ReviewAnnals of medicine and surgery (2012)2022
The future of Cardiothoracic surgery in Artificial intelligence.
Review in Annals of medicine and surgery (2012), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers, 2 of them syntheses that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
17 citing papers in PubMed, 2 syntheses or guidelines pooled it, 35 citations in OpenAlex.
- Revolution or routine? Comparing AI and traditional imaging in thoracic surgery outcomes: a systematic review.Journal of medicine and life · 2025Pooled it
- The Advent of Artificial Intelligence into Cardiac Surgery: A Systematic Review of Our Understanding.Brazilian journal of cardiovascular surgery · 2024Pooled it
- MVRepairAI: A machine learning-based system to predict surgical methods in mitral valve repair.JTCVS open · 2026Article
- Navigating the challenges of artificial intelligence integration in thoracic surgery.Current challenges in thoracic surgery · 2025Article
- State-of-the-art artificial intelligence methods for pre-operative planning of cardiothoracic surgery and interventions: a narrative review.Journal of thoracic disease · 2025Review
- Innovative strategies in coagulation management for cardiothoracic surgery: a narrative review of pharmacological and nonpharmacological approaches.Journal of cardiothoracic surgery · 2025Review
- Artificial Intelligence in Thoracic Surgery: A Review Bridging Innovation and Clinical Practice for the Next Generation of Surgical Care.Journal of clinical medicine · 2025Review
- Artificial intelligence in cardiovascular procedures: a bibliometric and visual analysis study.Annals of medicine and surgery (2012) · 2025Review
- Artificial Intelligence in Cardiac Surgery: Transforming Outcomes and Shaping the Future.Clinics and practice · 2025Review
- Artificial Intelligence in the Hands of Perfusionists: Revolutionizing Cardiopulmonary Bypass.Brazilian journal of cardiovascular surgery · 2024Article
- Evaluating capabilities of large language models: Performance of GPT-4 on surgical knowledge assessments.Surgery · 2024Article
- Review
- The future of artificial intelligence in thoracic surgery for non-small cell lung cancer treatment a narrative review.Frontiers in oncology · 2024Review
- Artificial Intelligence in Gynaecological Malignancies: Perspectives of a Clinical Oncologist.Cureus · 2023Review
- Evaluating Capabilities of Large Language Models: Performance of GPT4 on Surgical Knowledge Assessments.medRxiv : the preprint server for health sciences · 2023Article
- Recent advances in cardiac anaesthesia.Indian journal of anaesthesia · 2023Article
- The future of artificial intelligence in neurosurgery: A narrative review.Surgical neurology international · 2022Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors at 6 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Humans' great and quick technological breakthroughs in the previous decade have undoubtedly influenced how surgical procedures are executed in the operating room. AI is becoming incredibly influential for surgical decision-making to help surgeons make better projections about the implications of surgical operations by considering different sources of data such as patient health conditions, disease natural history, patient values, and finance. Although the application of artificial intelligence in healthcare settings is rapidly increasing, its mainstream application in clinical practice remains limited. The use of machine learning algorithms in thoracic surgery is extensive, including different clinical stages. By leveraging techniques such as machine learning, computer vision, and robotics, AI may play a key role in diagnostic augmentation, operative management, pre-and post-surgical patient management, and upholding safety standards. AI, particularly in complex surgical procedures such as cardiothoracic surgery, may be a significant help to surgeons in executing more intricate surgeries with greater success, fewer complications, and ensuring patient safety, while also providing resources for robust research and better dissemination of knowledge. In this paper, we present an overview of AI applications in thoracic surgery and its related components, including contemporary projects and technology that use AI in cardiothoracic surgery and general care. We also discussed the future of AI and how high-tech operating rooms will use human-machine collaboration to improve performance and patient safety, as well as its future directions and limitations. It is vital for the surgeons to keep themselves acquainted with the latest technological advancement in AI order to grasp this technology and easily integrate it into clinical practice when it becomes accessible. This review is a great addition to literature, keeping practicing and aspiring surgeons up to date on the most recent advances in AI and cardiothoracic surgery.
Identifiers
What OpenQuestion holds
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.