Dr. Lincoln Liow is a Consultant Orthopaedic Surgeon at Singapore General Hospital and Clinical Associate Professor at Duke-NUS Medical School. The recipient of the prestigious Singapore Armed Forces Medicine Scholarship, he holds dual certification from the United States Educational Commission for Foreign Medical Graduates (ECFMG) and the Federation of State Medical Boards (FSMB).
Dr. Liow's international training is a cornerstone of his expertise. He was competitively selected for an Orthopaedic Biomechanics Research Fellowship at Massachusetts General Hospital (MGH), Harvard Medical School, Boston, USA, before completing a clinical fellowship in Complex Joint Reconstruction at the Hospital for Special Surgery (HSS), New York — consistently ranked the world's No. 1 institution for orthopaedic surgery.
A high-volume robotic arthroplasty surgeon, Dr. Liow performs more than 500 joint replacements annually, with specialized expertise in Enhanced Recovery After Surgery (ERAS) protocols and complex revision arthroplasty. Recognized internationally as a Key Opinion Leader (KOL) and Global Faculty, he regularly teaches robotic and revision arthroplasty techniques at local, regional, and international courses, and performs live surgical demonstrations for educational events and knowledge exchanges worldwide.
Dr. Liow is a distinguished clinician-academician with an H-index of 32, over 150 peer-reviewed publications, more than 3,000 citations, and over S$3 million in competitive grant funding as Principal or Co-Investigator. He serves as Research Director of SGH Orthopaedic Surgery and Deputy Vice-Chair (Medical Device Development) of the SingHealth Musculoskeletal Academic Clinical Programme (MSK ACP).
An accomplished clinician-innovator, Dr. Liow was honoured with the JCI Ten Outstanding Young Persons Merit Award for Medical Innovation and the National Medical Research Council (NMRC) Clinician Innovator Award. He is Co-Founder and Chief Medical Advisor of CartiFuse, an orthopaedic start-up translating his research into clinical solutions. His work has earned recognition at the highest international levels, including the American Academy of Orthopaedic Surgeons (AAOS) Best Poster Award, the Hospital for Special Surgery Annual Alumni Meeting Best Research Poster Award, the Jacques Duparc Best Poster Award from the European Federation of National Associations of Orthopaedics and Traumatology (EFORT), Top 100 Posters at the American Association of Hip and Knee Surgeons (AAHKS) Annual Meeting, and a nomination for the New Investigator Recognition Award (NIRA) at the Orthopaedic Research Society Annual Meeting.
Dr. Liow serves on the editorial boards of the Journal of Arthroplasty (the official journal of AAHKS) and Arthroplasty, and is an invited peer reviewer for numerous leading orthopaedic journals. He remains deeply committed to the education and mentorship of the next generation of orthopaedic surgeons.
Current
Past appointments
RESEARCH GRANTS
FY2020 Singhealth Duke-NUS Musculoskeletal Sciences Academic Clinical Programme Nurturing Clinician Scientist Scheme (NCSS) Research Support Grant – 13/FY2020/P1/18-A33
Quantum: S$225,000
RESEARCH AND CLINICAL TRIALS AS PRINCIPAL INVESTIGATOR (ONGOING)
CIRB Ref No. : 2020/2866
Protocol Title : A Propective Randomised, Controlled Clinical Trial to Compare the Functional Outcomes of Patients Undergoing Total Knee Replacement Using the Zimmer-Biomet Persona Total Knee System with Cruciate-Retaining or Medial Congruent Bearing
CIRB Ref No. : 2020/2750
Protocol Title : A Review of the Functional Outcomes and Quality of Life after Revision Total Hip Arthroplasty
CIRB Ref No. : 2020/2237
Protocol Title : A Review of the Functional Outcomes and Quality of Life after Revision Total Knee Arthroplasty
CIRB Ref No. : 2020/2316
Protocol Title : Cost-Effectiveness of Robot-Assisted Total Knee Arthroplasty: A Markov Decision Analysis
CIRB Ref No. : 2019/2878
Protocol Title : Enhancing appropriateness of surgical referral to improve accessibility and value in knee osteoarthritis: Development and validation of a deep-learning algorithm