SynthesisJournal of medicine and life2025
Revolution or routine? Comparing AI and traditional imaging in thoracic surgery outcomes: a systematic review.
Synthesis in Journal of medicine and life, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Artificial intelligence (AI) and machine learning (ML) are increasingly pivotal in advancing postoperative imaging for thoracic surgery, presenting transformative potentials in clinical practice. This comprehensive review investigates the current applications and future directions of AI and ML by comparing them with traditional imaging methods. It highlights how these technologies assist in the early detection of postoperative complications such as infections, anastomotic leaks, and pleural effusions through sophisticated image analysis algorithms. The discussion extends to the automation of routine imaging tasks, which not only improves efficiency but also allows radiologists to focus on more complex cases. Looking ahead, the article considers the implications of emerging technologies such as deep learning and neural networks. This further enhances the capabilities of AI in medical imaging. By providing a thorough overview of the current landscape and anticipating future advancements, this article highlights the profound impact of AI and ML on improving patient care and outcomes in thoracic surgery.
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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.