Hey Siri! Perform a type 3 hysterectomy. Please watch out for the ureter!” What is autonomous surgery and what are the latest developments?

Gültekin, İsmail Burak; Karabük, Emine; Köse, Mehmet

doi:10.4274/jtgga.galenos.2021.2020.0187

Cited by 5 publications

(4 citation statements)

References 13 publications

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“…24 An earlier study looked at reported cases against Intuitive Surgical, maker of the daVinci system, from 2000-2017. 28 Of the 108 claims in the study, 62% were gynecologic surgeries. Of these claims, 35% were dismissed, but "no other information regarding settlements or trial outcomes was available."…”

Section: Liabilitymentioning

confidence: 99%

Liability in robotic gyn surgery

Sanfilippo¹

2023

OBGM

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Section: Liabilitymentioning

confidence: 99%

Liability in robotic gyn surgery

Sanfilippo¹

2023

OBGM

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“…This designation of the term ‘robot’ is rather incorrect, but unfortunately, the name has stuck. Therefore, when developing an autonomous surgical system, it is better to define the term ‘autonomy’ to avoid misnomers ( 11 ). Han et al and Yang et al proposed six frameworks describing the levels of autonomy for medical robots, similar to that for autonomous vehicles.…”

Section: Autonomous Surgical Robotsmentioning

confidence: 99%

“…In recent years, the number of studies on the use of deep learning to analyze surgical videos and apply them to medical care has increased ( 9 , 10 ), and a growing number of studies on the development of autonomous surgical robots have been published ( 11 , 12 ).…”

Section: Introductionmentioning

confidence: 99%

Evolution of a surgical system using deep learning in minimally invasive surgery (Review)

et al. 2023

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Recently, artificial intelligence (AI) has been applied in various fields due to the development of new learning methods, such as deep learning, and the marked progress in computational processing speed. AI is also being applied in the medical field for medical image recognition and omics analysis of genomes and other data. Recently, AI applications for videos of minimally invasive surgeries have also advanced, and studies on such applications are increasing. In the present review, studies that focused on the following topics were selected: i) Organ and anatomy identification, ii) instrument identification, iii) procedure and surgical phase recognition, iv) surgery-time prediction, v) identification of an appropriate incision line, and vi) surgical education. The development of autonomous surgical robots is also progressing, with the Smart Tissue Autonomous Robot (STAR) and RAVEN systems being the most reported developments. STAR, in particular, is currently being used in laparoscopic imaging to recognize the surgical site from laparoscopic images and is in the process of establishing an automated suturing system, albeit in animal experiments. The present review examined the possibility of fully autonomous surgical robots in the future.

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“…With this background, it has become possible to realize deep learning, which previously had problems in terms of data. Another reason why deep learning is attracting attention is that the processing power of computers has improved [66][67][68]. To perform deep learning, a high-performance graphics processing unit (GPU) is essential so that an AI can learn while comparing a large amount of data [69,70].…”

Section: Introductionmentioning

confidence: 99%

Applications of Deep Learning for Drug Discovery Systems with BigData

Matsuzaka

Yashiro

2022

BioMedInformatics

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The adoption of “artificial intelligence (AI) in drug discovery”, where AI is used in the process of pharmaceutical research and development, is progressing. By using the ability to process large amounts of data, which is a characteristic of AI, and achieving advanced data analysis and inference, there are benefits such as shortening development time, reducing costs, and reducing the workload of researchers. There are various problems in drug development, but the following two issues are particularly problematic: (1) the yearly increases in development time and cost of drugs and (2) the difficulty in finding highly accurate target genes. Therefore, screening and simulation using AI are expected. Researchers have high demands for data collection and the utilization of infrastructure for AI analysis. In the field of drug discovery, for example, interest in data use increases with the amount of chemical or biological data available. The application of AI in drug discovery is becoming more active due to improvement in computer processing power and the development and spread of machine-learning frameworks, including deep learning. To evaluate performance, various statistical indices have been introduced. However, the factors affected in performance have not been revealed completely. In this study, we summarized and reviewed the applications of deep learning for drug discovery with BigData.

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Hey Siri! Perform a type 3 hysterectomy. Please watch out for the ureter!” What is autonomous surgery and what are the latest developments?

Cited by 5 publications

References 13 publications

Liability in robotic gyn surgery

Liability in robotic gyn surgery

Evolution of a surgical system using deep learning in minimally invasive surgery (Review)

Applications of Deep Learning for Drug Discovery Systems with BigData

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