ReviewJournal of medical systems2025
Artificial Intelligence in Ambulatory Surgery: Current Applications, Challenges, and Future Directions.
Review in Journal of medical systems, 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.
- Evaluating the feasibility and safety of 48-h short-stay pathway for robot-assisted laparoscopic partial nephrectomy: a propensity score-matched analysis.International urology and nephrology · 2026Article
- Cardiotoxicity Challenges in Nanomaterial: Risk Assessment, Mechanisms, and Mitigation Strategies.International journal of nanomedicine · 2026Review
- Artificial intelligence and machine learning approaches for patient safety in complex surgery: a review.Patient safety in surgery · 2025Review
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
Abstract
Ambulatory surgery enhances resource utilization through reduced hospital stays and costs without compromising clinical outcomes. However, existing workflows are labor-intensive and repetitive, necessitating optimization in patient selection, assessment, admission notifications, no-show management, patient education, and postoperative follow-up. Artificial intelligence (AI) offers promising solutions to these challenges. This narrative review aimed to outline current AI applications in ambulatory surgery, appraise limitations, and discuss actionable pathways for future innovation. The PubMed database was systematically searched. Inclusion criteria were original research on AI in ambulatory surgery. Exclusion criteria covered weak thematic connections and unavailable full texts. Two researchers independently conducted the search and data extraction. 50 articles were analyzed in this review. AI technologies, including machine learning, computer vision, and natural language processing, are increasingly used for preoperative patient selection and no-show prediction, intraoperative patient information verification, real-time monitoring and decision support, and postoperative recovery monitoring and health guidance. Nonetheless, AI implementation faces challenges such as data heterogeneity, algorithm interpretability, ethical concerns, and regulatory hurdles. AI demonstrates significant potential to optimize ambulatory surgery procedures, enhance clinical decision-making, and improve patient outcomes. Standardized data collection, collaborative data-sharing, transparency, and model validation with clinically meaningful endpoints are essential for robust and extensive AI application in ambulatory surgery. These elements can ultimately enhance the efficiency and safety of ambulatory surgical procedures.
Indexed as
Identifiers
41144066What 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.