ArticleProceedings of the IEEE. Institute of Electrical and Electronics Engineers2020
CAI4CAI: The Rise of Contextual Artificial Intelligence in Computer Assisted Interventions.
Article in Proceedings of the IEEE. Institute of Electrical and Electronics Engineers, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
25 citing papers in PubMed.
- The Emerging Role of Multimodal Artificial Intelligence in Urological Surgery.Current oncology (Toronto, Ont.) · 2025Review
- TEMSET-24K: Densely Annotated Dataset for Indexing Multipart Endoscopic Videos using Surgical Timeline Segmentation.Scientific data · 2025Article
- CholecInstanceSeg: A Tool Instance Segmentation Dataset for Laparoscopic Surgery.Scientific data · 2025Article
- LoViT: Long Video Transformer for surgical phase recognition.Medical image analysis · 2025Article
- Artificial intelligence automated surgical phases recognition in intraoperative videos of laparoscopic pancreatoduodenectomy.Surgical endoscopy · 2024Article
- SDA-CLIP: surgical visual domain adaptation using video and text labels.Quantitative imaging in medicine and surgery · 2023Article
- Artificial intelligence and automation in endoscopy and surgery.Nature reviews. Gastroenterology & hepatology · 2023Review
- Procedural Software Toolkit in the Armamentarium of Interventional Therapies: A Review of Additive Usefulness and Current Evidence.Diagnostics (Basel, Switzerland) · 2023Review
- Translation of Medical AR Research into Clinical Practice.Journal of imaging · 2023Article
- Analysing multi-perspective patient-related data during laparoscopic gynaecology procedures.Scientific reports · 2023Article
- PATG: position-aware temporal graph networks for surgical phase recognition on laparoscopic videos.International journal of computer assisted radiology and surgery · 2022Article
- Healing Hands: The Tactile Internet in Future Tele-Healthcare.Sensors (Basel, Switzerland) · 2022Article
- Surgical data science - from concepts toward clinical translation.Medical image analysis · 2022Review
- Does Anatomical Contextual Information Improve 3D U-Net-Based Brain Tumor Segmentation?Diagnostics (Basel, Switzerland) · 2021Article
- Artificial intelligence in perioperative management of major gastrointestinal surgeries.World journal of gastroenterology · 2021Review
- Simulation-to-real domain adaptation with teacher-student learning for endoscopic instrument segmentation.International journal of computer assisted radiology and surgery · 2021Article
- Automation of surgical skill assessment using a three-stage machine learning algorithm.Scientific reports · 2021Article
- Computer Vision in the Operating Room: Opportunities and Caveats.IEEE transactions on medical robotics and bionics · 2021Article
- The Impact of Machine Learning on 2D/3D Registration for Image-Guided Interventions: A Systematic Review and Perspective.Frontiers in robotics and AI · 2021Article
- Computer Vision in the Surgical Operating Room.Visceral medicine · 2020Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
Abstract
Data-driven computational approaches have evolved to enable extraction of information from medical images with a reliability, accuracy and speed which is already transforming their interpretation and exploitation in clinical practice. While similar benefits are longed for in the field of interventional imaging, this ambition is challenged by a much higher heterogeneity. Clinical workflows within interventional suites and operating theatres are extremely complex and typically rely on poorly integrated intra-operative devices, sensors, and support infrastructures. Taking stock of some of the most exciting developments in machine learning and artificial intelligence for computer assisted interventions, we highlight the crucial need to take context and human factors into account in order to address these challenges. Contextual artificial intelligence for computer assisted intervention, or CAI4CAI, arises as an emerging opportunity feeding into the broader field of surgical data science. Central challenges being addressed in CAI4CAI include how to integrate the ensemble of prior knowledge and instantaneous sensory information from experts, sensors and actuators; how to create and communicate a faithful and actionable shared representation of the surgery among a mixed human-AI actor team; how to design interventional systems and associated cognitive shared control schemes for online uncertainty-aware collaborative decision making ultimately producing more precise and reliable interventions.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.