The remarkable progress in data aggregation and deep learning algorithms has positioned artificial intelligence (AI) and machine learning (ML) to revolutionize the field of medicine. AI is becoming more and more prevalent in the healthcare sector, and its impact on orthopedic surgery is already evident in several fields. This review aims to examine the literature that explores the comprehensive clinical relevance of AI-based tools utilized before, during, and after anterior cruciate ligament (ACL) reconstruction. The review focuses on current clinical applications and future prospects in preoperative management, encompassing risk prediction and diagnostics; intraoperative tools, specifically navigation, identifying complex anatomic landmarks during surgery; and postoperative applications in terms of postoperative care and rehabilitation. Additionally, AI tools in educational and training settings are presented. Orthopedic surgeons are showing a growing interest in AI, as evidenced by the applications discussed in this review, particularly those related to ACL injury. The exponential increase in studies on AI tools applicable to the management of ACL tears promises a significant future impact in its clinical application, with growing attention from orthopedic surgeons.

Total hip arthroplasty (THA) is a widely performed surgical procedure that has evolved significantly due to advancements in artificial intelligence (AI) and robotics. As demand for THA grows, reliable tools are essential to enhance diagnosis, preoperative planning, surgical precision, and postoperative rehabilitation. AI applications in orthopedic surgery offer innovative solutions, including automated hip osteoarthritis (OA) diagnosis, precise implant positioning, and personalized risk stratification, thereby improving patient outcomes. Deep learning models have transformed OA severity grading and implant identification by automating traditionally manual processes with high accuracy. Additionally, AI-powered systems optimize preoperative planning by predicting the hip joint center and identifying complications using multimodal data. Robotic-assisted THA enhances surgical precision with real-time feedback, reducing complications such as dislocations and leg length discrepancies while accelerating recovery. Despite these advancements, barriers such as cost, accessibility, and the steep learning curve for surgeons hinder widespread adoption. Postoperative rehabilitation benefits from technologies like virtual and augmented reality and telemedicine, which enhance patient engagement and adherence. However, limitations, particularly among elderly populations with lower adaptability to technology, underscore the need for user-friendly platforms. To ensure comprehensiveness, a structured literature search was conducted using PubMed, Scopus, and Web of Science. Keywords included “artificial intelligence”, “machine learning”, “robotics”, and “total hip arthroplasty”. Inclusion criteria emphasized peer-reviewed studies published in English within the last decade focusing on technological advancements and clinical outcomes. This review evaluates AI and robotics’ role in THA, highlighting opportunities and challenges and emphasizing further research and real-world validation to integrate these technologies into clinical practice effectively.

Purpose: Robotic‐assisted total knee arthroplasty (TKA) and new alignment principles are transforming traditional implant positioning, not only in the coronal plane but also in the sagittal and axial planes. The impact of differences between a patient's native tibial slope (TS) and distal femoral flexion (DFF) compared to the final implant positioning remains unclear. This study aims to evaluate whether variations in TS (ΔTS) and DFF (ΔDFF) play a role in clinical outcomes and implant survival.Methods: This retrospective study analysed patients who underwent robotic‐assisted TKA following functional alignment (FA) principles between March 2021 and January 2023. A total of 310 patients were included. Preoperative and postoperative data, including radiographic and robotic measurements, were collected. Clinical outcomes (KSS, FJS‐12 and AKPS), range of motion (ROM), complication rates, and implant survival were compared between groups at a minimum of 2 years follow‐up.Results: At the final follow‐up (mean 2.93 ± 0.62 years), no significant differences were found in clinical scores or ROM between groups with different ΔTS or ΔDFF values. Complication rates and implant survival (99%) were also similar. However, patients with ΔTS > 5° had a slightly increased femoral valgus alignment, while those with ΔDFF ≤ 5° had lower preoperative ROM, with the femoral implant positioned in varus.Conclusions: Variations between native and implant TS and DFF in robotic‐assisted TKA do not negatively impact functional outcomes or implant survival. A personalised sagittal alignment approach within the concept of FA represents a progression to a functional knee positioning based on three dimensions. Further research is needed to explore the long‐term effects of sagittal alignment on TKA performance.Level of Evidence: Level III.

(1) Background: Mobile health (mHealth) solutions can become a means of improving functional recovery and reducing the peri-operative burden and costs associated with arthroplasty procedures. The aim of this study is to explore the objectives, functionalities, and outcomes of a platform designed to provide personalized surgical experiences to qualified patients, along with the associated problems and opportunities. (2) Methods: A survey-based analysis was conducted on patients who were prescribed the use of a specific care management platform and underwent primary robotic total knee arthroplasty (rTKA) between January 2021 and February 2023. (3) Results: Patients registered on the platform who have undergone primary robotic TKA (rTKA) were considered. The mean age of registered patients is 68.6 years. The male (M)/female (F) ratio is 45.1%/54.9%. The patients interviewed were at an average distance of 485 days from the intervention, with a standard deviation of 187.5. The survey highlighted appreciation for the app and its features, but also limitations in its use and in its perception by the patients. All these data were evaluated according to the Wald principles and strategies to improve patient recruitment, enhance adherence, and create a comprehensive patient journey for optimized surgical experiences. (4) Conclusions: This patient care platform may have the potential to impact surgical experiences by increasing patient engagement, facilitating remote monitoring, and providing personalized care. There is a need to emphasize the importance of integrating the recruiting process, improving adherence strategies, and creating a comprehensive patient journey within the platform.

Purpose: Personalized alignment in total knee arthroplasty (TKA) is becoming increasingly widespread, driven in part by advancements in robotic‐assisted surgery. However, true personalization must extend beyond the coronal plane to include sagittal and axial planes. This study investigates the impact of combined flexion (CF) of the femoral and tibial components in robotic‐assisted TKA within functional alignment (FA), also analyzing its variation (ΔCF) from native anatomy and its correlation with functional outcomes and complications.Methods: A retrospective analysis was conducted on 310 patients who underwent primary TKA using an image‐based robotic system between March 2021 and January 2023. CF was calculated based on tibial slope (TS) and distal femoral flexion (DFF). Patients were stratified into groups based on CF (≤7.5° vs. >7.5°) and CF variation (ΔCF < −3, −3 to 3 and >3). Clinical scores, radiographic parameters and complication rates were analyzed.Results: Patients with CF ≤ 7.5° had lower preoperative maximum flexion values (p = 0.005). No significant differences in complication rates or clinical outcomes were observed between groups. ΔCF were associated with post‐operative coronal alignment changes (mechanical hip–knee–ankle angle and medial proximal tibial angle) but did not impact patient‐reported outcomes. Additionally, subgroup analysis revealed that ΔCF were not linked to differences in implant survival, revision rates or mechanical failure.Conclusions: Although CF influences knee biomechanics, its direct impact on clinical outcomes remains unclear. It is evident that a personalized approach to sagittal alignment can be an integral component of functional knee positioning.Level of Evidence: Level III.

Purpose: Alignment strategies in total knee arthroplasty (TKA) have predominantly emphasized coronal alignment in extension, with minimal focus on dynamic alignment during flexion. This study aims to identify the predictors of the intraoperative robotic hip–knee–ankle angle at 90° of flexion (rHKA‐90F) and assess its clinical significance in postoperative outcomes, proposing that rHKA‐90F may be a critical factor in enhancing functional results in TKA.Methods: A retrospective analysis was conducted on 180 patients with varus deformity undergoing robotic‐assisted TKA under the functional alignment principles. Clinical outcomes were assessed using the Knee Society Score (KSS), Forgotten Joint Score (FJS‐12) and Kujala Anterior Knee Pain Scale (AKPS) score. Predictors for final rHKA‐90F and its intraoperative changes were identified using multiple linear regression models. Initial and intraoperative robotic measurements were also analyzed.Results: Significant predictors of the final rHKA‐90F included femoral rotation, tibial varus/valgus alignment, initial rHKA‐90F and the final robotic axis in extensions. Patients with a final rHKA‐90F ≥ 5° of varus demonstrated superior KSS function and KSS knee compared to those with a final rHKA‐90F between 0° and 4° of varus. Furthermore, patients with intraoperative changes of rHKA‐90F > 2.5° neutralization (varus reduction or with a valgus value) achieved better FJS and AKPS score.Conclusions: This study highlights the clinical relevance of rHKA‐90F as an intraoperative tool in robotic knee arthroplasty, emphasizing the need to balance the correction of varus deformity with the maintenance of slight varus alignment in flexion. Personalized alignment strategies tailored to patient‐specific anatomy and kinematics are crucial to optimizing outcomes. There is still a need for future research on the long‐term effects of dynamic alignment.Level of Evidence: Level III.