1. IntroductionIn recent years, the rapid development of precision medicine has led to significant improvements in diagnosing and treating many diseases [1]. However, overcoming the interference of patient heterogeneity, such as complex primary diseases and multiple risk factors, has become an obstacle to the development of precision medicine in critical care medicine (CCM) [2,3]. Accordingly, ensuring that the commonalities are identified may be an effective strategy for addressing this problem [4]. Patients in intensive care units (ICUs) are continuously monitored and treated, generating various real-time data, including manually recorded and digitally obtained data, such as temperature, urine volume, oxygen saturation, etc. These data should be considered in combination to assist in clinical decision-making. However, too much information increases the difficulty of data interpretation, especially when data conflicts. Identifying which data is more reliable and reflects the commonality confuses clinicians [5]. This makes ICUs the best field for embracing Big Data.Artificial intelligence (AI), with a better capacity to handle big data than the human brain, has been of great aid in clinical decision-making [6,7], contributing to the practical success of precision medicine in CCM. In 1981, logistic regression was implemented to verify the validity of the APACHE score [8], which was the initial application of AI in aiding clinical decision-making in CCM. Since then, AI has become widely recognized in digging clinical evidence [9,10]. Researchers have begun to realize that data-processing capabilities are essential for CCM [9,11], and many applications of AI have been carried out. Electronic health records (EHRs), integrated with various types of medical information, are the basis of evidence-based medicine. This data volume is ideal for AI models, which thrive on large datasets [9]. The widespread adoption of EHRs brought the data science world to the patient’s bedside and paved the way for data-based AI models [11]. The MIMIC database is a perfect example of integrated EHR data. The MIMIC is an extensive, freely available database comprising health-related data from patients admitted to the critical care units of the Beth Israel Deaconess Medical Center from 2001 to 2019 [12,13]. Several specific disease/complication evaluation models have been generated from the MIMIC database to promote decision-making [14]. The implementation of AI models for infectious diseases with unknown pathogens initially, such as sepsis and COVID-19, has made significant progress in the early prediction, diagnosis, and exploration of early treatment strategies [15] and remains crucial for the foreseeable future.Bibliometrics is a field of quantitative science that analyzes the scientific literature to provide an overview of a certain topic [16]. It could reveal the current hotspots and future directions in a certain area by bibliometric methods. Although there has been an increasing focus on AI in CCM, a detailed analysis of the development and trends in AI in CCM has not yet been carried out. Additionally, with the announcement of the research initiative on precision medicine in 2015, the “-omics” technology has been valued for its identification of complex biological mechanisms [17]. However, literature that provides what happened and is happening with a combination of omics and AI in CCM is still lacking. In this study, we aimed to reveal current trends of AI in CCM by analyzing the hot topics and reflecting on the current status of the application of omics with AI in CCM. Specifically, AI studies were retrieved in the field of CCM between January 2010 and June 2021, and the varying trends in published studies of AI in CCM were analyzed. We hope that our survey will further deepen the critical care experts’ understanding of AI in CCM and promote the commonality mining for critical illnesses. 4. Discussion

This study found that the number of articles related to AI in CCM has been growing over the years, particularly with a fold increase from 2018 to 2020. The quantity as well as quality of the articles is overwhelming. The top 10 active journals were JCR Q1. This suggests that AI is gaining increasing attention in CCM. In addition, the USA is in a dominant position in this field, with the most published articles and the top 10 active affiliations. China’s publication of AI-related studies in CCM ranks second, suggesting that current research in this field is in its infancy in China. Moreover, it was observed that research often focused on common diseases in the ICU, such as sepsis, pneumonia, and acute kidney injury. Additionally, more than 90% of articles depended on traditional data types, such as EHRs and waveforms (including ECG, EEG, arterial pulse waveforms, and other waveform data), which was possibly due to data accessibility.

Unfortunately, the importance of “-omics” technologies in precision medicine has not been fully recognized by critical care physicians and is not widely applied in AI modeling (Figure 4B). High-throughput sequencing coordinated with in-depth biological information can reveal detailed differences between individual health and disease statuses [18]. Different omics data provide different but complementary biological information from different biological layers, and these can be integrated by multi-omics studies to offer a more comprehensive view of complex diseases which are common in ICU. Publications based on “multi-omics and machine learning or deep learning” keywords have started to emerge and become popular in the recent 5 years [19]. Most of the studies focus on cancer [19], and COVID-19 is a hot topic of critical care medicine in this area with a focus on the prediction of the severity and exploration of the mechanism of COVID-19 [20,21]. However, the number of related publications is smaller compared with other data types and most of them only use individual omics according to our study. One possible reason could be the belief that “-omics” technologies are quite immature to provide reliable results. Additionally, this field lacks standard operating procedures for data acquisition, integration, and analysis. Despite the limitations of omics itself, there are still some challenges during the application of omics in AI models. For example, the imbalanced omics dataset caused by rare disease classes may lead to an overfitted model [22,23]. In addition, the classical “curse of dimensionality” problem is inevitable [24]. Omics technologies provide large amounts of raw data, resulting in computationally intensive methods and likely misleading algorithm training [19]. Reduced dimensional data could show the interaction among different omics but may also lead to the ignorance of weak signals and missing information [22,25,26]. These factors impede the generalization of “-omics” technologies in intensive care medicine, particularly at community hospitals. This suggests that there is a lot of work to be done before achieving precision medicine in intensive care medicine.However, such concerns should not obscure the progress of AI using traditional data types, which performed superior accuracy and earlier prediction compared with empirical clinical decisions. In 2015, Pirracchio et al. provided a new mortality prediction algorithm for ICU patients using the implementation of a super learner [27]. They found that two super learner prediction models (SL1 and SL2) offered better performance for mortality prediction (cv-AUROC = 0.85, 0.88, respectively) than the SOFA score (cv-AUROC = 0.71) and SAPS II score (cv-AUROC = 0.78). AI also performs better in early prediction. According to Wickramaratne et al.’s work, sepsis can be predicted 6 h in advance with their model (AUROC = 0.97) [28]. In 2018, Meyer et al. used deep machine learning methods (recurrent neural networks) to predict real-time severe complications [29]. It was shown to be more accurate than conventional clinical reference tools, enhancing the absolute complication prediction AUC by 0.29 for bleeding, 0.24 for mortality, and 0.24 for renal failure. These studies suggest that with the development of new technology and the iteration of the algorithm, AI with more training data will more accurately assess the prognosis than today’s various scores in CCM [30]. AI also plays an irreplaceable role in diseases with unknown pathogens, such as COVID-19. The COVID-19 outbreak has spread globally and placed tremendous pressure on healthcare resources. AI provides an effective and efficient strategy to combat the COVID-19 pandemic [15,31,32]. Jiao et al. developed an AI system to predict the prognosis of patients with COVID-19 based on chest X-rays [33]. The model showed a significantly better prognostic performance than the severity scores on both internal (C-index 0.805 vs. 0.781) and external testing (C-index 0.752 vs. 0.715).Along with the progress of AI in medicine, more and more AI products/devices are approved by the U.S. Food and Drug Administration (FDA). Some products may be helpful for healthcare, such as some image analysis software (for example, DeepRhythmAI by Medicalgorithmics SA) and basic cardiopulmonary function monitoring software (such as IRNF App by Apple lnc and Air Next by NuvoAir AB), etc. [34]. However, we still lack products specifically for critically ill patients. More targeted AI devices for patients in ICU are expected.Meanwhile, the challenges of implementing AI in the ICU have captured public attention [11]. AI application is generally associated with barriers concerning data collection/management and the development/generalization of models. The most important aspect that should be considered is data sharing [35,36]. ICU data were shared among hospitals, however, using such sensitive information and sharing this data experiences several difficulties, such as privacy concerns, ethical considerations, attribution issues, laws, and regulations. Ensuring data security hampers the progress of AI from this angle. Therefore, an international consensus is urgently needed. Furthermore, it should be noted that AI is not always accurate. Some researchers have raised the technical challenges of machine learning in CCM, such as the difficulty of manually calibrating and adjusting models calculated using AI and whether this will affect their applicability [37]. In addition, inaccurate data re-entry irreparably damages the continuous learning model and consequently affects its localization [38,39]. This may have led to incorrect clinical decisions. Thus, physicians need to make decisions with personal experience and should not rely excessively on AI; be a good master, but not a slave to AI.Admittedly, it is believed that a continuous learning algorithm can enhance its effectiveness [40]. It would be convenient for real-time updates regarding localization and to improve accuracy. However, the principle of machine learning methods is not yet fully understood; therefore, we can use the fundamental computing model as the first auxiliary clinical judgment. Data can be periodically input in the backup after professional supervision [38]. If the supervision team detects abnormal data, it can select and remove them in time.It is believed that AI models should serve as an aid but not as a replacement for clinical judgment. However, scientists who use AI may obtain more valuable information than those who do not. Although AI may not be fully accurate owing to the live streaming data and different models, it can be used as a reference, similar to clinical scores, to guide clinical decision-making. The iteration of the algorithm improves the credibility and reliability of AI models and resolves the aforementioned problems [41].

In conclusion, our study has provided an overview of the AI field in CCM and has revealed the development status and main research topics in this area. Through the analysis, we also identify new perspectives for future research, for instance, the combination of “-omics” technology and AI models. We hope our study could provide some valuable information to researchers, considering the possibility of using existing data for AI, organizing disciplinary teams, and getting a better design of clinical trials. We believe that with a combination of personal experience and proper AI models, AI will show its great power in achieving precision medicine in CCM.