ReviewCurrent oncology (Toronto, Ont.)2024
Enhancing Thoracic Surgery with AI: A Review of Current Practices and Emerging Trends.
Review in Current oncology (Toronto, Ont.), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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
11 citing papers in PubMed.
- CT-based deep learning radiomics predicts micropapillary/solid components in lung adenocarcinoma.Journal of thoracic disease · 2026Article
- ERS Congress 2025: highlights from the Thoracic Surgery and Lung Transplantation Assembly.ERJ open research · 2026Article
- Application of artificial intelligence in lung cancer diagnosis, therapy, and prognosis.BMC medical genomics · 2026Review
- Twin Transformation in Cardiothoracic Surgery: The Convergence of Digital Innovation and Sustainability.Journal of cardiovascular development and disease · 2026Review
- Artificial intelligence in non-small cell lung cancer: transforming diagnosis, treatment, and prognostic evaluation.Frontiers in medicine · 2026Review
- Emerging technologies in thoracic surgery: artificial intelligence, robotic-assisted surgery, and telesurgery.Frontiers in surgery · 2026Review
- The utility of a rigid bronchoscope.Breathe (Sheffield, England) · 2025Review
- Navigating the challenges of artificial intelligence integration in thoracic surgery.Current challenges in thoracic surgery · 2025Article
- Bridging surgical oncology and personalized medicine: the role of artificial intelligence and machine learning in thoracic surgery.Annals of medicine and surgery (2012) · 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
- Quantitative evaluation and optimization of AI policy and regulatory texts for smart healthcare.Frontiers in public health · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Artificial intelligence (AI) is increasingly becoming integral to medical practice, potentially enhancing outcomes in thoracic surgery. AI-driven models have shown significant accuracy in diagnosing non-small-cell lung cancer (NSCLC), predicting lymph node metastasis, and aiding in the efficient extraction of electronic medical record (EMR) data. Moreover, AI applications in robotic-assisted thoracic surgery (RATS) and perioperative management reveal the potential to improve surgical precision, patient safety, and overall care efficiency. Despite these advancements, challenges such as data privacy, biases, and ethical concerns remain. This manuscript explores AI applications, particularly machine learning (ML) and natural language processing (NLP), in thoracic surgery, emphasizing their role in diagnosis and perioperative management. It also provides a comprehensive overview of the current state, benefits, and limitations of AI in thoracic surgery, highlighting future directions in the field.
Indexed as
Identifiers
What Socratic 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 Socratic graph.