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 May 2025 | Viewed by 714

Special Issue Editors

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

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Keywords

  • artificial intelligence
  • surgical planning
  • surgical medical robots
  • machine learning in surgery
  • AI-supported post-operative care

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Published Papers (1 paper)

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Research

9 pages, 736 KiB  
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 389
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
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