- Artificial Intelligence-Assisted Monitoring for Detecting Perioperative Safety Deviations in General Surgical Practice
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Opeyemi Qozeem Asafa, Aishat Omowunmi Asafa, Ayodeji Olaolu Oyeniran, Olajide Emmanuel Babalola, Olumuyiwa Tope Ajayeoba, Roseline Olufunmilola Folami, Ganiyu Adebukola Oyeniyi, Kehinde Adesola Alatishe, Adegboyega Segun Afolabi, Ismail Idowu Uthman
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Received April 17, 2026 Accepted May 25, 2026 Published online June 5, 2026
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DOI: https://doi.org/10.69474/jsie.2026.00080
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 Abstract
- Background
Perioperative safety deviations remain an important challenge in surgical care despite implementation of safety measures such as the surgical safety checklist. Emerging digital technologies, particularly artificial intelligence (AI), may provide additional support for identifying potential safety threats during surgical care. This study evaluated the usefulness of AI-assisted monitoring for identifying and helping prevent common perioperative safety deviations in routine general surgical practice.
Methods
This prospective observational study included 136 patients who underwent general surgical procedures at a tertiary hospital. Procedures included inguinal hernia repair, exploratory laparotomy, appendectomy, ventral or incisional hernia repair, excisional biopsy, and other minor surgical operations. AI-supported monitoring tools were integrated into perioperative workflows to identify potential safety deviations during operative care. Demographic characteristics, procedure types, and intraoperative safety events were recorded. The primary outcome was the frequency of safety deviations and their detection using AI support. Secondary outcomes included the proportion of identified deviations corrected before completion of surgery.
Results
Among the 136 procedures, 26 perioperative safety deviations (19.1%) were identified. The most common deviations involved incomplete checklist steps, delayed administration of prophylactic antibiotics, and discrepancies in instrument or sponge counts. AI-assisted monitoring detected 20 of the 26 deviations (76.9%), and 17 of the 20 detected deviations (85.0%) were corrected before completion of the procedure. The overall detection rate increased from 53.8% with routine observation alone to 76.9% with AI-assisted monitoring (p=0.02). No cases of retained surgical items or wrong-site surgery occurred during the study period.
Conclusions
AI-assisted monitoring demonstrated the potential to improve early recognition and correction of perioperative safety deviations during general surgical procedures. Integration of such systems into perioperative workflows may strengthen existing safety practices and improve detection of workflow-related safety irregularities.
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