Artificial intelligence and robotic surgery

2020 ◽  
Vol 30 (1) ◽  
pp. 48-54 ◽  
Author(s):  
Mahendra Bhandari ◽  
Trevor Zeffiro ◽  
Madhu Reddiboina
Author(s):  
Mariyam S. ◽  
Haris P. ◽  
Sasi M. P. ◽  
Babu D. ◽  
Lakshmanan . ◽  
...  

Robotic surgery is a rapid advancement in the scientific strata of artificial intelligence and has evolved into a refined tool for the surgeons. Over the last 30 years, this field has evolved in leaps and bounds with wide applications in the field of surgery by improving the dexterity and accessibility for the surgeons in various array of major complicated cases. The surgical armamentarium has been strengthened by evolution of robotic surgery to an extent that man may be replaced by artificial intelligence-based robots in the operation theatre, thereby eliminating the possibility of human errors and limitations.


Author(s):  
Ankita Daghottra ◽  
Dr. Divya Jain

Machine learning is a branch of artificial intelligence (AI) through which identification of patterns in data is done and with help of these patterns, useful outcomes or conclusions are predicted. One of the most prominent or frequently studied applications of machine learning is the surgical phase or robotic surgery. This makes machine learning an important part of research in robotics. The implementation of this technology in the field of healthcare aims in improving medical practices resulting in more precise and advanced surgical assessments. This paper aims in outlining the implementation and applications of machine learning related to robotics in the field of healthcare. Machine learning aims in generating positive outcomes with assumptions. The objective of this paper is to bring light on how these technologies have become an important part of providing more effective and comprehensive strategies which eventually add to positive patient outcomes and more advanced healthcare practices.


2020 ◽  
Vol 24 (02) ◽  
pp. 14-26

The following topics are under this section: Game Changers – Medical Device company leverages on artificial intelligence for robotic surgery Epidemiology of Factors associated with Low Muscle Mass in Elderly Low Glycaemic Index Foods for Healthier Diets Disruptive Technologies in the Tobacco Industry


Author(s):  
Shane O'Sullivan ◽  
Nathalie Nevejans ◽  
Colin Allen ◽  
Andrew Blyth ◽  
Simon Leonard ◽  
...  

2021 ◽  
Vol 108 (Supplement_6) ◽  
Author(s):  
A Brodie ◽  
N Dai ◽  
J Y C Teoh ◽  
K Decaestecker ◽  
P Dasgupta ◽  
...  

Abstract Aim A comprehensive review of the literature on the current and future applications of artificial intelligence (AI) in the context of urological oncology. Method Four key areas of urological oncology were identified, and a comprehensive literature review was carried out in each area looking at the current and future applications of AI. These four areas included: Prostate cancer, Renal cancer, Bladder cancer, Robotic Surgery. Results In total, 63 primary research articles were reviewed across these four areas. For prostate, renal and bladder cancer, AI has already shown great promise in the areas of imaging and histopathology interpretation, predicting tumour grade, reducing inter-observer variability and identification of genomic biomarkers. For robotic surgery, AI has already demonstrated value in the assessment of operator skill and using this to predict surgical outcomes. However, some common limitations to the applicability of AI into clinical practice include an overwhelming predominance of small retrospective studies, concerns over the datasets and methodology of AI training, the complexity of AI algorithms being such that they become un-interpretable and the technological requirements and ethical considerations with so much confidential “big data.” Conclusions The potential for AI to improve clinical care is clearly unparalleled but there remain significant challenges to adoption into clinical practice. Future research will need to focus on the establishment of multi-institute open access databases and improved data collection and integration for improved training of AI algorithms and ultimately, for clinical applicability to be realised, there needs to be high-quality prospective randomised multi-institute studies.


2021 ◽  
Vol 48 (1) ◽  
pp. 151-160
Author(s):  
Timothy C. Chang ◽  
Caleb Seufert ◽  
Okyaz Eminaga ◽  
Eugene Shkolyar ◽  
Jim C. Hu ◽  
...  

2019 ◽  
Author(s):  
Bethany Stai ◽  
Nick Heller ◽  
Sean McSweeney ◽  
Jack Rickman ◽  
Paul Blake ◽  
...  

ObjectiveTo understand better the public perception and comprehension with medical technology such as artificial intelligence and robotic surgery. Additionally, to identify sensitivity to, and comfort with, the use of AI and robotics in medicine a in order to ensure acceptability and quality of counseling and to guide future development.Subjects and MethodsA survey was conducted on a convenience sample of visitors to the Minnesota State Fair (n = 264). The survey investigated participant beliefs on the capabilities of AI and robotics in medicine and their comfort with such technology. Participants were randomized to receive one of two similar surveys. In the first a diagnosis was made by a physician and in the second by an AI application in order to compare confidence in human and computer-based diagnosis.ResultsThe median age of participants was 45 (IQR 28-59), 58% were female (n=154) vs. 42% male (n=110), 69% had completed at least a bachelor’s degree, 88% were Caucasian (n=233) vs. 12% ethnic minorities (n=31) and were from 12 states in the US with most from the Upper Midwest. Participants had nearly equal trust in AI vs. physician diagnoses, however, they were significantly more likely to trust an AI diagnosis of cancer over a doctor’s diagnosis when responding to the version of the survey that suggested an AI could make medical diagnosis (p = 9.32e-06). Though 55% of respondents (n=145) reported they were uncomfortable with automated robotic surgery the majority of the individuals surveyed (88%) mistakenly believed that partially autonomous surgery was already being performed. Almost all (94%) stated they would be willing to pay for an AI to review their medical imaging, if available.ConclusionMost participants express confidence in AI providing medical diagnoses, sometimes even over human physicians. Participants generally expressed concern with surgical AI, but mistakenly believe it is already happening. As AI applications make their way into medical practice, health care providers should be cognizant of patient misconceptions and the sensitivity that patients have to how such technology is represented.


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