ReviewAnnals of medicine and surgery (2012)2025
Bridging surgical oncology and personalized medicine: the role of artificial intelligence and machine learning in thoracic surgery.
Review in Annals of medicine and surgery (2012), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 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
3 citing papers in PubMed.
- Artificial intelligence-assisted early screening of lung cancer and accurate diagnosis of pulmonary nodules: research progress and clinical prospects from radiomics to multi-omics integration: a narrative review.Journal of thoracic disease · 2026Review
- Emerging technologies in thoracic surgery: artificial intelligence, robotic-assisted surgery, and telesurgery.Frontiers in surgery · 2026Review
- Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Lung cancer remains the leading cause of cancer-related deaths globally, often detected in advanced stages with poor prognosis. While surgical resection is the mainstay of curative treatment, early detection remains a significant challenge. Advances in personalized medicine, including genomic profiling and low-dose CT scans, have led to more tailored therapies, offering improved outcomes. Integrating artificial intelligence (AI) and machine learning (ML) into oncology has the potential to revolutionize lung cancer management by enhancing early detection, improving treatment precision, and supporting surgical decision-making. AI-driven technologies, such as deep learning algorithms and predictive models, have demonstrated effectiveness in identifying lung nodules, predicting immunotherapy response, and reducing diagnostic errors. Additionally, AI-powered robotics have contributed to improved surgical precision and better patient recovery. However, the widespread adoption of AI in clinical practice faces challenges, including data standardization, ethical concerns, and the need for robust validation. This study explores the question: How can AI and ML optimize thoracic surgical oncology by improving early detection, enhancing surgical precision, and enabling personalized care? This review highlights the significance of AI and ML in thoracic surgery and oncology, discussing their current applications, limitations, and future potential to advance personalized cancer care and improve patient outcomes.
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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.