ReviewCureus2026
Use of Artificial Intelligence in Preoperative Planning in Surgery: A Narrative Review.
Review in Cureus, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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
1 citing paper in PubMed.
- Artificial intelligence in brain tumor diagnosis and surgical planning: Recent advances.Surgical neurology international · 2026Review
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.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The integration of artificial intelligence (AI) into preoperative planning for surgery has shown significant potential for enhancing surgical outcomes. AI technologies, such as machine learning (ML), deep learning, three-dimensional (3D) modeling, and predictive analytics, are increasingly being used to improve the accuracy of surgical planning, risk stratification, and patient outcomes. The aim of this narrative review was primarily to evaluate the applications, effectiveness, and limitations of AI in preoperative surgical planning. This review synthesized findings from recent studies published between 2020 and 2025, focusing on the use of AI in preoperative planning across surgical specialties, with particular attention to well-documented examples from plastic and reconstructive surgery. AI technologies, including ML algorithms, 3D modeling, and predictive analytics, have shown promise in potentially improving surgical precision, reducing complications, and enhancing patient satisfaction across multiple surgical procedures. However, challenges related to data heterogeneity, publication bias, and the limited number of large-scale prospective multicenter clinical studies remain significant barriers to its broader implementation. AI can potentially support preoperative planning in surgery by augmenting surgical decision-making and contributing to improved clinical outcomes. Integrating AI with emerging technologies, such as 3D printing and virtual reality, could further expand its applications in surgical planning and patient care.
Indexed as
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.