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Clinical Applications of Artificial Intelligence (AI) in Surgery: Future Trends and Challenges

A special issue of Journal of Clinical Medicine (ISSN 2077-0383). This special issue belongs to the section "General Surgery".

Deadline for manuscript submissions: 15 November 2025 | Viewed by 2113

Special Issue Editors

Prof. Dr. Michał H. Strzelecki

E-Mail Website
Guest Editor
Institute of Electronics, Lodz University of Technology, 93-590 Lodz, Poland
Interests: artificial intelligence; medical imaging; surgical medical robots; machine learning; image analysis
Special Issues, Collections and Topics in MDPI journals
Dr. Rafał Obuchowicz

E-Mail Website
Guest Editor
Department of Diagnostic Imaging, Jagiellonian University Medical College, 31-008 Krakow, Poland
Interests: artificial intelligence; diagnostic imaging; image analysis; diagnostic radiology; image analysis
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

We are pleased to invite you to contribute to our upcoming Special Issue on “Clinical Applications of Artificial Intelligence (AI) in Surgery: Future Trends and Challenges”. In the evolving landscape of modern healthcare, the integration of artificial intelligence (AI) in surgical practices is becoming increasingly significant, promising substantial benefits for both medical professionals and patients. AI’s role in automating and enhancing processes traditionally managed by human professionals is growing in importance. AI algorithms, when supported by extensive datasets, can perform multilayer pattern analyses, thereby aiding in various aspects of the surgical workflow. Unlike radiology, where AI has predominantly focused on image analysis, our Special Issue will delve into the specificities of AI applications in surgery. This includes, but is not limited to, the following areas: preoperative planning, utilizing AI to enhance its precision through advanced imaging techniques and predictive analytics; intraoperative assistance, implementing AI-driven tools and robotic systems to support surgeons during procedures, thus improving accuracy and reducing human error; and postoperative care and rehabilitation, specifically AI applications in monitoring patient recovery, predicting complications, and personalizing rehabilitation programs.

We are particularly interested in submissions that highlight the technical aspects and practical applications of AI in these areas, discuss future trends, and address the challenges faced in integrating AI into surgical practice.

This Special Issue aims to gather and disseminate current knowledge on AI methods utilized in surgical systems, fostering an exchange of ideas which will contribute to a deeper understanding of AI's surgical potential and limitations. We believe that your expertise in this field would make a valuable contribution to this collection.

We are excited about the prospect of your participation in this important discourse and look forward to receiving your submission.

Prof. Dr. Michał H. Strzelecki
Dr. Rafał Obuchowicz
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 100 words) can be sent to the Editorial Office for announcement on this website.

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. Journal of Clinical Medicine is an international peer-reviewed open access semimonthly 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 2600 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
  • surgical planning
  • surgical medical robots
  • machine learning in surgery
  • AI-supported post-operative care

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Published Papers (2 papers)

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9 pages, 736 KB  
Article
Computer-Assisted Protocol-Adherent Blood Lipid Evaluation in Vascular Outpatients (CAPABLE-Vascular)
by Benjamin Thurston, Shrirajh Satheakeerthy, Lewis Hains, Andrew E. C. Booth, Christina Gao, Jamie Bellinge, Brandon Stretton, Peter Psaltis and Stephen Bacchi
J. Clin. Med. 2025, 14(4), 1321; https://doi.org/10.3390/jcm14041321 - 17 Feb 2025
Viewed by 629
Abstract
Background: The lack of availability of test results in vascular surgery outpatient clinics impedes the medical management of vascular risk factors, such as dyslipidaemia and diabetes mellitus. This study sought to evaluate the feasibility of using computer-assisted processes to promote the ordering of [...] Read more.
Background: The lack of availability of test results in vascular surgery outpatient clinics impedes the medical management of vascular risk factors, such as dyslipidaemia and diabetes mellitus. This study sought to evaluate the feasibility of using computer-assisted processes to promote the ordering of routine investigations to promote this management. Method: After consultation with specialist clinicians, clinician–programmers developed a rule-based system to facilitate the ordering of lipid studies and HbA1c prior to vascular clinic appointments. A four-week historical control period prior to the initiation of the intervention was compared to a four-week period following the intervention. Results: There were 1165 patients in the study. In the pre-intervention period, 38.0% of patients had HbA1c and 17.9% had lipid studies in the preceding 6 months. In the post-intervention period, HbA1c and lipid studies were ordered for 100% of vascular outpatients (p < 0.001). Conclusions: The use of computer-assisted processes to facilitate the requesting of routine outpatient investigations is feasible and shows early signs of being effective. Follow-up studies examining clinical endpoints are required. Full article
(This article belongs to the Special Issue Clinical Applications of Artificial Intelligence (AI) in Surgery: Future Trends and Challenges)
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15 pages, 446 KB  
Systematic Review
The Integration of Artificial Intelligence into Robotic Cancer Surgery: A Systematic Review
by Agnieszka Leszczyńska, Rafał Obuchowicz, Michał Strzelecki and Michał Seweryn
J. Clin. Med. 2025, 14(17), 6181; https://doi.org/10.3390/jcm14176181 - 1 Sep 2025
Viewed by 805
Abstract
Background/Objectives: This systematic review aims to synthesize recent studies on the integration of artificial intelligence (AI) into robotic surgery for oncological patients. It focuses on studies using real patient data and AI tools in robotic oncologic surgery. Methods: This systematic review [...] Read more.
Background/Objectives: This systematic review aims to synthesize recent studies on the integration of artificial intelligence (AI) into robotic surgery for oncological patients. It focuses on studies using real patient data and AI tools in robotic oncologic surgery. Methods: This systematic review followed PRISMA guidelines to ensure a robust methodology. A comprehensive search was conducted in June 2025 across Embase, Medline, Web of Science, medRxiv, Google Scholar, and IEEE databases, using MeSH terms, relevant keywords, and Boolean logic. Eligible studies were original research articles published in English between 2024 and 2025, focusing on AI applications in robotic cancer surgery using real patient data. Studies were excluded if they were non-peer-reviewed, used synthetic/preclinical data, addressed non-oncologic indications, or explored non-robotic AI applications. This approach ensured the selection of studies with practical clinical relevance. Results: The search identified 989 articles, with 17 duplicates removed. After screening, 921 were excluded, and 37 others were eliminated for reasons such as misalignment with inclusion criteria or lack of full text. Ultimately, 14 articles were included, with 8 using a retrospective design and 6 based on prospective data. These included articles that varied significantly in terms of the number of participants, ranging from several dozen to several thousand. These studies explored the application of AI across various stages of robotic oncologic surgery, including preoperative planning, intraoperative support, and postoperative predictions. The quality of 11 included studies was very good and good. Conclusions: AI significantly supports robotic oncologic surgery at various stages. In preoperative planning, it helps estimate the risk of conversion from minimally invasive to open colectomy in colon cancer. During surgery, AI enables precise tumor and vascular structure localization, enhancing resection accuracy, preserving healthy tissue, and reducing warm ischemia time. Postoperatively, AI’s flexibility in predicting functional and oncological outcomes through context-specific models demonstrates its value in improving patient care. Due to the relatively small number of cases analyzed, further analysis of the issues presented in this review is necessary. Full article
(This article belongs to the Special Issue Clinical Applications of Artificial Intelligence (AI) in Surgery: Future Trends and Challenges)
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