
==== Front
BMC Anesthesiol
BMC Anesthesiol
BMC Anesthesiology
1471-2253
BioMed Central London

2699
10.1186/s12871-024-02699-z
Systematic Review
Artificial intelligence-assisted interventions for perioperative anesthetic management: a systematic review and meta-analysis
Shimada Kensuke 123
Inokuchi Ryota inokuchir-icu@h.u-tokyo.ac.jp

456
Ohigashi Tomohiro 78
Iwagami Masao 4
Tanaka Makoto 9
Gosho Masahiko 8
Tamiya Nanako 4101112
1 https://ror.org/02956yf07 grid.20515.33 0000 0001 2369 4728 Graduate School of Comprehensive Human Sciences, University of Tsukuba, Ibaraki, Japan
2 https://ror.org/02956yf07 grid.20515.33 0000 0001 2369 4728 Translational Research Promotion Center, Tsukuba Clinical Research & Development Organization, University of Tsukuba, Ibaraki, Japan
3 https://ror.org/028fz3b89 grid.412814.a 0000 0004 0619 0044 Department of Anesthesiology, University of Tsukuba Hospital, Ibaraki, Japan
4 https://ror.org/02956yf07 grid.20515.33 0000 0001 2369 4728 Department of Health Services Research, Institute of Medicine, University of Tsukuba, Ibaraki, Japan
5 grid.412708.8 0000 0004 1764 7572 Department of Emergency and Critical Care Medicine, The University of Tokyo Hospital, Tokyo, Japan
6 grid.412708.8 0000 0004 1764 7572 Department of Clinical Engineering, The University of Tokyo Hospital, Tokyo, Japan
7 https://ror.org/05sj3n476 grid.143643.7 0000 0001 0660 6861 Department of Information and Computer Technology, Faculty of Engineering, Tokyo University of Science, Tokyo, Japan
8 https://ror.org/02956yf07 grid.20515.33 0000 0001 2369 4728 Department of Biostatistics, Institute of Medicine, University of Tsukuba, Ibaraki, Japan
9 https://ror.org/02956yf07 grid.20515.33 0000 0001 2369 4728 Department of Anesthesiology, Institute of Medicine, University of Tsukuba, Ibaraki, Japan
10 https://ror.org/02956yf07 grid.20515.33 0000 0001 2369 4728 Health Services Research and Development Center, University of Tsukuba, Ibaraki, Japan
11 https://ror.org/02956yf07 grid.20515.33 0000 0001 2369 4728 Center for Artificial Intelligence Research, University of Tsukuba, Ibaraki, Japan
12 https://ror.org/02956yf07 grid.20515.33 0000 0001 2369 4728 Cybermedicine Research Center, University of Tsukuba, Ibaraki, Japan
4 9 2024
4 9 2024
2024
24 30625 6 2024
26 8 2024
© The Author(s) 2024
2024
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Background

Integration of artificial intelligence (AI) into medical practice has increased recently. Numerous AI models have been developed in the field of anesthesiology; however, their use in clinical settings remains limited. This study aimed to identify the gap between AI research and its implementation in anesthesiology via a systematic review of randomized controlled trials with meta-analysis (CRD42022353727).

Methods

We searched the databases of Medical Literature Analysis and Retrieval System Online (MEDLINE), Excerpta Medica Database (Embase), Web of Science, Cochrane Central Register of Controlled Trials (CENTRAL), Institute of Electrical and Electronics Engineers Xplore (IEEE), and Google Scholar and retrieved randomized controlled trials comparing conventional and AI-assisted anesthetic management published between the date of inception of the database and August 31, 2023.

Results

Eight randomized controlled trials were included in this systematic review (n = 568 patients), including 286 and 282 patients who underwent anesthetic management with and without AI-assisted interventions, respectively. AI-assisted interventions used in the studies included fuzzy logic control for gas concentrations (one study) and the Hypotension Prediction Index (seven studies; adding only one indicator). Seven studies had small sample sizes (n = 30 to 68, except for the largest), and meta-analysis including the study with the largest sample size (n = 213) showed no difference in a hypotension-related outcome (mean difference of the time-weighted average of the area under the threshold 0.22, 95% confidence interval -0.03 to 0.48, P = 0.215, I2 93.8%).

Conclusions

This systematic review and meta-analysis revealed that randomized controlled trials on AI-assisted interventions in anesthesiology are in their infancy, and approaches that take into account complex clinical practice should be investigated in the future.

Trial registration

This study was registered with the International Prospective Register of Systematic Reviews (PROSPERO ID: CRD42022353727).

Supplementary Information

The online version contains supplementary material available at 10.1186/s12871-024-02699-z.

Keywords

Anesthesia
Artificial intelligence
Systematic review
Meta-analysis
Rondomized controlled trial
issue-copyright-statement© BioMed Central Ltd., part of Springer Nature 2024
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pmcBackground

There has been a recent surge in studies on the application of artificial intelligence (AI) in medicine [1]. A similar trend has also been observed in the field of anesthesia, as evidenced by the numerous predictive models that have been proposed [2–4]. The number of publications is expected to increase with advances in technology [5].

However, to date, in the conventional operating room setting, AI models have not been comprehensively employed to replace clinical judgement in patient care. Typically, anesthesiologists continue to rely on own clinical judgment, often without AI support. Thus, there is a gap between current practices and the growing body of research on AI applications in this area.

To the best of our knowledge, no systematic review of randomized controlled trials (RCTs) has covered AI-assisted interventions and their outcomes in anesthesiology. Gaining an in-depth understanding of the characteristics and results of studies on AI can help clarify the delay in the widespread adoption of AI in the field of anesthesiology and guide future research. Therefore, this systematic review aimed to summarize the findings of RCTs that compared interventions with and without AI assistance in anesthesiology.

Methods

This study was registered with the International Prospective Register of Systematic Reviews (CRD42022353727). This review adheres to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines [6].

Eligibility criteria

RCTs that met the following criteria were included in this review: (i) published in peer-reviewed journals, (ii) included patients who underwent surgery under anesthesia as the study population, (iii) included patients who underwent surgery without AI-assisted interventions for perioperative anesthetic management as the control group, and (iv) investigated any anesthesia-related outcomes (e.g. vital signs, indicators in perioperative managements, and complications). Observational studies, reviews, editorials, conference articles, comments, standalone abstracts, and nonhuman studies were excluded. We defined AI as computer or model-based methods including neural network, machine learning, prediction network (in the context of machine learning), regression, and fuzzy logic (which allows computers to make flexible, human-like decisions by representing vague situations with numerical values). We used a broad definition of AI because a comprehensive review is more useful than a narrow definition that results in the exclusion of important studies from the review. However, we did not include studies for which the algorithm was not specified.

Search strategy

Medical Literature Analysis and Retrieval System Online (MEDLINE), Excerpta Medica Database (Embase), Web of Science, Cochrane Central Register of Controlled Trials (CENTRAL), Institute of Electrical and Electronics Engineers Xplore (IEEE), and Google Scholar were searched to retrieve relevant articles published between the date of database inception and August 31, 2023, without language restrictions. The search terms included: (“artificial intelligence” OR “machine learning” OR “supervised learning” OR “unsupervised learning” OR “reinforcement learning” OR “neural network*” OR “support vector machine” OR “fuzzy logic” OR “data mining” OR “pattern recognition*” OR “deep learning” OR “prediction network*”) AND (anesthes* OR anaesthes*) AND ("randomized controlled trial"). Additional file 1 summarizes the search strategies used for each database. In addition, the reference lists of all included studies and recent relevant reports or reviews were manually searched.

Study selection

Two authors (K.S. and R.I.) independently screened the literature using Covidence. The reference lists of the included articles were also screened to identify additional eligible studies. In the case of disagreements between the two authors, a consensus was reached via discussion with a third reviewer (M.I.). The corresponding authors of studies under evaluation were contacted for clarification if it was unclear whether the study was eligible for inclusion in the present review or if the study did not report sufficient data.

Data extraction

Data regarding the following characteristics were extracted: study characteristics (publication year and country), participant characteristics (age, sex, and type of surgery and anesthesia), interventions (type and details of AI), and anesthesia-related outcomes.

Statistical analyses

If the same outcome was measured for the same intervention in ≥ 3 studies, random-effects meta-analysis was conducted to estimate the outcome effect size and 95% confidence intervals (CI) using the quantile matching estimation method as implemented in the “metamedian” package (version 1.1.1) in R (version 4.2.2) [7, 8]. Heterogeneity was assessed using the I2 statistic (low, I2 < 25%; moderate, 25% ≤ I2 ≤ 50%; I2 > 50%) [9, 10]. Small study effects were assessed using funnel plot asymmetry and Egger’s regression test [11]. P < 0.05 was considered to be statistically significant.

Risk of bias assessment

Two authors (K.S. and R.I.) independently assessed the risk of bias of the included RCTs using the Cochrane risk of bias tool. The RCTs were rated as having a low risk of bias, some concerns regarding bias, or a high risk of bias across the following domains: randomization process, changes in the intended intervention, missing outcome data, outcome measurements, and selection of the reported results. The overall risk of bias was rated as high if one or more of the evaluated domains were rated as high risk. The overall risk of bias was rated as low if all domains were rated as low risk. In the case of disagreements between the two authors, a consensus was reached via discussion. Risk of bias plots were created using robvis [12].

Results

Study selection

Figure 1 illustrates the study selection process. Nineteen of the 176 identified studies were considered potential candidates for inclusion in this systematic review [13–31]. Thirteen studies were excluded after the application of the inclusion and exclusion criteria: three studies were excluded based on the publication type [18–20], four studies were excluded based on the study design [13, 15–17], one study was excluded based on the setting [27], four studies were excluded based on the intervention [21, 26, 28, 30], and one study was excluded based on the comparator [23]. Two RCTs identified manually were added subsequently [32, 33]; thus, a total of eight RCTs were included in this systematic review [14, 22, 24, 25, 29, 31–33].Fig. 1 Flow chart of the study selection process. Abbreviations: CENTRAL, Cochrane Central Register of Controlled Trials; Embase, Excerpta Medica Database; IEEE, Institute of Electrical and Electronics Engineers Xplore; MEDLINE, Medical Literature Analysis and Retrieval System Online

Study characteristics

A total of 568 patients were included in the eight studies. Among them, 286 and 282 patients underwent AI-assisted and non-AI-assisted interventions, respectively (Table 1). The study by Schenk et al. [24] (n = 54) was a sub-study of the study by Wijnberge et al. [22] (n = 68). Thus, the number of patients included in each of these studies was counted twice. All studies were conducted in an operating room setting under general anesthesia. Only adult participants were included. One study included patients who underwent elective lumbar spinal surgery under induced hypotension anesthesia to reduce blood loss (Koo et al. [25], n = 68). One study included patients who underwent major elective thoracic surgeries under general anesthesia with one-lung ventilation (Šribar et al. [29], n = 34). Table 1 Characteristics of included studies

First author	Year	Country	Surgery	Anesthesia	Sample Size, n	Age, year: AI	Age, year: Control	Male, n (%): AI	Male, n (%): Control	AI-assisted	Control	
n	Intervention	n	Intervention	
Curatolo	1996	Switzerland	Discectomy for disc herniation	GA	30	43 (37–49)	41 (35–51)	8 (53.3)	7 (46.7)	15	Fuzzy logic control of gas concentrations	15	Manual control of gas concentrations	
Wijnberge	2020	Netherlands	Noncardiac surgery	GA	60	68 (61–73)	62 (56–67)	21 (67.7)	13 (44.8)	31	Hypotension Prediction Index	29	Standard care	
Maheshwari	2020	USA	Moderate- or high-risk noncardiac surgery	GA	213	67 ± 10	66 ± 10	58 (55.2)	65 (60.2)	105	Hypotension Prediction Index	108	Standard care	
Schneck	2020	Germany	Total hip arthroplasty	GA	49	66 (57–71)	60 (56–71)	12 (48.0)	13 (54.2)	25	Hypotension Prediction Index and flow monitoring	24	Standard care	
Schenk	2021	Netherlands	Noncardiac surgery	GA	54	69 (61–73)	62 (57–66)	19 (67.9)	11 (42.3)	28	Hypotension Prediction Index	26	Standard care	
Koo	2022	Korea	Lumbar spinal fusion surgery	GA with induced hypotension	68	64 ± 7	63 ± 9	11 (31.4)	16 (48.5)	35	Hypotension Prediction Index	33	Standard care	
Šribar	2023	Croatia	Major thoracic surgery	GA with one lung ventilation	34	60 (65–67)	62 (69–71)	10 (58.8)	8 (47.1)	17	Hypotension Prediction Index	17	Conventional pulse contour analysis (FloTrac™)	
Frassanito	2023	Italy	Gynecologic oncologic surgery	GA	60	55 (45–72)	59 (49–68)	0 (0.0)a	0 (0.0)a	30	Hypotension Prediction Index	30	Standard care (Goal Directed haemodynamic Therapy)	
Age is presented as mean ± standard deviation or median (interquartile range) unless otherwise indicated

Abbreviations: AI Artificial intelligence, GA General anesthesia, USA United States of America

aNot mentioned in the text, but listed as such since only gynecologic surgeries were included in this study

Interventions

Table 1 summarizes the interventions used in the included studies. Fuzzy logic control for gas concentrations was used during general anesthesia in one study (Curatolo et al. [14]). Fuzzy logic was used for control of inspired oxygen and isoflurane concentration. The Hypotension Prediction Index (Edwards Life Sciences Corporation, California, USA) was used for intraoperative monitoring in seven studies (Wijnberge et al. [22], Maheshwari et al. [32], Schneck et al. [33], Schenk et al. [24], Koo et al. [25], Šribar et al. [29], and Frassanito et al. [31]). The Hypotension Prediction Index was only used in addition to usual care in each study, with no specific instructions on how to respond to the prediction. All interventions, with the exception of fuzzy logic control, simply added one indicator to routine clinical practice.

Outcomes

Table 2 presents the outcomes. Table 2 Outcomes of included studies

First author	Type of AI	Intervention, n	Intraoperative TWA-AUTa, mmHg	Postoperative TWA-AUTa, mmHg	Number of hypotensive events per patient during surgery	Duration of intraoperative hypotension, %	Volume of intraoperative surgical blood loss, ml	Oxygen concentration within target range, %	
AI	C	AI	C	AI	C	AI	C	AI	C	AI	C	AI	C	
Curatolo	FL	15	15	NA	NA	NA	NA	NA	NA	0.0b (0.0–0.0)	0.0b (0.0–0.0)	NA	NA	82*c (76–91)	64*c (47–94)	
Wijnberge	HPI	31	29	0.10* (0.01–0.43)	0.44* (0.23–0.72)	NA	NA	NA	NA	2.8*d (0.8–6.6)	10.3*d (4.6–15.6)	NA	NA	NA	NA	
Maheshwari	HPI	105	108	0.14 (0.03–0.37)	0.14 (0.03–0.39)	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	
Schneck	HPI	25	24	NA	NA	NA	NA	NA	NA	0*e (0–1)	6*e (2–12)	700 (600–800)	550 (438–700)	NA	NA	
Schenk	HPI	28	26	NA	NA	0.07 (0.0–1.10)	0.23 (0.01–1.11)	NA	NA	NA	NA	NA	NA	NA	NA	
Koo	HPI	35	33	NA	NA	NA	NA	NA	NA	NA	NA	299.3* ± 219.8	532.0* ± 232.7	NA	NA	
Šribar	HPI	17	17	0.01* (0.0–0.08)	0.08* (0.02–0.22)	NA	NA	0.0*d (0.0–1.3)	2.0*d (0.7–3.3)	NA	NA	NA	NA	NA	NA	
Frassanito	HPI	30	30	0.14* (0.04–0.66)	0.77* (0.36–1.30)	NA	NA	2*d (1–5)	7*d (5–13)	2.7*d (0.9–4.2)	13.7*d (6.9–24.4)	NA	NA	NA	NA	
Continuous data are presented as mean ± standard deviation or median (interquartile range), unless otherwise indicated. *, statistically significant difference

Abbreviations: AI Artificial intelligence, C Control, FL Fuzzy logic, HPI Hypotension Prediction Index, NA Not available, TWA-AUT Time-weighted average of the area under the threshold

aTWA-AUT = (depth of hypotension in millimeters of mercury below a mean arterial pressure of 65 mmHg × time in millimeters of mercury below a mean arterial pressure of 65 mmHg) ÷ (total duration of operation (or observed time in the postanesthesia care unit) in minutes)

bSystolic blood pressure < 90 mmHg

cOxygen concentration within 28–32 vol.%

dMean arterial pressure < 65 mmHg

ePercentage of hypotension time during total anesthesia time

The time-weighted average of the area under the threshold intra- or postoperatively

The time-weighted average of the area under the threshold (TWA-AUT) was calculated in five studies that used the Hypotension Prediction Index [22, 24, 29, 31, 32]. TWA-AUT is defined as the area under the threshold divided by the total duration of surgery: TWA-AUT = (depth of hypotension in mmHg below a mean arterial pressure [MAP] of 65 mmHg × time in minutes below a MAP of 65 mmHg) / (total duration of operation in minutes) [22, 34]. However, Schenk et al. used “total observed time” rather than “total duration of surgery” for the calculation of TWA-AUT in the post-anesthesia care unit (PACU) [24]. TWA-AUT was found to be significantly lower in the Hypotension Prediction Index group than in the control group in the three studies that assessed TWA-AUT in the operating room (0.10 mmHg vs 0.44 mmHg by Wijnberge et al., 0.01 mmHg vs 0.08 mmHg by Šribar et al., and 0.14 mmHg vs 0.77 mmHg by Frassanito et al., the Hypotension Prediction Index vs control, respectively) [22, 29, 31]. However, the study by Maheshwari et al. [32], which had a larger sample size (n = 213), revealed no differences between the groups (0.14 mmHg vs 0.14 mmHg, P = 0.757). Similarly, the study by Schenk et al. [24], which was conducted in the PACU, revealed no significant differences (0.07 mmHg vs 0.23 mmHg, P = 0.295, the Hypotension Prediction Index vs control, respectively). Note that all values in this section are listed as medians.

Number of intraoperative hypotensive events per patient

Two studies (Šribar et al. [29] and Frassanito et al. [31]) that used the Hypotension Prediction Index evaluated the number of intraoperative hypotensive events per patient. The number of hypotensive events was significantly lower in the Hypotension Prediction Index group than that in the control group.

Duration of intraoperative hypotension

Four studies, comprising one study (Curatolo et al. [14]) that used fuzzy logic and three studies (Wijnberge et al. [22], Schneck et al. [33], and Frassanito et al. [31]) that used the Hypotension Prediction Index, evaluated the duration of intraoperative hypotension. The duration of intraoperative hypotension time was significantly lower in the Hypotension Prediction Index group than in the control group in the three studies that used the Hypotension Prediction Index. However, almost no differences were observed in the study that used fuzzy logic (durations of period of systolic blood pressure were 0% [0–0%] vs 0% [0–0%] for systolic blood pressure under 90 mmHg and 2% [0–9%] vs 1% [0–7%] for systolic blood pressure over 140 mmHg, fuzzy group vs control, respectively).

Volume of intraoperative blood loss

Schneck et al. [33] and Koo et al. [25] evaluated the volume of intraoperative blood loss. Koo et al. [25] compared the volume of blood loss under induced hypotension anesthesia with and without the Hypotension Prediction Index and revealed that the volume of blood loss was lower in the Hypotension Prediction Index group than control group (299.3 ± 219.8 mL vs 532.0 ± 232.7 mL). Note that Koo et al. did not evaluate hypotension, which is supposed to be the original purpose of the Hypotension Prediction Index. However, by Schneck et al. [33], no significant difference was observed between the groups.

Percentage of oxygen concentration within the target range

Curatolo et al. [14] compared the percentage of oxygen concentration within the target range with and without the use of fuzzy logic and revealed that fuzzy logic control of oxygen concentration is superior to manual control. In this study, isoflurane concentrations were also controlled by fuzzy logic, but there was no comparison with the control group with respect to isoflurane.

Meta-analysis

For intraoperative TWA-AUT and duration of intraoperative hypotension, meta-analyses were conducted because 3 or more studies measured the same outcome for the same intervention (Fig. 2).Fig. 2 Forest plot and funnel plot of meta-analyses of each outcome. Abbreviations: AI, artificial intelligence; HPI, Hypotension Prediction Index. a, b Forest plot and funnel plot of meta-analysis of intraoperative time-weighted average of the area under the threshold with vs without the Hypotension Prediction Index. c, d Forest plot and funnel plot of meta-analysis of duration of intraoperative hypotension with vs without the Hypotension Prediction Index

Intraoperative TWA-AUT

Wijnberge et al. [22], Maheshwari et al. [32], Šribar et al. [29], and Frassanito et al. [31] used the Hypotension Prediction Index as interventions and measured the intraoperative TWA-AUT. There was no significant difference between the Hypotension Prediction Index group and the control group (mean difference 0.22, 95% CI -0.03 to 0.48, P = 0.086; mean difference > 0 indicates the superiority of the Hypotension Prediction Index; Fig. 2a). The heterogeneity between the studies was high, with I2 of 93.8%. Figure 2b shows the funnel plot. Egger’s regression test did not show small study effects but the point estimate of intercept was large (intercept 29.15, 95% CI -40.75 to 99.04, P = 0.215).

Duration of intraoperative hypotension

Wijnberge et al. [22], Schneck et al. [33], and Frassanito et al. [31] used the Hypotension Prediction Index as an intervention and measured the duration of intraoperative hypotension. The mean difference was 7.41%, which indicates the superiority of the Hypotension Prediction Index (95% CI 4.95 to 9.86, P < 0.001; Fig. 2c). The heterogeneity between the studies was low, with I2 of 0.0%. The funnel plot was shown in Fig. 2d. Egger’s regression test showed no publication bias (intercept 0.92, 95% CI -0.95 to 2.78, P = 0.101).

Risk of bias

Figure 3 presents the results of the risk of bias assessment. Two studies (Curatolo et al. [14] and Šribar et al. [29]) were categorized as having a high risk of bias owing to inappropriate randomization processes or the presence of multiple primary outcomes. The remaining six studies were considered to have a low risk of bias.Fig. 3 Risk of bias. A Summary plot of risk of bias. B Risk of bias of each study

Discussion

This systematic review explored the impact of AI-assisted interventions, such as fuzzy logic and Hypotension Prediction Index, on anesthesia-related outcomes (hypotension, blood loss, and the accuracy of oxygen concentration). The findings of this review suggest that some small studies reported promising results, whereas the results of the meta-analysis with the largest sample study showed no significant differences in hypotension-related outcomes.

The interventions used in this systematic review involved the addition of a single measure to routine anesthetic management, with the exception of the fuzzy logic study by Curatolo et al. [14]. Anesthesiologists make decisions based on many considerations in routine clinical practice, including the patient characteristics, multiple vital signs and their trends, medication status, and surgical progress (e.g. appropriate management goals vary according to patient characteristics, comorbidities, and surgical procedure and its progress; depth of anesthesia may be intentionally deepened before a highly invasive procedure, or conversely, the depth may be decreased toward awakening). Thus, a new single metric would have a limited impact in the case of anesthesiologists managing their day-to-day clinical practice without AI assistance.

The following is a summary of the characteristics of each intervention.

Fuzzy logic

Fuzzy logic was mainly studied in the 1990s in the field of anesthesiology and is widely used in household appliances, such as washing machines and microwave ovens [13, 35]. Rather than regulating whether the cut-off value is on or off, fuzzy logic defines an intermediate state. For instance, fuzzy logic does not interpret 99 mmHg as “low” or 100 mmHg as “normal.” The values are divided into fuzzy sets, and each value can be categorized into one or more sets. For instance, 85 mmHg can be categorized as 75% to “low” as well as 25% to “normal” [35]. This enables computers to understand and respond to imprecise information.

Curatolo et al. [14] used fuzzy logic to regulate the oxygen and isoflurane concentrations intraoperatively and reported that the concentration control in the fuzzy logic group was superior to that in the manual control group. Some studies that attempted to apply fuzzy logic to the management of intraoperative blood pressure were identified during the search [13, 15]. However, they were excluded as they did not meet the inclusion criteria of this systematic review. Thus, only one RCT using fuzzy logic was included. The concept of fuzzy logic is compatible with the intraoperative management of anesthesia; therefore, although it is an old method, it may be worth revisiting with an appropriate study design.

Hypotension prediction index

The Hypotension Prediction Index is a machine learning-based technology [36] trained using the arterial pressure waveform data of 1334 of the 1684 patients included in the historical database that comprised intensive care unit and operating room data. It was internally validated using the data of the remaining 350 patients. External validation was performed using the data of 204 patients in the operating room. The machine learning mechanism was a logistic regression analysis. The objective variables were hypotensive event and non-event samples: the event sample included data from 5, 10, 15, and 20 min before the hypotensive episode (MAP < 65 mmHg for at least 1 min), whereas the non-event sample included data at least 20 min away from the hypotensive episode (MAP > 75 mmHg). The non-event sample is the midpoint of a 30-min hypotensive episode. Multiple data points extracted from the arterial pressure waveform data at the corresponding time points were used as explanatory variables. The 0–1 prediction obtained from the logistic regression analysis was multiplied by 100 for scaling [36].

Regarding the Hypotension Prediction Index, differences in results were observed between the included studies. Four small studies with the Hypotension Prediction Index reported improved intraoperative hypotension-related outcomes in the Hypotension Prediction Index groups [22, 29, 31, 33]. The meta-analysis of these studies also showed that the duration of intraoperative hypotension was significantly lower in the Hypotension Prediction Index group than that in the control group. These findings suggest that the Hypotension Prediction Index could be useful for intraoperative anesthetic management. However, no significant difference was observed in intraoperative TWA-AUT in the study by Maheshwari et al. [32], which had the largest sample size. The authors considered that this result could be attributed to clinicians largely ignoring this unfamiliar alert [37]. The meta-analysis including this largest study also showed no significant difference in intraoperative TWA-AUT. Although the Egger's regression test of the meta-analysis showed that the intercept was not significantly larger than zero (intercept 29.15, 95% CI -40.75 to 99.04, P = 0.215), the small study effects could not be excluded, and the presence of publication bias was suspected because (i) the intercept of Egger’s regression model was large, (ii) only four studies were included in the analysis, and (iii) the funnel plot was asymmetric. Therefore, taking these findings together, the usefulness of the Hypotension Prediction Index may not yet be reliable.

There may be several possible avenues for improvement in this index: (i) this index does not suggest further measures to be taken if the index increases; (ii) the algorithm is based on a simple logistic regression model for complex situations; (iii) data regarding blood pressure and arterial pressure waveform are used only at a few selected time points; and (iv) blood pressure data are replaced by binary variables in the analysis phase, and the degree of hypotension and time information are not used effectively [36]. It should be also noted that there are concerns in terms of evaluating the performance of the Hypotension Prediction Index [38]. Correcting these problems may facilitate the construction of a model that has improved predictive performance and might be able to indicate what interventions should be used [4, 39, 40].

Studies on the use of AI in the field of anesthesiology have increased recently [41, 42]. However, only eight RCTs on AI interventions were included in this systematic review. A PubMed search found 64 results using our search formula again (7 August, 2024), while replacing "AND ("Randomized Controlled Trial"[Publication Type])" at the end of the search formula with "AND ("observational" OR "retrospective" OR "simulation")" at the end of the search formula found 1,009 results. Thus, it is clear that perioperative studies using AI are biased toward non-interventional studies. This situation may indicate that the use of AI has been limited to data analysis and model building, and its usefulness in actual clinical practice has not yet been evaluated. Although in the field of basic research, systems have been established to introduce new drugs in clinical practice [43], there are a few clinicians who can translate between the computational aspects of model building and the clinical insights around the problem to be solved and then integrate the model into the clinical workflow [44]. Thus, the RCTs are less likely to be conducted in the field of AI at this point. However, AI could potentially be applied at any point in the perioperative period, as it has been widely studied in pre-operative, intra-operative, post-operative, and operating room managements [45]. Therefore, it is necessary to create a system in the field of data science to verify model building in real-world settings.

This study has certain limitations. First, two high risk of bias studies were included. Second, arbitrary elements might influence the outcomes of some studies that were categorized into the “low-risk” group, as the intervention was not blinded [46]. Lastly, meta-analyses were performed, but caution should be exercised in interpreting the results because of the high heterogeneity among the studies. There were also some differences in the participants, interventions, and controls. However, due to the small number of included studies, no additional subgroup analyses were performed. At this stage, the results from our meta-analyses should be used as reference values, due to the insufficient number of studies evaluated.

Conclusions

This systematic review and meta-analysis found that only a few high-quality RCTs comparing interventions with and without AI assistance in anesthetic management have been conducted. Future RCTs of AI-assisted anesthesia interventions that take into account complex clinical situations are necessary.

Supplementary Information

Additional file 1. Step-wise approach to the search strategy development.

Additional file 2.

Abbreviations

AI Artificial intelligence

CENTRAL Cochrane central register of controlled trials

CI Confidence interval

CISA Combined index of stimulus and analgesia

Embase Excerpta medica database

IEEE Institute of electrical and electronics engineers xplore

MAP Mean arterial pressure

MEDLINE Medical literature analysis and retrieval system online

NRS Numerical rating scale

PACU Post-anesthesia care unit

PRISMA Preferred reporting items for systematic reviews and meta-analyses

PROSPERO International prospective register of systematic reviews

RCT Randomized controlled trial

SD Standard deviation

TWA-AUT The time-weighted average of the area under the threshold

Acknowledgements

Not applicable.

Authors’ contributions

KS helped handle this manuscript, designing the study, and conducting the review and meta-analysis. RI helped in designing the study, conducting the review, and drafting the manuscript. TO helped in designing the study, conducting the meta-analysis, and drafting the manuscript. MI helped design the study and write the manuscript. MG helped in designing the study, conducting the meta-analysis, and drafting the manuscript. MT helped design the study and write the manuscript. NT helped design the study and write the manuscript. All authors read and approved the final manuscript.

Funding

None.

Availability of data and materials

Data sharing is not applicable to this article as no datasets were generated or analyzed during the current study.

Data availability

No datasets were generated or analysed during the current study.

Declarations

Ethics approval and consent to participate

Not applicable.

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests.

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
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