Minimally Invasive Procedures (MIPs) emerged as an alternative to more invasive surgical approaches, offering patient benefits such as smaller incisions, less pain, and shorter hospital stay. In one class of MIPs, where natural body lumens or small incisions are used to access deeper anatomical locations, Flexible Surgical and Interventional Robots (FSIRs) such as catheters and endoscopes are widely used. Due to their flexible and compliant nature, FSIRs can be inserted via natural orifices or small incisions, then moved towards hard-to-reach targets to perform interventional tasks. However, existing FSIRs are confronted with challenges in sensing, control, and navigation. These issues stem from the robot's non-linear behavior and the intricate nature of the lumens, where accurately modeling the complex interactions and disturbances proves to be exceptionally difficult. The rapid advances in Machine Learning (ML) have facilitated the widespread adoption of ML techniques in FSIRs. This article provides an overview of these efforts by first introducing a classification of existing ML algorithms, including traditional ML methods and modern Deep Learning (DL) approaches, commonly used in FSIRs. Next, the use of ML algorithms is surveyed per sub-domain, namely for perception, modeling, control, and navigation. Trends, popularity, strengths, and/or limitations of different ML algorithms are analyzed. The different roles that ML plays among tasks are investigated and described. Finally, discussions are conducted on the limitations and the prospects of ML in MIPs.

Increased demand for less invasive procedures has accelerated the adoption of Intraluminal Procedures (IP) and Endovascular Interventions (EI) performed through body lumens and vessels. As navigation through lumens and vessels is quite complex, interest grows to establish autonomous navigation techniques for IP and EI for reaching the target area. Current research efforts are directed toward increasing the Level of Autonomy (LoA) during the navigation phase. One key ingredient for autonomous navigation is Motion Planning (MP) techniques. This paper provides an overview of MP techniques categorizing them based on LoA. Our analysis investigates advances for the different clinical scenarios. Through a systematic literature analysis using the PRISMA method, the study summarizes relevant works and investigates the clinical aim, LoA, adopted MP techniques, and validation types. We identify the limitations of the corresponding MP methods and provide directions to improve the robustness of the algorithms in dynamic intraluminal environments. MP for IP and EI can be classified into four subgroups: node, sampling, optimization, and learning-based techniques, with a notable rise in learning-based approaches in recent years. One of the review's contributions is the identification of the limiting factors in IP and EI robotic systems hindering higher levels of autonomous navigation. In the future, navigation is bound to become more autonomous, placing the clinician in a supervisory position to improve control precision and reduce workload.