Martin Reichert

Universitätsklinikum Gießen und Marburg

Papers

4

Total Citations

44

H-Index

4

About

Martin Reichert is a surgical researcher whose work sits at the forefront of robotic and minimally invasive surgery, with particular expertise in bariatric and colorectal procedures. His research has made meaningful contributions to understanding how robotic surgical platforms can be safely and effectively implemented in complex clinical settings. Reichert's most recognized work examines the learning curve associated with robotic Roux-en-Y gastric bypass at an academic tertiary center, a study that has garnered 16 citations and offers practical guidance for institutions adopting robotic bariatric programs. Building on this, his investigation into outcomes for super-obese patients with BMI ≥ 50 kg/m² explores whether robotic precision can mitigate traditionally elevated surgical risks in this challenging population. His contributions to colorectal surgery are equally notable, spanning learning-curve analyses for robotic right colectomy and a comprehensive review of current evidence in robotic colorectal surgery, which has already attracted 12 citations since its 2025 publication. Collectively, Reichert's body of work addresses a critical translational question in modern surgery: how emerging robotic technologies can be responsibly integrated into routine clinical practice. His research provides surgeons and institutions with evidence-based frameworks for adoption, safety benchmarking, and patient selection across high-complexity operative domains.

Research Focus

Key Achievements

4
H-Index
4
Papers
44
Total Citations
11
Avg Citations/Paper
🏆 Most Cited Paper
Establishing robotic bariatric surgery at an academic tertiary hospital: a learning curve analysis for totally robotic Roux-en-Y gastric bypass
16 citations · 2022
📈 Most Prolific Year: 2022 (2 Papers)
🤝 Key Collaborators: 18
🏛 Institutions: Universitätsklinikum Gießen und Marburg

Top Papers

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Key Collaborators

Contact & Links

Available for collaboration
Content generated · 14 days ago