Arthroplasty continues to provide pain relief and improved function for many patients who have reached an end stage of the disease. As robotics and artificial intelligence (AI) are increasingly integrated into arthroplasty (TKR and Total Joint Replacements), they will be able to improve patient planning for surgery, intraoperative surgical conduct, and postoperative outcomes. This communication will summarize existing evidence, highlight specific applications in RA arthroplasty, note the limitations and pitfalls, and provide an outline for future research and clinical applications1–6. Regrowth of the secondary or staged procedure occurs in primary osteoarthritis. The demands of these anatomical and biological differences contribute to a greater degree of requirement for meticulous preoperative planning and intraoperative adaptability to achieve stable fixation, proper alignment, and soft tissue balance7–9. Classical two-dimensional templating and surgeon judgment remain adequate in the hands of many, but RA cases, particularly those with bony deformity, loss, and previous surgery, benefit from a higher level of imaging, 3D modeling, and surgical appliances, which allow for greater reproducibility. The incorporation of robotics and AI into this mix enables the careful three-dimensional modeling and simulation of various alignment strategies to identify the optimal one, as well as intraoperative navigation that accommodates the complex anatomy and soft tissue constraints9–13. Commercial robotic systems used in knee and hip arthroplasty have been divided into three general classifications: (1) imaging-based, CT/3D planning platforms with haptic or active robotic-assisted capabilities (e.g., MAKO), (2) imageless, registration-based robotic arms (e.g., ROSA, NAVIO variants), and (3) handheld or semi-autonomous cutting instruments and navigation hybrids2,4,10–12. Across multiple comparative studies and meta-analyses, robotic-assisted approaches increase the accuracy with which components are positioned and eliminate outliers from the alignment distribution compared to conventional means of instrumentation, although improvements in long-term clinical outcomes are still being elucidated2,6,14. For cases of RA, where the bony stock may be unpredictable, and deformities can be significant, this capability of allowing previsualization of bony resection, fitting trial components, and simulating gaps is of great value15. The use of AI in Preoperative Planning could be very useful. Imaging, Segmentation, and 3D Templating Application of AI methods, chiefly convolutional neural networks (CNN), and deep learning (DL) pipelines, have allowed for prosthetic joint anatomy to be automatically segmented from CT or MRI images, accurate 3D models to be reconstructed, and the sizes and positions of the components involved recommended with great fidelity8,9,16,17. The accuracy of AI-associated 3D templating surpasses both manual templating and 2D templating models in predicting implant size and alignment, and also enables surgeons to rapidly run virtual scenarios (e.g., different component positions, bone cuts, and offsets)8,16. These features are particularly important in patients with RA, as they often exhibit bony erosions, cystic defects, and variable bony quality during templating. AI can facilitate the detection of bony defects, quantify bone loss, and provide suggestions for augmentation techniques or constrained implants using decision rules trained on large datasets9,16,18. Many multicenter studies have demonstrated that AI models achieve an accuracy of over 90% for component sizing and exhibit less discrepancy in final intraoperative decision-making compared to conventional methods8. Modern robotic platforms utilize measurements of intraoperative ligamentous laxity, dynamic gap balancing algorithms, and, when used synergistically with AI-predictive modeling of soft tissue response, can suggest targeted bone cuts and implant positions to optimize function while minimizing soft tissue releases10,11,17. New AI algorithms in development will predict post-operative gaps with varying alignment profiles and suggest real-time management. Early clinical work suggests that robotic guidance to perform the AI-optimized process will enable more reproducible targeting of the gap and also reduce the need for extensive soft-tissue releases in challenging cases10. This synergism is especially useful in RA, where the variability in balancing techniques between surgeons may otherwise lead to variations in outcomes. The role of artificial intelligence in arthroplasty extends beyond anatomic planning. Machine learning (ML) models, which have been trained on large clinical registries or administrative datasets, have been shown to provide predictions of perioperative risks (e.g., readmission, infection, blood transfusion, prosthetic failure) and functional outcomes13,19,20. In patients with RA, whose comorbidity profiles and immunomodulatory therapies may alter the postoperative risk profile, tools such as AI risk calculators can aid in shared decision-making, perioperative optimization, and postoperative surveillance. Studies comparing traditional risk prediction to ML models have shown superior discrimination and calibration of ML systems for 30- and 90-day outcomes in joint arthroplasty cohorts19,21. Intraoperatively, robotic systems translate the preoperative plan into constrained motions of instruments, actual cutting of bone, or haptic feedback that enforces the planned resection margins. AI algorithms that are fed intraoperatively captured sensor data (force, kinematics, and optical tracking) can provide real-time quality control, modify resection parameters, and note deviations from plan. While regulatory, ethical, and technical hurdles remain to fully autonomous arthroplasty, semi-autonomous workflows (surgeon-in-the-loop with robotic enforcement of safe zones) are now commonly found in major centers and clinics4,8,14. For arthroplasty in RA patients, this controlled autonomy leads to a lesser reliance on intraoperative judgment alone for anatomic abnormalities of joints, and better reproducibility of technically demanding reconstructions and augment placements15. Prospective validation of imaging segmentation, implant size selection, and risk stratification analysis across varied populations and imaging protocols is needed to confirm robustness8,16. There is a need for cost-effectiveness in pragmatic care pathways. Analyses that evaluate RA-specific revision risk, management of immunosuppression, and downstream health care utilization will better inform policy. Integration studies comparing AI-enhanced robotic planning to robotic planning alone, or to conventional planning, would remove the incremental value that AI could represent in the workflow9,14. Research into optimal learning, cognitive burden, and the interaction of surgeon and machine can reduce unintended harm during the learning curve11. While awaiting supervised clinical studies for higher-level evidence, clinicians contemplating the use of robotics and AI for RA arthroplasty planning may adopt a pragmatic approach; selecting cases where the benefit is most apparent should be considered. Complex deformities, excessive bone loss, prior hardware, or revision scenarios that predominate in RA are situations where patients’ specific 3D planning and robotic execution would confer maximum technical advantages15. Utilize validated AI modules and perform local audits on performance by utilizing tools that have been externally validated and implement local quality audits comparing pre-operative plans, intra-operative performance, and post-operative alignment9,16. Integrate AI risk stratification into perioperative decision-making for RA patients (e.g., timing of biological agents), combining technical planning with medical optimization13,19. Utilizing Document AI and involving patients in the consent process will be highly beneficial. Clearly outline the role of AI and robotics in planning, describe alternatives, and document the discussion in the consent process16. Robotics and AI are complementary technologies that, when combined, can enhance surgical precision, personalize pre-operative planning, and provide predictive insights into risks and outcomes. In the case of rheumatoid arthritis arthroplasty, where anatomy is often complex, and patient biology requires individualization, AI-assisted 3D templating and robotic performance have a considerable theoretical and early empirical basis. Nevertheless, the limitations of the evidence base, the possible bias of AI models, cost, and regulation mean that predicting broadly improved outcomes in the longer term is premature. High-quality comparative trials into RA cohorts, verifiable AI algorithms, and careful management within multi-disciplinary care pathways must be priorities. In conclusion, robotics + AI could become efficacious instruments through which we can deliver safer, more reproducible, and better patient-centered arthroplasty care to patients with RA18. We would also like to declare that we used the TITANS guidelines for this letter22. There is a need to further valuate and pragmaticaly aligned under big data analysis for the current use of robotics and AI in rheumatoid arthritis.
Htoo et al. (Fri,) studied this question.