Publication Name	World Journal of Clinical Oncology
Manuscript ID	94752
Country	Colombia

Received	2024-03-24 19:51
Peer-Review Started	2024-03-24 19:51
To Make the First Decision
Return for Revision	2024-08-09 12:35
Revised	2024-08-10 23:06
Second Decision	2024-08-22 02:35
Accepted by Journal Editor-in-Chief
Accepted by Executive Editor-in-Chief	2024-08-22 08:07
Articles in Press	2024-08-22 08:07
Publication Fee Transferred
Edit the Manuscript by Language Editor
Typeset the Manuscript	2024-08-26 01:07
Publish the Manuscript Online	2024-09-29 06:08

ISSN	2218-4333 (online)
Open Access	This article is an open-access article that was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution NonCommercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: https://creativecommons.org/Licenses/by-nc/4.0/
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Category	Computer Science, Artificial Intelligence
Manuscript Type	Editorial
Article Title	Precision at scale: Machine learning revolutionizing laparoscopic surgery
Manuscript Source	Invited Manuscript
All Author List	Carlos M Ardila and Daniel González-Arroyave
Funding Agency and Grant Number
Corresponding Author	Carlos M Ardila, Doctor, MSc, PhD, Academic Editor, Academic Research, Associate Professor, Biomedical Stomatology Research Group, Universidad de Antioquia U de A, Calle 70 52-21, Medellín 0057, Colombia. martin.ardila@udea.edu.co
Key Words	Machine learning; Computer neural network; Minimally invasive surgical procedures; Hand-assisted laparoscopy; Laparoscopy
Core Tip	Integration of machine learning in laparoscopic surgery revolutionizes patient care, enhancing surgical precision and personalized treatment. Advanced imaging techniques, robotic systems, and virtual reality simulations powered by machine learning algorithms optimize procedural techniques and training methods. However, challenges such as data privacy and algorithm bias must be addressed for responsible deployment. Collaborations between clinicians, engineers, and data scientists drive innovation, shaping a future where minimally invasive surgery is safer, more effective, and accessible to all.
Publish Date	2024-09-29 06:08
Citation	<p>Ardila CM, González-Arroyave D. Precision at scale: Machine learning revolutionizing laparoscopic surgery. <i>World J Clin Oncol</i> 2024; 15(10): 1256-1263</p>
URL	https://www.wjgnet.com/2218-4333/full/v15/i10/1256.htm
DOI	https://dx.doi.org/10.5306/wjco.v15.i10.1256

Full Article (PDF)	WJCO-15-1256-with-cover.pdf
Manuscript File	94752_Auto_Edited_012031.docx
Answering Reviewers	94752-answering-reviewers.pdf
Audio Core Tip	94752-audio.ogg
Conflict-of-Interest Disclosure Form	94752-conflict-of-interest-statement.pdf
Copyright License Agreement	94752-copyright-assignment.pdf
Non-Native Speakers of English Editing Certificate	94752-non-native-speakers.pdf
Peer-review Report	94752-peer-reviews.pdf
Scientific Misconduct Check	94752-scientific-misconduct-check.png
Scientific Editor Work List	94752-scientific-editor-work-list.pdf
CrossCheck Report	94752-crosscheck-report.pdf