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Operationalizing the BSM into research and clinical care is challenging because most investigators work in silos that concentrate on only one or two BSM domains. Furthermore, the expanding, multidisciplinary nature of BSM research creates practical limitations as to how individual investigators integrate current data into their processes of generating impactful hypotheses. The rapidly advancing field of artificial intelligence (AI) is providing new tools for organizing knowledge, but the practical aspects for how AI may advance LBP research and clinical are beginning to be explored. The goals of the work presented here are to: (1) explore the current capabilities of knowledge integration technologies (large language models (LLM), similarity graphs (SGs), and knowledge graphs (KGs)) to synthesize biomedical literature and depict multimodal relationships reflected in the BSM, and; (2) highlight limitations, implementation details, and future areas of research to improve performance. We demonstrate preliminary evidence that LLMs, like GPT\u20103, may be useful in helping scientists analyze and distinguish cLBP publications across multiple BSM domains and determine the degree to which the literature supports or contradicts emergent hypotheses. We show that SG representations and KGs enable exploring LBP's literature in novel ways, possibly providing, trans\u2010disciplinary perspectives or insights that are currently difficult, if not infeasible to achieve. The SG approach is automated, simple, and inexpensive to execute, and thereby may be useful for early\u2010phase literature and narrative explorations beyond one's areas of expertise. Likewise, we show that KGs can be constructed using automated pipelines, queried to provide semantic information, and analyzed to explore trans\u2010domain linkages. The examples presented support the feasibility for LBP\u2010tailored AI protocols to organize knowledge and support developing and refining trans\u2010domain hypotheses.<\/jats:p>","DOI":"10.1002\/jsp2.1300","type":"journal-article","created":{"date-parts":[[2023,11,29]],"date-time":"2023-11-29T19:39:46Z","timestamp":1701286786000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":14,"title":["An exploration of knowledge\u2010organizing technologies to advance transdisciplinary back pain research"],"prefix":"10.1002","volume":"6","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9654-0647","authenticated-orcid":false,"given":"Jeffrey C.","family":"Lotz","sequence":"first","affiliation":[{"name":"Department of Orthopaedic Surgery University of California at San Francisco  San Francisco California USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Glen","family":"Ropella","sequence":"additional","affiliation":[{"name":"Tempus Dictum, Inc  Milwaukie Oregon USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Paul","family":"Anderson","sequence":"additional","affiliation":[{"name":"Department of Computer Science &amp; Software Engineering California Polytechnic State University  San Luis Obispo California USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qian","family":"Yang","sequence":"additional","affiliation":[{"name":"Department of Information Science Cornell University  Ithaca New York USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Michael A.","family":"Hedderich","sequence":"additional","affiliation":[{"name":"Department of Information Science Cornell University  Ithaca New York USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jeannie","family":"Bailey","sequence":"additional","affiliation":[{"name":"Department of Orthopaedic Surgery University of California at San Francisco  San Francisco California USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9372-6860","authenticated-orcid":false,"given":"C. 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