Artificial intelligence (AI) is revolutionizing nearly every aspect of modern life. In the medical field, robotic surgery is the sector with some of the most innovative and impactful advancements. In this narrative review, we outline recent contributions of AI to the field of robotic surgery with a particular focus on intraoperative enhancement. AI modeling is allowing surgeons to have advanced intraoperative metrics such as force and tactile measurements, enhanced detection of positive surgical margins, and even allowing for the complete automation of certain steps in surgical procedures. AI is also Query revolutionizing the field of surgical education. AI modeling applied to intraoperative surgical video feeds and instrument kinematics data is allowing for the generation of automated skills assessments. AI also shows promise for the generation and delivery of highly specialized intraoperative surgical feedback for training surgeons. Although the adoption and integration of AI show promise in robotic surgery, it raises important, complex ethical questions. Frameworks for thinking through ethical dilemmas raised by AI are outlined in this review. AI enhancements in robotic surgery is some of the most groundbreaking research happening today, and the studies outlined in this review represent some of the most exciting innovations in recent years. 

Clinical applications of artificial intelligence in robotic surgery

As computer vision algorithms increase in capability, their applications in clinical systems will become more pervasive. These applications include: diagnostics, such as colonoscopy and bronchoscopy; guiding biopsies, minimally invasive interventions, and surgery; automating instrument motion; and providing image guidance using pre-operative scans. Many of these applications depend on the specific visual nature of medical scenes and require designing algorithms to perform in this environment. In this review, we provide an update to the field of camera-based tracking and scene mapping in surgery and diagnostics in medical computer vision. We begin with describing our review process, which results in a final list of 515 papers that we cover. We then give a high-level summary of the state of the art and provide relevant background for those who need tracking and mapping for their clinical applications. After which, we review datasets provided in the field and the clinical needs that motivate their design. Then, we delve into the algorithmic side, and summarize recent developments. This summary should be especially useful for algorithm designers and to those looking to understand the capability of off-the-shelf methods. We maintain focus on algorithms for deformable environments while also reviewing the essential building blocks in rigid tracking and mapping since there is a large amount of crossover in methods. With the field summarized, we discuss the current state of the tracking and mapping methods along with needs for future algorithms, needs for quantification, and the viability of clinical applications. We then provide some research directions and questions. We conclude that new methods need to be designed or combined to support clinical applications in deformable environments, and more focus needs to be put into collecting datasets for training and evaluation. 

Tracking and mapping in medical computer vision: A review

Human activity recognition (HAR) has become a hotspot. However, the existing HAR has some shortcomings, such as few recognized human activities, no identification, privacy leakage, and battery maintenance. Therefore, we design body RFID skeleton which fully senses the key features of human activity, and further propose human activity recognitions by taking advantage of RFID (privacy protection, identification, and battery-less maintenance). First, we devise RFID skeleton activity graph as human activity model. Second, we propose baseline RFID skeleton activity graph convolution network (FSCN) by using the graph convolution network (GCN), and FSCN classifies the RFID skeleton activity graph for human activity recognition. Third, to solve the tag response signal feature data over-smoothing and the different information aggregation degree in FSCN, we propose improved FSCN with the residual network (R-FSCN). Finally, we design the parallel RFID skeleton activity graph convolution network (PR-FSCN) for optimizing R-FSCN. Massive experiments show that PR-FSCN has comprehensive superiority to the existing HARs. As far as we know, this study is the first work that appropriately designs body RFID skeleton for HAR, and that successfully introduces GCN to investigate the new bound-RFID HAR with high recognition performance.

Body RFID Skeleton-Based Human Activity Recognition Using Graph Convolution Neural Network

Surgical Simulation: Virtual Reality to Artificial Intelligence

With the compelling popularity of integrated sensing and communication (ISAC), WiFi sensing has drawn increasing attention in recent years. Starting from 2010, WiFi CSI-based wireless sensing has enabled various exciting applications such as indoor localization, target imaging, activity recognition and vital sign monitoring. In this paper, we retrospect the up-to-date achievements of WiFi sensing using commodity-off-the-shelf (COTS) devices over the past 5 years in detail. Specifically, this paper first presents the background of CSI signal and related sensing models. Then, recent researches are categorized from two perspectives, namely according to their application scenario diversity and corresponding sensing methodology difference respectively. Next, this paper points out the challenges faced by WiFi sensing including domain dependency and sensing range limitation. Finally, three imperative research directions are highlighted, which are critical for realizing more ubiquitous and practical WiFi sensing in real-life applications. 

Commodity WiFi-Based Wireless Sensing Advancements over the Past 5 Years

Surgical smoke removal via residual Swin transformer network

Recently, using deep learning to achieve WiFi-based human activity recognition (HAR) has drawn significant attention. While capable of achieving accurate identification in a single domain (i.e., training and testing in the same consistent WiFi environment), it would become extremely tough when WiFi environments change significantly. As such, domain adversarial neural networks-based approaches have been proposed to handle such diversities across domains, yet often found to share the same limitation in practice: the imbalance between high-capacity of feature extractors and data insufficiency of source domains. This article proposes i-Sample, an intermediate sample generation-based framework, striving to tackle this issue for WiFi-based HAR. i-Sample is mainly designed as two-stage training, where four data augmentation operations are proposed to train a coarse domain-invariant feature extractor in the first stage. In the second stage, we leverage the gradients of classification error to generate intermediate samples to refine the classifiers together with original samples, making i-Sample also capable to be integrated into most domain adversarial adaptation methods without neural network modification. We have implemented a prototype system to evaluate i-Sample, which shows that i-Sample can effectively augment the performance of nowadays mainstream domain adversarial adaptation models for WiFi-based HAR, especially when source domain data is insufficient.

i-Sample: Augment Domain Adversarial Adaptation Models for WiFi-based HAR

Dynamic surface reconstruction in robot-assisted minimally invasive surgery based on neural radiance fields.

Stereo attention-based all-in-one super-resolution for robot-assisted minimally invasive surgery.

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Papers

Surgical smoke removal via residual Swin transformer network

i-Sample: Augment Domain Adversarial Adaptation Models for WiFi-based HAR

Dynamic surface reconstruction in robot-assisted minimally invasive surgery based on neural radiance fields.

Stereo attention-based all-in-one super-resolution for robot-assisted minimally invasive surgery.