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2 citation(s) found for "Xing, Lei".

Generative AI, foundation models and large language models in radiation therapy physics: Clinical applications, challenges, and future directions
Qi, X. Sharon, Wang, Yi, Yang, Xiaofeng, Ren, Lei , Liu, Wei, Benedict, Stanley , Xiao, Ying , Xing, Lei , Naqa, Issam M. El, Qi, X. Sharon , Qi, X. Sharon, Yang, Xiaofeng

Medical Physics | August 7, 2026 | Volume: 53 | Issue: 8 | Pages: No page available

10.1002/mp.70616 | PMID 42563397 Get It

Abstract Generative AI (Gen AI), Foundation Models (FMs), and Large Language Models (LLMs) are powerful emerging technologies that demonstrate exceptional capabilities in processing vast amounts of unstructured and structured data, including text, voice, images, video and other formats, and adapting to a wide range of specific tasks. Their immense potential to drive meaningful improvements in treatment outcomes is increasingly evident. The advent of these technologies has marked a transformative era in healthcare, including the fields of radiation oncology and medical physics. Specifically, these powerful technologies offer unprecedented opportunities to analyze domain‐specific data, process and synthesize medical images, automate routine tasks, support clinical decision‐making, optimize and streamline clinical workflows, and enhance the quality of clinical trials. While these emerging technologies present new opportunities to revolutionize radiation therapy practice, their implementation also raises important educational, ethical, and regulatory considerations. This scoping review highlights benefits, promises, risks, and challenges, such as interpretability, data privacy, regulatory compliance, reproducibility, hallucination, and integration into existing clinical workflow. Finally, emerging opportunities are outlined to guide future research directions. This review paper provides a timely overview of Gen AI, FMs and LLMs, aiming to inform medical physicists, clinicians, and researchers of the evolving role of these disruptive technologies in shaping the future of radiation therapy.
Intra‐Articular Purified Exosome Product and Hyaluronan Attenuate Osteoarthritis Progression in a Minimally Invasive Turkey Model
Lei, Xing , Wang, Lei, Reisdorf, Ramona L., Brosig, Maggie A, Moran, Steven L, Evans, CH, Zhao, Chunfeng, Brosig, Maggie, Moran, Steven L., Evans, Christopher H., Zhao, Chunfeng

Journal of Orthopaedic Research | April 29, 2026 | Volume: 44 | Issue: 5 | Pages: No page available

10.1002/jor.70207 | PMID 42051070 Get It

ABSTRACT Our study aimed to develop a novel, reproducible, minimally invasive turkey knee osteoarthritis (OA) model and evaluate whether intra‐articular hyaluronan (HA) alone or combined with purified exosome product (PEP) attenuates OA‐related functional and structural deterioration. OA was induced arthroscopically by transection of the anterior and posterior cruciate ligaments with resection of the anterior horn of the medial meniscus. Animals received intra‐articular injections of saline, HA, or PEP + HA at postoperative 2–5 weeks. Gait was assessed at 4, 8, and 12 weeks, and cartilage degeneration, subchondral bone sclerosis, osteophyte formation, synovial fibrosis, heterotopic ossification, and meniscal integrity were evaluated by quantitative CT and histology at week 12. Compared with saline, HA improved lameness, preserved cartilage glycosaminoglycan content, and reduced meniscal rupture, subchondral bone sclerosis, and periarticular heterotopic ossification. PEP + HA provided additional benefits, including prolonged stance phase, reduced synovial fibrosis, and greater chondroprotection than HA alone, while plantar pressure variables, subchondral bone remodeling, and patellar osteophytes did not differ between HA and PEP + HA. This turkey model reliably produces human‐like OA changes and enables longitudinal functional and structural assessment. Intra‐articular HA mitigated OA progression, and the addition of PEP conferred incremental joint protection.