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Surgeries
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The Application of Artificial Intelligence in Surgical Procedures
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The Application of Artificial Intelligence in Surgical Procedures

A special issue of Surgeries (ISSN 2673-4095).

Deadline for manuscript submissions: 30 September 2026 | Viewed by 2066

Special Issue Editors

Dr. Milan Toma

E-Mail Website
Guest Editor
Department of Osteopathic Manipulative Medicine, College of Osteopathic Medicine, New York Institute of Technology, Old Westbury, NY 11568, USA
Interests: biomechanical engineering; computational mechanics; computational biomechanics; image processing; brain injuries; fetus injuries; impact biomechanics; cardiovascular fluid–structure interaction
Special Issues, Collections and Topics in MDPI journals
Dr. Michael Nizich

E-Mail Website
Co-Guest Editor
Entrepreneurship and Technology Innovation Center, College of Engineering and Computing Sciences, New York Institute of Technology, Old Westbury, NY 11568, USA
Interests: cybersecurity; software development; database design; data science; machine learning; quantum computing

Special Issue Information

Dear Colleagues,

We are pleased to announce a Special Issue of Surgeries dedicated to the transformative role of Artificial Intelligence (AI) in the field of surgery. The integration of AI technologies is rapidly reshaping surgical practice, from preoperative planning and intraoperative guidance to postoperative care and outcome prediction. This Special Issue aims to showcase cutting-edge research, innovative methodologies, and comprehensive reviews that highlight the current and future impact of AI across all surgical disciplines.

We invite submissions that explore the development and application of AI-driven tools for surgical decision support, robotic-assisted procedures, image analysis, and real-time intraoperative navigation. Topics may also include surgical simulation and training, predictive analytics for patient outcomes, digital twins, telemedicine, and the ethical, legal, and social implications of AI in surgical settings. Contributions addressing challenges such as data privacy, algorithm transparency, and the integration of AI into clinical workflows are particularly welcome.

By bringing together multidisciplinary perspectives, this Special Issue seeks to foster collaboration between surgeons, engineers, data scientists, and healthcare professionals. Our goal is to provide a comprehensive overview of how AI is advancing surgical care, improving patient safety, and shaping the future of operative medicine. We look forward to your valuable contributions to this rapidly evolving field.

Dr. Milan Toma
Dr. Michael Nizich
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Surgeries is an international peer-reviewed open access quarterly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • artificial intelligence in surgery
  • surgical robotics
  • medical imaging
  • surgical simulation
  • clinical decision support
  • digital health
  • predictive analytics
  • telemedicine
  • surgical data science
  • ethical and legal issues in AI

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (2 papers)

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Research

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31 pages, 381 KB  
Article
Stratified Procedural Risk Assessment in Colorectal Surgery: A Comparative Analysis of Statistical and Machine Learning Approaches Using Combined Surgical Approach and Operative Duration Categories
by Dennis Elengickal, Michael Nizich and Milan Toma
Surgeries 2026, 7(2), 42; https://doi.org/10.3390/surgeries7020042 - 25 Mar 2026
Viewed by 465
Abstract
Background: Postoperative complications following colorectal surgery remain a persistent clinical challenge. Traditional risk stratification has focused on patient characteristics, while conventional modeling approaches treat procedural factors such as operative duration and surgical approach as independent predictors, potentially obscuring interaction effects. Methods: This study [...] Read more.
Background: Postoperative complications following colorectal surgery remain a persistent clinical challenge. Traditional risk stratification has focused on patient characteristics, while conventional modeling approaches treat procedural factors such as operative duration and surgical approach as independent predictors, potentially obscuring interaction effects. Methods: This study developed a machine learning model stratifying 7908 colorectal surgery patients into four distinct procedural risk categories based on combined surgical approach and operative duration (laparoscopic-short, laparoscopic-long, open-short, open-long), rather than treating these factors as separate variables. A gradient boosting ensemble classifier with RUSBoost resampling was trained on predictor variables including patient demographics, comorbidities, and intraoperative factors. Results: Feature importance analysis revealed that the open-long category emerged as the single most important predictor, substantially exceeding all other variables. Weight loss, body mass index, patient age, and electrolyte abnormalities ranked as the next most important predictors. Stratified complication rates demonstrated a critical interaction: prolonged duration more than doubled complication risk in open procedures (short-duration: 9.99%, long-duration: 20.46%), whereas laparoscopic procedures showed only a modest increase from short-duration (10.45%) to long-duration (14.08%) cases. Logistic regression benchmark analysis confirmed the duration-approach interaction (OR = 1.53, 95% CI: 0.97–2.39), achieving comparable discrimination (c-statistic 0.678 vs. 0.665 for the ensemble model). Decision curve analysis demonstrated logistic regression provided superior clinical utility across most threshold probabilities. Conclusions: The dual analytical framework (i.e., statistical inference for quantifying associations and machine learning for predictive feature ranking) offers complementary insights for clinical application. These findings demonstrate that stratified feature engineering can elucidate complex risk phenotypes that may be obscured when procedural factors are analyzed independently. Full article
(This article belongs to the Special Issue The Application of Artificial Intelligence in Surgical Procedures)
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Review

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24 pages, 2962 KB  
Review
Image-Guided Autonomous Robotic Surgery in the Context of Therapies Managed by Intelligent Digital Technologies: A Narrative Review
by Adel Razek
Surgeries 2026, 7(1), 26; https://doi.org/10.3390/surgeries7010026 - 16 Feb 2026
Viewed by 840
Abstract
This narrative review aims to highlight and analyze the supervision of precision robotic surgical interventions. These are autonomous, closed-loop procedures, assisted by images and managed by intelligent digital tools. These administered procedures are designed to be safe and reliable, adhering to the principles [...] Read more.
This narrative review aims to highlight and analyze the supervision of precision robotic surgical interventions. These are autonomous, closed-loop procedures, assisted by images and managed by intelligent digital tools. These administered procedures are designed to be safe and reliable, adhering to the principles of minimal invasiveness, precise positioning, and non-toxicity. Thus, a precision intervention uses non-ionizing imaging-assisted robotics, controlled by a precise positioning device, forming an autonomous procedure augmented by artificial intelligence tools and supervised by digital twins. This intelligent digital management procedure allows staff to plan, train, predict, and execute interventions under human supervision. Patient safety and staff efficiency are linked to non-ionizing imaging, minimal invasiveness through image guidance, and strict delimitation of the intervention zone through precise positioning. This study includes, successively, sections covering an introduction, therapeutic and surgical interventions, imaging strategies integrating diagnostic and assistance functions, intelligent digital tools including digital twins and artificial intelligence, image-guided procedures including autonomous and precision robotic surgical interventions increased by machine learning, as well as augmented healthcare monitoring, and a discussion and conclusions of the review. All topics addressed in this analysis are supported by examples from the literature. Full article
(This article belongs to the Special Issue The Application of Artificial Intelligence in Surgical Procedures)
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