Knee osteoarthritis, the most prevalent degenerative joint disorder worldwide, is driven by chronic low‐grade inflammation and subsequent cartilage degradation. Clinical data on the role of the Hoffa or infrapatellar fat pad in knee osteoarthritis are, however, scarce. The infrapatellar fat pad is a richly innervated intracapsular, extrasynovial adipose tissue, and an abundant source of adipokines and proinflammatory and catabolic cytokines, which may contribute to chronic synovial inflammation, cartilage destruction, and subchondral bone remodeling during knee osteoarthritis. How the infrapatellar fat pad interacts with neighboring tissues is poorly understood. Here, we review available literature with regard to the infrapatellar fat pad's interactions with cartilage, synovium, bone, menisci, ligaments, and nervous tissue during the development and progression of knee osteoarthritis. Signaling cascades are described with a focus on immune cell populations, pro‐ and anti‐inflammatory cytokines, adipokines, mesenchymal stromal cells, and molecules derived from conditioned media from the infrapatellar fat pad. Understanding the complex interplay between the infrapatellar fat pad and its neighboring articular tissues may help to better understand and treat the multifactorial pathogenesis of osteoarthritis.

Prior to undergoing total knee arthroplasty (TKA), surgeons are often confronted with patients with numerous questions regarding the procedure and the recovery process. Due to limited staff resources and mounting individual workload, increased efficiency, e.g., using artificial intelligence (AI), is of increasing interest. We comprehensively evaluated ChatGPT’s orthopedic responses using the DISCERN instrument. Three independent orthopedic surgeons rated the responses across various criteria. We found consistently high scores, predominantly exceeding a score of three out of five in almost all categories, indicative of the quality and accuracy of the information provided. Notably, the AI demonstrated proficiency in conveying precise and reliable information on orthopedic topics. However, a notable observation pertains to the generation of non-existing references for certain claims. This study underscores the significance of critically evaluating references provided by ChatGPT and emphasizes the necessity of cross-referencing information from established sources. Overall, the findings contribute valuable insights into the performance of ChatGPT in delivering accurate orthopedic information for patients in clinical use while shedding light on areas warranting further refinement. Future iterations of natural language processing systems may be able to replace, in part or in entirety, the preoperative interactions, thereby optimizing the efficiency, accessibility, and standardization of patient communication.

Objective: This study aimed to evaluate the quality and readability of information generated by ChatGPT versions 3.5 and 4 concerning platelet-rich plasma (PRP) therapy in the management of knee osteoarthritis (OA), exploring whether large language models (LLMs) could play a significant role in patient education. Design: A total of 23 common patient queries regarding the role of PRP therapy in knee OA management were presented to ChatGPT versions 3.5 and 4. The quality of the responses was assessed using the DISCERN criteria, and readability was evaluated using six established assessment tools. Results: Both ChatGPT versions 3.5 and 4 produced moderate quality information. The quality of information provided by ChatGPT version 4 was significantly better than version 3.5, with mean DISCERN scores of 48.74 and 44.59, respectively. Both models scored highly with respect to response relevance and had a consistent emphasis on the importance of shared decision making. However, both versions produced content significantly above the recommended 8th grade reading level for patient education materials (PEMs), with mean reading grade levels (RGLs) of 17.18 for ChatGPT version 3.5 and 16.36 for ChatGPT version 4, indicating a potential barrier to their utility in patient education. Conclusions: While ChatGPT versions 3.5 and 4 both demonstrated the capability to generate information of moderate quality regarding the role of PRP therapy for knee OA, the readability of the content remains a significant barrier to widespread usage, exceeding the recommended reading levels for PEMs. Although ChatGPT version 4 showed improvements in quality and source citation, future iterations must focus on producing more accessible content to serve as a viable resource in patient education. Collaboration between healthcare providers, patient organizations, and AI developers is crucial to ensure the generation of high quality, peer reviewed, and easily understandable information that supports informed healthcare decisions.

Graef F, et al. Clin Orthop Surg. 2021 Jul;13:e68. https://doi.org/10.4055/cios20275