March 3, 2026Open Access

Architecting the Orthopedical Clinical AI Pipeline: A Review of Integrating Foundation Models and FHIR for Agentic Clinical Assistants and Digital Twins

ABAssiya BoltaboyevaAl-Farabi Kazakh National University ZBZhanel BaigarayevaAl-Farabi Kazakh National University BIBaglan ImanbekAl-Farabi Kazakh National University

Key Points

The deployment of large language models in orthopedics faces significant challenges in achieving safe decision support.
Current methodologies emphasize the need for multimodal data fusion and standardized data interoperability pipelines to enhance AI effectiveness.
A structured synthesis reveals trade-offs between fine-tuning methods for model adaptation and their impact on computational costs.
Addressing challenges such as algorithmic bias and security is crucial for transitioning these AI technologies into scalable clinical applications.

Abstract

The exponential growth of multimodal orthopedic data, ranging from longitudinal Electronic Health Records to high-resolution musculoskeletal imaging, has rendered manual analysis insufficient. This has established Large Language Models (LLMs) as algorithmically necessary for managing healthcare complexity. However, their deployment in high-stakes surgical environments presents a fundamental algorithmic paradox: while generic foundation models possess vast reasoning capabilities, they often lack the precise, protocol-driven domain knowledge required for safe orthopedic decision support. This review provides a structured synthesis of the emerging algorithmic frameworks required to build modern clinical AI assistants. We deconstruct current methodologies into their core components: large-language-model adaptation, multimodal data fusion, and standardized data interoperability pipelines. Rather than proposing a single proprietary architecture, we analyze how recent literature connects specific algorithmic choices such as the trade-offs between full fine-tuning and Low-Rank Adaptation to their computational costs and factual reliability. Furthermore, we examine the theoretical architectures required for ‘agentic’ capabilities, where AI systems integrate outputs from deep convolutional neural networks and biosensors. The review concludes by outlining the unresolved challenges in algorithmic bias, security, and interoperability that must be addressed to transition these technologies from research prototypes to scalable clinical solutions.

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