# Adverse Events in Robotic Surgery: A Retrospective Study of 14 Years of FDA Data

arXiv: https://arxiv.org/abs/1507.03518 · HTML: https://mlsanity.com/paper/1507.03518/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Jul 2015.

## Built on by (most cited first, 9 of 9)

- [Runtime Detection of Executional Errors in Robot-Assisted Surgery](https://mlsanity.com/paper/2203.00737/) (2022)
- [Think Step by Step: Chain-of-Gesture Prompting for Error Detection in Robotic Surgical Videos](https://mlsanity.com/paper/2406.19217/) (2024)
- [SEDMamba: Enhancing Selective State Space Modelling with Bottleneck Mechanism and Fine-to-Coarse Temporal Fusion for Efficient Error Detection in Robot-Assisted Surgery](https://mlsanity.com/paper/2406.15920/) (2024)
- [CARES: Collaborative Agentic Reasoning for Error Detection in Surgery](https://mlsanity.com/paper/2508.08764/) (2025)
- [RoboJailBench: Benchmarking Adversarial Attacks and Defenses in Embodied Robotic Agents](https://mlsanity.com/paper/2605.19328/) (2026)
- [Mapping Surgeon's Hand/Finger Motion During Conventional Microsurgery to Enhance Intuitive Surgical Robot Teleoperation](https://mlsanity.com/paper/2102.10585/) (2021)
- [Moral Decision-Making in Medical Hybrid Intelligent Systems: A Team Design Patterns Approach to the Bias Mitigation and Data Sharing Design Problems](https://mlsanity.com/paper/2102.11211/) (2021)
- [Systems-Theoretic and Data-Driven Security Analysis in ML-enabled Medical Devices](https://mlsanity.com/paper/2506.15028/) (2025)
- [Real-Time Multimodal Activity-Aware Error Detection in Robot-Assisted Surgery](https://mlsanity.com/paper/2606.23593/) (2026)

## Abstract (arXiv)

Understanding the causes and patient impacts of surgical adverse events will help improve systems and operational practices to avoid incidents in the future. We analyzed the adverse events data related to robotic systems and instruments used in minimally invasive surgery, reported to the U.S. FDA MAUDE database from January 2000 to December 2013. We determined the number of events reported per procedure and per surgical specialty, the most common types of device malfunctions and their impact on patients, and the causes for catastrophic events such as major complications, patient injuries, and deaths. During the study period, 144 deaths (1.4% of the 10,624 reports), 1,391 patient injuries (13.1%), and 8,061 device malfunctions (75.9%) were reported. The numbers of injury and death events per procedure have stayed relatively constant since 2007 (mean = 83.4, 95% CI, 74.2-92.7). Surgical specialties, for which robots are extensively used, such as gynecology and urology, had lower number of injuries, deaths, and conversions per procedure than more complex surgeries, such as cardiothoracic and head and neck (106.3 vs. 232.9, Risk Ratio = 2.2, 95% CI, 1.9-2.6). Device and instrument malfunctions, such as falling of burnt/broken pieces of instruments into the patient (14.7%), electrical arcing of instruments (10.5%), unintended operation of instruments (8.6%), system errors (5%), and video/imaging problems (2.6%), constituted a major part of the reports. Device malfunctions impacted patients in terms of injuries or procedure interruptions. In 1,104 (10.4%) of the events, the procedure was interrupted to restart the system (3.1%), to convert the procedure to non-robotic techniques (7.3%), or to reschedule it to a later time (2.5%). Adoption of advanced techniques in design and operation of robotic surgical systems may reduce these preventable incidents in the future.