Artificial Intelligence Will (MAY) Make Doctors Expendable (IN GOOD WAYS): Pro

Hung, Andrew J.; Chen, Andrew B.; Cacciamani, Giovanni; Gill, Inderbir S.

doi:10.1016/j.euf.2021.03.011

Cited by 8 publications

(4 citation statements)

References 6 publications

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“…AI has been used successfully to measure surgical efficiency and improve skills [ 34 , 35 , 36 , 37 , 37 , 38 ]. Specifically, CNNs use feature extraction to provide objective feedback for trainees on robotic skills such as suturing, knot tying, and needle passing with accuracy in the 90s [ 39 ], and entropy-based models achieves similar levels of success in the automated scoring of suturing and knot-tying [ 40 ].…”

Section: Discussionmentioning

confidence: 99%

Automated Capture of Intraoperative Adverse Events Using Artificial Intelligence: A Systematic Review and Meta-Analysis

Eppler

Sayegh

Maas

et al. 2023

JCM

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Intraoperative adverse events (iAEs) impact the outcomes of surgery, and yet are not routinely collected, graded, and reported. Advancements in artificial intelligence (AI) have the potential to power real-time, automatic detection of these events and disrupt the landscape of surgical safety through the prediction and mitigation of iAEs. We sought to understand the current implementation of AI in this space. A literature review was performed to PRISMA-DTA standards. Included articles were from all surgical specialties and reported the automatic identification of iAEs in real-time. Details on surgical specialty, adverse events, technology used for detecting iAEs, AI algorithm/validation, and reference standards/conventional parameters were extracted. A meta-analysis of algorithms with available data was conducted using a hierarchical summary receiver operating characteristic curve (ROC). The QUADAS-2 tool was used to assess the article risk of bias and clinical applicability. A total of 2982 studies were identified by searching PubMed, Scopus, Web of Science, and IEEE Xplore, with 13 articles included for data extraction. The AI algorithms detected bleeding (n = 7), vessel injury (n = 1), perfusion deficiencies (n = 1), thermal damage (n = 1), and EMG abnormalities (n = 1), among other iAEs. Nine of the thirteen articles described at least one validation method for the detection system; five explained using cross-validation and seven divided the dataset into training and validation cohorts. Meta-analysis showed the algorithms were both sensitive and specific across included iAEs (detection OR 14.74, CI 4.7–46.2). There was heterogeneity in reported outcome statistics and article bias risk. There is a need for standardization of iAE definitions, detection, and reporting to enhance surgical care for all patients. The heterogeneous applications of AI in the literature highlights the pluripotent nature of this technology. Applications of these algorithms across a breadth of urologic procedures should be investigated to assess the generalizability of these data.

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Section: Discussionmentioning

confidence: 99%

Automated Capture of Intraoperative Adverse Events Using Artificial Intelligence: A Systematic Review and Meta-Analysis

Eppler

Sayegh

Maas

et al. 2023

JCM

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“…Автоматизированная хирургия может стать возможной в отдаленном будущем, но, как сообщили N. Heller и C. Weight [44], ИИ в настоящее время способен выполнять небольшие автоматизированные хирургические этапы при урологических процедурах. Сотрудничество «человек + машина» было бы желательно, по крайней мере частично, чтобы облегчить нагрузку на системы здравоохранения, уменьшить «выгорание» специалистов, повысить удовлетворенность пациентов и улучшить способы использования «человеческого» интеллекта для улучшения жизни пациентов [45]. Впечатляющим примером такого взаимодействия стала разработка автономного робота STAR, который впервые в мире выполнил кишечный анастомоз в эксперименте на лабораторном животном, что знаменует собой шаг на пути к полностью автоматизированной хирургии у человека [46].…”

Section: хирургическая метавселеннаяunclassified

Metaverse: a new reality in surgery. Review

Kozlov

2024

Russian Journal of Pediatric Surgery, Anesthesia and Intensive

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The metaverse is a relatively new concept in technological advancement. It is a three-dimensional immersive environment including real and virtual world elements, in which various digital agents, more precisely, avatars, interact with each other and various activities (educational, cultural, and financial) can be carried out. It is an emerging controversial concept without precise definition and will likely continue to evolve as technology improves. Works of literature from 2020 to 2023, posted in the PubMed and Web of Science databases, were extracted using the following keywords: “metaverse”, “education”, “digital therapy”, “interdisciplinary consultation”, “medical Internet of things”, “virtual reality”, “superimposed reality”, “extended reality”, “mixed reality”, “lifelogging”, “mirror world”, “digital twins”, and “surgery”. In the PubMed search using the original terms, no publications were extracted in 2020, 9 in 2021, 161 in 2022, and 246 in 2023. In the healthcare context, metaverse technology refers to the use of virtual reality and interactive and other immersive technologies, such as augmented reality, to create simulated environments intended for training, education, and clinical applications. Various aspects of medicine such as digital therapeutics, multidisciplinary team discussions, medical Internet of things, surgical simulations, conferences and meetings, research programs, etc., are being explored. Depending on the requirements, the metaverse provides a versatile platform that can be modeled accordingly, providing a flexible tool for medical development. In this review, we discussed in detail the possible applications of the metaverse in surgery. All scoping rubrics are based on the sparse evidence in the scientific literature and are adequately evaluated to provide compelling evidence on the utility of the metaverse in the future of medicine.

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“…Applications of artificial intelligence have continued to grow in medicine. In the present, the availability of data in urologic surgery and improvement in artificial intelligence and machine learning algorithms allows for further growth of automated assessment in the determination of surgical skills [10 ▪ ,11]. Artificial intelligence serves as the perfect tool for addressing the inadequacies of current evaluation methods with the potential to further differentiate surgical skill and technique.…”

Section: Introductionmentioning

confidence: 99%

How the use of the artificial intelligence could improve surgical skills in urology: state of the art and future perspectives

Cacciamani

Anvar²,

Chen³

et al. 2021

Current Opinion in Urology

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Purpose of review As technology advances, surgical training has evolved in parallel over the previous decade. Training is commonly seen as a way to prepare surgeons for their day-to-day work; however, more importantly, it allows for certification of skills to ensure maximum patient safety. This article reviews advances in the use of machine learning and artificial intelligence for improvements of surgical skills in urology. Recent findings Six studies have been published, which met the inclusion criteria. All articles assessed the application of artificial intelligence in improving surgical training. Different approaches were taken, such as using machine learning to identify and classify suturing gestures, creating automated objective evaluation reports, and determining surgical technical skill levels to predict clinical outcomes. The articles illustrated the continuously growing role of artificial intelligence to address the difficulties currently present in evaluating urological surgical skills. Summary Artificial intelligence allows us to efficiently analyze the surmounting data related to surgical training and use it to come to conclusions that normally would require human intelligence. Although these metrics have been shown to predict surgeon expertise and surgical outcomes, evidence is still scarce regarding their ability to directly improve patient outcomes. Considering this, current active research is growing on the topic of deep learning-based computer vision to provide automated metrics needed for real-time surgeon feedback.

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Artificial Intelligence Will (MAY) Make Doctors Expendable (IN GOOD WAYS): Pro

Cited by 8 publications

References 6 publications

Automated Capture of Intraoperative Adverse Events Using Artificial Intelligence: A Systematic Review and Meta-Analysis

Automated Capture of Intraoperative Adverse Events Using Artificial Intelligence: A Systematic Review and Meta-Analysis

Metaverse: a new reality in surgery. Review

How the use of the artificial intelligence could improve surgical skills in urology: state of the art and future perspectives

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