is a clarion call to embrace intelligent systems that can predict, plan, and perform more efficiently," Meier added. "It's forward-looking, yet grounded in data and operational logic."

This review represents a crucial step toward modernizing surgical infrastructure through AI-an evolution that holds immense promise not only for industrialized health systems, but also for healthcare providers around the world striving for efficiency, equity, and excellence.

Citation: Chamarthi B, Polu O, Anumula S, et al. (April 22, 2025) Natural Language Processing (NLP)- and Machine Learning (ML)-Enabled Operating Room Optimization: A Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) Systematic Review Anchored in Project Planning Theory. Cureus 17(4): e82796. DOI 10.7759/cureus.82796

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