Özşahin Y.
THE ROLE OF ARTIFICIAL INTELLIGENCE IN PERIOPERATIVE CARE, Prof. Dr. Kerem ERKALP,Prof. Dr. ZiyaSALİHOĞLU, Editör, Turkiye Klinikleri, İstanbul, ss.90-98, 2025
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Yayın Türü:
Kitapta Bölüm / Mesleki Kitap
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Basım Tarihi:
2025
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Yayınevi:
Turkiye Klinikleri
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Basıldığı Şehir:
İstanbul
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Sayfa Sayıları:
ss.90-98
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Editörler:
Prof. Dr. Kerem ERKALP,Prof. Dr. ZiyaSALİHOĞLU, Editör
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İstanbul Üniversitesi-Cerrahpaşa Adresli:
Evet
Özet
Preoperative anesthesia assessment is a crucial step in perioperative care, aimed at evaluating a patient's overall
health and formulating tailored management strategies to ensure safety and reduce complications. Traditional
assessment methods, although fundamental, are often limited by subjectivity, time constraints, and inconsistent
clinical interpretation. In this context, artificial intelligence technologies offer valuable tools for enhancing
precision, consistency, and efficiency in preoperative planning.
AI systems utilize machine learning algorithms, natural language processing, and deep neural networks to analyze
vast amounts of patient data ranging from electronic health records and laboratory findings to biometric images.
These technologies facilitate applications such as automated ASA scoring, comorbidity risk prediction, airway
difficulty assessment, and individualized drug dosing. For example, AI models trained on facial images can
outperform traditional clinical scoring systems in predicting difficult intubation. Similarly, ML-driven tools can
identify latent conditions like undiagnosed diabetes or anemia, which can then be managed proactively.
Beyond risk assessment, AI supports resource optimization by improving surgical scheduling accuracy and
predicting operating room utilization. Informed consent processes and patient education can also benefit from AI-
driven systems, which improve accessibility, comprehension, and standardization. However, while AI enhances
clinical decision-making, it must be viewed as a complementary tool. Limitations such as data bias, lack of
empathy, and the need for clinician oversight must be carefully managed.
In conclusion, integrating AI into preoperative anesthesia management holds promise for advancing patient-
centered, data-informed care. Ongoing research and clinician–developer collaboration will be key in aligning these
innovations with ethical standards, patient safety, and clinical excellence. anesthesia assessment is a crucial step in perioperative care, aimed at evaluating a patient's overall
health and formulating tailored management strategies to ensure safety and reduce complications. Traditional
assessment methods, although fundamental, are often limited by subjectivity, time constraints, and inconsistent
clinical interpretation. In this context, artificial intelligence technologies offer valuable tools for enhancing
precision, consistency, and efficiency in preoperative planning.
AI systems utilize machine learning algorithms, natural language processing, and deep neural networks to analyze
vast amounts of patient data ranging from electronic health records and laboratory findings to biometric images.
These technologies facilitate applications such as automated ASA scoring, comorbidity risk prediction, airway
difficulty assessment, and individualized drug dosing. For example, AI models trained on facial images can
outperform traditional clinical scoring systems in predicting difficult intubation. Similarly, ML-driven tools can
identify latent conditions like undiagnosed diabetes or anemia, which can then be managed proactively.
Beyond risk assessment, AI supports resource optimization by improving surgical scheduling accuracy and
predicting operating room utilization. Informed consent processes and patient education can also benefit from AI-
driven systems, which improve accessibility, comprehension, and standardization. However, while AI enhances
clinical decision-making, it must be viewed as a complementary tool. Limitations such as data bias, lack of
empathy, and the need for clinician oversight must be carefully managed.
In conclusion, integrating AI into preoperative anesthesia management holds promise for advancing patient-
centered, data-informed care. Ongoing research and clinician–developer collaboration will be key in aligning these
innovations with ethical standards, patient safety, and clinical excellence.Preoperative anesthesia assessment is a crucial step in perioperative care, aimed at evaluating a patient's overall
health and formulating tailored management strategies to ensure safety and reduce complications. Traditional
assessment methods, although fundamental, are often limited by subjectivity, time constraints, and inconsistent
clinical interpretation. In this context, artificial intelligence technologies offer valuable tools for enhancing
precision, consistency, and efficiency in preoperative planning.
AI systems utilize machine learning algorithms, natural language processing, and deep neural networks to analyze
vast amounts of patient data ranging from electronic health records and laboratory findings to biometric images.
These technologies facilitate applications such as automated ASA scoring, comorbidity risk prediction, airway
difficulty assessment, and individualized drug dosing. For example, AI models trained on facial images can
outperform traditional clinical scoring systems in predicting difficult intubation. Similarly, ML-driven tools can
identify latent conditions like undiagnosed diabetes or anemia, which can then be managed proactively.
Beyond risk assessment, AI supports resource optimization by improving surgical scheduling accuracy and
predicting operating room utilization. Informed consent processes and patient education can also benefit from AI-
driven systems, which improve accessibility, comprehension, and standardization. However, while AI enhances
clinical decision-making, it must be viewed as a complementary tool. Limitations such as data bias, lack of
empathy, and the need for clinician oversight must be carefully managed.
In conclusion, integrating AI into preoperative anesthesia management holds promise for advancing patient-
centered, data-informed care. Ongoing research and clinician–developer collaboration will be key in aligning these
innovations with ethical standards, patient safety, and clinical excellence.