Architecting the Orthopedical Clinical AI Pipeline: A Review of Integrating Foundation Models and FHIR for Agentic Clinical Assistants and Digital Twins
Boltaboyeva A. Baigarayeva Z. Imanbek B. Amangeldy B. Tasmurzayev N. Ozhikenov K. Alimbayeva Z. Alimbayev C. Karymsakova N.
February 2026Multidisciplinary Digital Publishing Institute (MDPI)
Algorithms
2026#19Issue 2
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.
AI assistant , clinical decision support systems , explainable AI , finetuning , large language models , medical AI , multimodal AI , NLP , RAG
Text of the article Перейти на текст статьи
Institute of Automation and Information Technology, Satbayev University, Almaty, 050013, Kazakhstan
Faculty of Information Technologies and Artificial Intelligence, Al Farabi Kazakh National University, Almaty, 050040, Kazakhstan
LLP “Kazakhstan RD Solutions”, Almaty, 050056, Kazakhstan
Department of Automation and Control, ALT University, Almaty, 050012, Kazakhstan
Institute of Automation and Information Technology
Faculty of Information Technologies and Artificial Intelligence
LLP “Kazakhstan RD Solutions”
Department of Automation and Control
10 лет помогаем публиковать статьи Международный издатель
Книга Публикация научной статьи Волощук 2026 Book Publication of a scientific article 2026