Should Artificial Intelligence Become a Standard Component of Perioperative Risk Stratification and Surgical Planning ?
DOI:
https://doi.org/10.68041/medsc.v1i1/04Keywords:
Artificial Intelligence, Decision Making, Machine Learning, Perioperative Care, Postoperative Complications, Risk Assessment, Surgical ProceduresAbstract
The field of perioperative medicine has the potential for transformative uses of artificial intelligence (AI), notably in risk stratification and in surgical planning. AI models can now combine high-dimensional datasets such as electronic health records, images, genomics and real-time vital signs to generate predictions for complications, mortality, and resource utilization after surgery. Proponents claim that AI minimizes cognitive bias, detects hidden patterns of risk, and allows for personalized surgical strategies, which could have a positive impact on patient outcomes and efficiency. However, significant limitations persist: some models have no external validation, limited generalizability for a variety of populations, and operate as “black boxes” with unclear clinical reasoning. Ethical challenges involve algorithmic bias amplifying disparities, informed consent for AI recommendations, and liability uncertainty when predictions fail. Implementation hurdles include interoperability of data, regulatory approval, clinician training, and integration with workflow. Currently, AI should be used as a decision support tool rather than a replacement for clinical standards. Explainable architectures, a clear medicolegal framework, prospective validation, and fairness audits are required to achieve widespread adoption. AI is not a universal standard yet, but targeted use in high-risk or high-volume areas can be justified. A responsible path forward is phased and evidence-based, initiating with low-risk advisory applications before broader expansion.
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