  Purpose: Autonomous navigation of devices in endovascular interventions can
decrease operation times, improve decision-making during surgery, and reduce
operator radiation exposure while increasing access to treatment. This
systematic review explores recent literature to assess the impact, challenges,
and opportunities artificial intelligence (AI) has for the autonomous
endovascular intervention navigation.
  Methods: PubMed and IEEEXplore databases were queried. Eligibility criteria
included studies investigating the use of AI in enabling the autonomous
navigation of catheters/guidewires in endovascular interventions. Following
PRISMA, articles were assessed using QUADAS-2. PROSPERO: CRD42023392259.
  Results: Among 462 studies, fourteen met inclusion criteria. Reinforcement
learning (9/14, 64%) and learning from demonstration (7/14, 50%) were used as
data-driven models for autonomous navigation. Studies predominantly utilised
physical phantoms (10/14, 71%) and in silico (4/14, 29%) models. Experiments
within or around the blood vessels of the heart were reported by the majority
of studies (10/14, 71%), while simple non-anatomical vessel platforms were used
in three studies (3/14, 21%), and the porcine liver venous system in one study.
We observed that risk of bias and poor generalisability were present across
studies. No procedures were performed on patients in any of the studies
reviewed. Studies lacked patient selection criteria, reference standards, and
reproducibility, resulting in low clinical evidence levels.
  Conclusions: AI's potential in autonomous endovascular navigation is
promising, but in an experimental proof-of-concept stage, with a technology
readiness level of 3. We highlight that reference standards with
well-identified performance metrics are crucial to allow for comparisons of
data-driven algorithms proposed in the years to come.
