About

Babar Kayani is a pioneering orthopaedic surgeon and researcher whose work has fundamentally advanced the field of robotic-assisted knee arthroplasty. With a research focus centered on total knee arthroplasty (TKA) and unicompartmental knee arthroplasty (UKA), Kayani has made landmark contributions to understanding how robotic technology improves surgical precision, patient outcomes, and postoperative recovery. His most cited work — a 2018 prospective cohort study with over 400 citations — demonstrated that robotic-arm assisted TKA yields superior early functional recovery and shorter hospital stays compared to conventional jig-based techniques. Complementing this, his investigations into surgical learning curves revealed that while workflow integration requires approximately seven cases, implant positioning accuracy is unaffected from the outset — a clinically significant finding that has encouraged broader adoption of robotic systems. Kayani has also made notable contributions to alignment philosophy, comparing functional and mechanical alignment strategies using sensor-guided and robotic technologies. Across his top ten papers alone, his work has accumulated over 2,000 citations, establishing him as one of the most influential voices in contemporary reconstructive knee surgery and robotic orthopaedics.

Research Focus

Key Achievements

25
H-Index
39
Papers
3,063
Total Citations
79
Avg Citations/Paper
🏆 Most Cited Paper
Robotic-arm assisted total knee arthroplasty is associated with improved early functional recovery and reduced time to hospital discharge compared with conventional jig-based total knee arthroplasty
407 citations · 2018
📈 Most Prolific Year: 2019 (9 Papers)
🤝 Key Collaborators: 54
🏛 Institutions: The Princess Grace Hospital, University College Hospital, Royal London Hospital, University College London Hospitals NHS Foundation Trust, University College London

Top Papers

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    Robotic total knee arthroplasty
    114 citations · 2019
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Key Collaborators

Contact & Links

Available for collaboration
Content generated · 14 days ago