The revolutionary impact of artificial intelligence in orthopedics: comprehensive review of current benefits and challenges.

Artificial intelligence (AI) is changing orthopedics through improving accuracy before, during, and after surgery. This investigation is a comprehensive review on the use of AI in orthopedics, its advantages, and challenges. Preoperatively, AI imaging shows outstanding fracture detection (up to 98% accuracy), sports injury detection (e.g., ACL tear diagnosis with AUC 0.89-0.98), and tumor recognition, cutting diagnostic delays and human error. 3D AI planning in joint replacements augments surgical precision by 45%, while robotic navigation improves alignment and decreases revision rates. Intraoperatively, AI-driven robotic platforms (e.g., MAKO and ROSA) enable better bone resection and component positioning. The robots can be allowed to provide tactile feedback to the surgeon. Virtual and augmented reality advances surgical training by enhancing skill acquisition (Campa et al. in J Orthop Surg Res. 18:729, 2023). Postoperatively, AI allows personalized rehabilitation with sensors and predictive analytics, while remote monitoring tools can boost patient engagement. Progress has not wiped out all challenges. There are technical barriers to be concerned about including data dependence, algorithmic bias, and heterogeneous datasets. Ethical issues, such as the protection of private data, liability for AI errors, and informed consent, require appropriate governance structures. Clinical application is limited by cost, complexity, and resistance to standardized protocols that may not coincide with the preferences of surgeons. Solutions suggest AI hybrid models that include patient-specific anatomical data and impartial audits, as well as financing mechanisms. In conclusion, AI has enormous prospects to enhance orthopedic care by making it more precise, informed, and effective. However, it is necessary to address several technical, ethical, and adoption barriers to fully implement AI. Future directions include the focus on transparent, interpretable AI, multimodal data integration, and frameworks for innovation that balance clinician wisdom and patient-centric values.