In this study evaluating the ability of AI applications to analyze patient information and determine the appropriate anesthesia method, it was observed that these applications often made choices close to those of anesthesiologists. Specifically, both the Gemini and CoPilot applications chose general anesthesia in 100% of cases in which anesthesiologists did the same. Furthermore, the Gemini application demonstrated a high concordance rate of 85.7% with anesthesiologists’ preferences for patients who were taking medication, indicating a robust capability to align with expert human decisions in specialized medical settings.

Clinicians’ confidence in AI is likely to increase as they understand its potential and limitations, especially when they observe its successful implementation in real-world scenarios [10]. In their review, Singh and Nath noted that allowing robots to perform routine typical procedures can save anesthesiologists time in critical situations, thereby enhancing their ability to think and make decisions. Additionally, studies have addressed instances in which robots have performed intubations in limited patient series or for simulated patients, or in which nerve recognition software-supported ultrasounds have been used to administer block procedures [14, 15].

Although there are publications related to drug infusion systems, pain management, and even intubation-performing robots in the literature, we did not encounter any studies specifically addressing AI technologies that determine anesthesia management in our literature review. Our study provides examples from current practice to increase confidence in AI. As noted in the reviews conducted by Singh and Nath and Bellini et al., the impact of AI in clinical practice generally remains confined to the level of digital display data. There is an ongoing need for more studies that directly explore real-life applications, as demonstrated by our research [14, 16].

In line with the objectives of our study, the review conducted by Lopes et al. emphasized that, despite the rapid AI advancements in the field of medicine, there is still a lack of clinical applications in anesthesia practice [17]. In many AI-related studies in the field of anesthesia, factors that an anesthesiologist could predict without the need for an extensive literature review have been considered, such as calculating the Cormack-Lehane score from human face photographs. In our planning of the present study, we aimed to analyze the results of AI analysis of patient data. For assessments of patients undergoing upper extremity surgery, we found that ChatGPT considered alternatives such as general anesthesia, axillary block, supraclavicular block, and interscalene block and made choices that were 80% consistent with the anesthesiologists’ preferences. This indicates significant potential for AI to support decision-making in anesthesia, with outcomes aligning closely with expert human judgments. AI can quickly select the most appropriate anesthesia method from among numerous alternatives, which is a substantial benefit. The fact that AI applications yield objective and emotion-free choices while considering anesthesia methods could also be a reason for their preference. Kambale and Jadhaw predicted that AI will contribute to the standardization of anesthesia management and the reduction of human errors. This capability highlights AI’s potential to enhance the efficiency and safety of medical procedures by leveraging consistent data-driven decision-making processes [18]. This leads us to suggest that, in the future, anesthesiologists could use AI applications to quickly review and validate their choices.

AI technology is currently being utilized in the field of anesthesia for critical tasks such as monitoring anesthesia depth or pain control and predicting adverse events [19]. The ability of AI applications to predict possible complications may put them a step ahead compared to physicians. The advantage of such AI applications lies in their ability to access and analyze far greater volumes of data in a short time compared to humans, and in a practical way. In the present study, AI was able to suggest anesthesia methods within seconds for patient scenarios. However, it did not respond according to current guideline practices regarding the use of regional anesthesia in patients using anticoagulants. It is clear that AI needs to be developed for specialized patient groups such as those with medication use and surgical history. Due to its incomplete mastery of guidelines and exceptional or specific situations, the prevailing view of AI at present is that it can be a good assistant for doctors [9, 10, 14, 16].

Singh and Nath noted in their review that AI is particularly beneficial in special cases, such as those of patients with rare diseases [14]. In surgeries where drug infusions are computer-assisted through closed-loop systems, the analysis of anesthesia depth and hemodynamic data has allowed the maintenance of vital parameters within narrower ranges. Joosten et al. demonstrated that patients using a computer-assisted closed-loop drug infusion system exhibited improved cognitive functions in the postoperative period [20]. In Hemmerling’s study, it was indicated that closed-loop anesthetic drug infusion systems under the control of an anesthesiologist could become routine in the future [21].

In our study, although AI was able to suggest anesthesia methods based on shared patient information, it was concluded that further development is needed regarding adherence to the current guidelines and the management of specific patient groups. Furthermore, while no statistical differences were found among the AI applications in terms of their approaches to patients, low-level differences were observed. The Copilot application chose general anesthesia more frequently than other applications. It was noted that for patients with pulmonary pathologies such as asthma or COPD, general anesthesia was sometimes recommended without querying vital parameters like the patient’s saturation value. Özsahin analyzed the chosen anesthetic agents and their alternatives while using various algorithms with the Fuzzy PROMETHEE application, emphasizing the necessity of expert support [22].

This study has several limitations due to its single-center design, the inclusion of patients within only a 1-month period, and the exclusion of patients who declined the anesthesiologist’s preference. The exclusion of patients who did not consent to the anesthesiologist’s choices and the lack of pediatric age groups prevent us from reflecting a broader population with our findings. Furthermore, as a potential limitation of the present study, only free versions of AI programs were used.

AI cannot fully master the guidelines and exceptional and specific cases that arrive in the course of medical treatment. Thus, we believe that AI can serve as a valuable assistant rather than replacing doctors.