Artificial Intelligence in Anesthesiology

Hashimoto, Daniel A.; Witkowski, Elan R.; Gao, Lei; Meireles, Ozanan R.; Rosman, Guy

doi:10.1097/aln.0000000000002960

Cited by 316 publications

(204 citation statements)

References 98 publications

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“…Artificial intelligence has been advancing in anesthesiology, including depth of anesthesia monitoring, control infusion, or pain management [33]. However, despite decades of research on optimal pain management, postoperative pain continues to be inadequately treated [34,35].…”

Section: Discussionmentioning

confidence: 99%

Pain Detection with Bioimpedance Methodology from 3-Dimensional Exploration of Nociception in a Postoperative Observational Trial

Neckebroek

Ghita

et al. 2020

JCM

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Although the measurement of dielectric properties of the skin is a long-known tool for assessing the changes caused by nociception, the frequency modulated response has not been considered yet. However, for a rigorous characterization of the biological tissue during noxious stimulation, the bioimpedance needs to be analyzed over time as well as over frequency. The 3-dimensional analysis of nociception, including bioimpedance, time, and frequency changes, is provided by ANSPEC-PRO device. The objective of this observational trial is the validation of the new pain monitor, named as ANSPEC-PRO. After ethics committee approval and informed consent, 26 patients were monitored during the postoperative recovery period: 13 patients with the in-house developed prototype ANSPEC-PRO and 13 with the commercial device MEDSTORM. At every 7 min, the pain intensity was measured using the index of Anspec-pro or Medstorm and the 0–10 numeric rating scale (NRS), pre-surgery for 14 min and post-anesthesia for 140 min. Non-significant differences were reported for specificity-sensitivity analysis between ANSPEC-PRO (AUC = 0.49) and MEDSTORM (AUC = 0.52) measured indexes. A statistically significant positive linear relationship was observed between Anspec-pro index and NRS (r2 = 0.15, p < 0.01). Hence, we have obtained a validation of the prototype Anspec-pro which performs equally well as the commercial device under similar conditions.

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Section: Discussionmentioning

confidence: 99%

Pain Detection with Bioimpedance Methodology from 3-Dimensional Exploration of Nociception in a Postoperative Observational Trial

Neckebroek

Ghita

et al. 2020

JCM

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“…AI has surpassed humans in several medical areas, such as disease diagnosis based on medical or pathological images and disease activity monitoring for atrial fibrillation and epilepsy relapse (24)(25)(26)(27)(28)(29). Pioneering work of AI applications in anesthesiology has been carried out in several aspects, including anesthesia depth monitoring, control of anesthesia, risk prediction, and logistics management (30). In pain management, AI has been used to select patients who may benefit from preoperative pain consultation (31).…”

Section: Ai-pca: a Promising Future Analgesia Directionmentioning

confidence: 99%

From Patient-Controlled Analgesia to Artificial Intelligence-Assisted Patient-Controlled Analgesia: Practices and Perspectives

Wang

Duan

et al. 2020

Front. Med.

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Pain relief is a major concern for patients who have undergone surgery, and it is an eternal pursuit for anesthesiologists. However, postoperative pain management is far from satisfactory, though the past decades have witnessed great progress in the development of novel analgesics and analgesic techniques. A Cochrane systematic review showed that patient-controlled analgesia (PCA) achieved better pain relief and greater patient satisfaction than traditional "on-demand" parenteral analgesia, suggesting that it might be the manner of analgesia implementation that matters for effective postoperative pain management. A wireless intelligent PCA (Wi-PCA) system that incorporated remote monitoring, an intelligent alarm, intelligent analysis and assessment of the PCA equipment, as well as automatically recording and reserving key information functions under a wireless environment was introduced in our department in 2018. The practice showed that the Wi-PCA system significantly reduced the incidence of moderate to severe postoperative pain and relevant adverse effects, shortened hospital stays, and improved patient satisfaction with postoperative pain relief. Nevertheless, for both traditional and Wi-PCA, analgesics are only administered when pain occurs, leaving behind a realm of possibilities for better postoperative pain management. With the rapid development of machinery and deep learning algorithms, artificial intelligence (AI) is changing the mode of clinical decision making. Integrating the big data collected by state-of-the-art monitoring sensors, the Internet of Things and AI algorithms, an AI-assisted PCA (Ai-PCA) may be a promising future direction for postoperative pain management.

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“…AI-based techniques like machine learning can be used to gather data from multiple sources for processing, which can provide novel insights. We have discussed how AI's ability to analyze large amounts of data swiftly makes it possible to use the data to predict the likelihood of new outbreaks, model successful disease containment strategies, and to find the most effective treatment protocols [9]. However, there are some other areas of application that have yet to be explored.…”

Section: Combination With Big Datamentioning

confidence: 99%

“…AI implies the use of a computer to model intelligent behavior without human intervention [8]. It has been applied to many areas of medicine [9], especially to aid the detection and prevention of disease [10]. AI techniques being used in medicine are broad, ranging from computer vision to deep learning techniques [11].…”

Section: Introductionmentioning

confidence: 99%

Artificial Intelligence for COVID-19: Rapid Review (Preprint)

Chen¹,

See²

2020

Preprint

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BACKGROUND Coronavirus Disease 2019 (COVID-19) was first discovered in December 2019 and has since evolved into a pandemic. To address this global health crisis, artificial intelligence (AI) has been deployed at various levels of the healthcare system. OBJECTIVE However, AI has both potential benefits and limitations. We therefore conducted a comprehensive review of AI applications for COVID-19. METHODS We performed an extensive search of the PubMed and Embase databases for COVID-19-related English-language studies published between 1/12/2019 and 31/3/2020. We supplemented the database search with reference list checks. Thematic analysis and narrative review of AI applications for COVID-19 documented was conducted. RESULTS 11 papers were included for review. AI was applied to COVID-19 in four areas: diagnosis, public health, clinical decision-making, and therapeutics. We identified several limitations including insufficient data, omission of multimodal methods of AI-based assessment, delay in realization of benefits, poor internal/external validation, inability to be used by laypersons, inability to be used in resource-poor settings, presence of ethical pitfalls and presence of legal barriers. AI could potentially be explored in four other areas: surveillance, combination with big data, operation of other core clinical services, and management of COVID-19 patients. CONCLUSIONS In view of the continuing increase in infected cases, and given that multiple waves of infections may occur, there is need for effective methods to help control the COVID-19 pandemic. Despite its shortcomings, AI holds the potential to greatly augment existing human efforts, which may otherwise be overwhelmed by large patient numbers.

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Artificial Intelligence in Anesthesiology

Cited by 316 publications

References 98 publications

Pain Detection with Bioimpedance Methodology from 3-Dimensional Exploration of Nociception in a Postoperative Observational Trial

Pain Detection with Bioimpedance Methodology from 3-Dimensional Exploration of Nociception in a Postoperative Observational Trial

From Patient-Controlled Analgesia to Artificial Intelligence-Assisted Patient-Controlled Analgesia: Practices and Perspectives

Artificial Intelligence for COVID-19: Rapid Review (Preprint)

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