Reproducibility challenges in robotic surgery

Abstract

Reproducibility of results is, in all research fields, the cornerstone of the scientific method and the minimum standard for assessing the value of scientific claims and conclusions drawn by other scientists. It requires a systematic approach and accurate description of the experimental procedure and data analysis, which allows other scientists to follow the steps described in the published work and obtain the “same results.” In general and in different research contexts with “same” results, we mean different things. It can be almost identical measures in a fully deterministic experiment or “validation of a hypothesis” or statistically similar results in a non-deterministic context. Unfortunately, it has been shown by systematic meta-analysis studies that many findings in fields like psychology, sociology, medicine, and economics do not hold up when other researchers try to replicate them. Many scientific fields are experiencing what is generally referred to as a “reproducibility crisis,” which undermines the trust in published results, imposes a thorough revision of the methodology in scientific research, and makes progress difficult. In general, the reproducibility of experiments is not a mainstream practice in artificial intelligence and robotics research. Surgical robotics is no exception. There is a need for developing new tools and putting in place a community effort to allow the transition to more reproducible research and hence faster progress in research. Reproducibility, replicability, and benchmarking (operational procedures for the assessment and comparison of research results) are made more complex for medical robotics and surgical systems, due to patenting, safety, and ethical issues. In this review paper, we selected 10 relevant published manuscripts on surgical robotics to analyze their clinical applicability and underline the problems related to reproducibility of the reported experiments, with the aim of finding possible solutions to the challenges that limit the translation of many scientific research studies into real-world applications and slow down research progress.

Evolution of Surgical Robot Systems Enhanced by Artificial Intelligence: A Review

“…In medical research, including the SRS domain, empirical evaluations within clinical environments or environments closely resembling clinical settings are needed. [169,170] Clinical evaluations of AI applications in SRS are nascent, with only a few clinical studies involving human subjects. In most studies, the evaluated outcomes primarily concentrated on the performance of the AI component within AI-aided SRS.…”