{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,27]],"date-time":"2026-05-27T17:40:04Z","timestamp":1779903604962,"version":"3.53.1"},"reference-count":135,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2026,1,27]],"date-time":"2026-01-27T00:00:00Z","timestamp":1769472000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Ministry of Science and Higher Education of the Republic of Kazakhstan","award":["BR24992820"],"award-info":[{"award-number":["BR24992820"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Algorithms"],"abstract":"<jats:p>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 \u2018agentic\u2019 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.<\/jats:p>","DOI":"10.3390\/a19020099","type":"journal-article","created":{"date-parts":[[2026,1,27]],"date-time":"2026-01-27T09:21:40Z","timestamp":1769505700000},"page":"99","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Architecting the Orthopedical Clinical AI Pipeline: A Review of Integrating Foundation Models and FHIR for Agentic Clinical Assistants and Digital Twins"],"prefix":"10.3390","volume":"19","author":[{"given":"Assiya","family":"Boltaboyeva","sequence":"first","affiliation":[{"name":"Institute of Automation and Information Technology, Satbayev University, Almaty 050013, Kazakhstan"},{"name":"Faculty of Information Technologies and Artificial Intelligence, Al Farabi Kazakh National University, Almaty 050040, Kazakhstan"},{"name":"LLP \u201cKazakhstan R&D Solutions\u201d, Almaty 050056, Kazakhstan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1919-3570","authenticated-orcid":false,"given":"Zhanel","family":"Baigarayeva","sequence":"additional","affiliation":[{"name":"Institute of Automation and Information Technology, Satbayev University, Almaty 050013, Kazakhstan"},{"name":"Faculty of Information Technologies and Artificial Intelligence, Al Farabi Kazakh National University, Almaty 050040, Kazakhstan"},{"name":"LLP \u201cKazakhstan R&D Solutions\u201d, Almaty 050056, Kazakhstan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Baglan","family":"Imanbek","sequence":"additional","affiliation":[{"name":"Faculty of Information Technologies and Artificial Intelligence, Al Farabi Kazakh National University, Almaty 050040, Kazakhstan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4089-6337","authenticated-orcid":false,"given":"Bibars","family":"Amangeldy","sequence":"additional","affiliation":[{"name":"Faculty of Information Technologies and Artificial Intelligence, Al Farabi Kazakh National University, Almaty 050040, Kazakhstan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3039-6715","authenticated-orcid":false,"given":"Nurdaulet","family":"Tasmurzayev","sequence":"additional","affiliation":[{"name":"Faculty of Information Technologies and Artificial Intelligence, Al Farabi Kazakh National University, Almaty 050040, Kazakhstan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kassymbek","family":"Ozhikenov","sequence":"additional","affiliation":[{"name":"Faculty of Information Technologies and Artificial Intelligence, Al Farabi Kazakh National University, Almaty 050040, Kazakhstan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2628-2515","authenticated-orcid":false,"given":"Zhadyra","family":"Alimbayeva","sequence":"additional","affiliation":[{"name":"Institute of Automation and Information Technology, Satbayev University, Almaty 050013, Kazakhstan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chingiz","family":"Alimbayev","sequence":"additional","affiliation":[{"name":"Institute of Automation and Information Technology, Satbayev University, Almaty 050013, Kazakhstan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8187-2369","authenticated-orcid":false,"given":"Nurgul","family":"Karymsakova","sequence":"additional","affiliation":[{"name":"Department of Automation and Control, ALT University, Almaty 050012, Kazakhstan"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,1,27]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"3454","DOI":"10.1016\/j.csbj.2024.09.010","article-title":"Confronting the data deluge: How artificial intelligence can be used in the study of plant stress","volume":"23","author":"Koh","year":"2024","journal-title":"Comput. 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