Challenges and opportunities for artificial intelligence in surgery

2021 ◽  
pp. 154851292110228
Author(s):  
Pamela Andreatta ◽  
Christopher S. Smith ◽  
John Christopher Graybill ◽  
Mark Bowyer ◽  
Eric Elster
Keyword(s):  
Artificial Intelligence ◽  
Human Performance ◽  
Medical Science ◽  
Deep Understanding ◽  
Extended Period ◽  
Military Health ◽  
Active Involvement ◽  
Performance Requirements ◽  
Challenges And Opportunities ◽  
The Military

Surgery is an exceptionally complex domain where multi-dimensional expertise is developed over an extended period of time, and mastery is maintained only through ongoing engagement in surgical contexts. Expert surgeons integrate perceptual information through both conscious and subconscious awareness, and respond to the environment by leveraging their deep understanding of surgical constructs. However, their ability to utilize these deep knowledge structures can be complicated by continuous advances in technology, medical science, pharmacology, technique, materials, operative environments, etc. that must be routinely accommodated in professional practice. The demands on surgeons to perform perfectly in ever-changing contexts increases cognitive load, which could be reduced through judicious use of accurate and reliable artificial intelligence (AI) systems. AI has great potential to support human performance in complex environments such as surgery; however, the foundational requirements for the rules governing algorithmic development of performance requirements necessitate the active involvement of surgeons to precisely model the quantitative measures of performance along the continuum of expertise. Providing the AI development community with these data will help assure that accurate and reliable systems are designed to supplement human performance in applied surgical contexts. The Military Health System’s Clinical Readiness Program is developing these types of metrics to support military medical readiness.

scholarly journals Viewpoint: When Will AI Exceed Human Performance? Evidence from AI Experts

2018 ◽  
Vol 62 ◽  
pp. 729-754 ◽  
Author(s):  
Katja Grace ◽  
John Salvatier ◽  
Allan Dafoe ◽  
Baobao Zhang ◽  
Owain Evans
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
High School ◽  
Public Policy ◽  
Human Performance ◽  
Health Science ◽  
Modern Life ◽  
North Americans ◽  
Large Survey ◽  
The Military

Advances in artificial intelligence (AI) will transform modern life by reshaping transportation, health, science, finance, and the military. To adapt public policy, we need to better anticipate these advances. Here we report the results from a large survey of machine learning researchers on their beliefs about progress in AI. Researchers predict AI will outperform humans in many activities in the next ten years, such as translating languages (by 2024), writing high-school essays (by 2026), driving a truck (by 2027), working in retail (by 2031), writing a bestselling book (by 2049), and working as a surgeon (by 2053). Researchers believe there is a 50% chance of AI outperforming humans in all tasks in 45 years and of automating all human jobs in 120 years, with Asian respondents expecting these dates much sooner than North Americans. These results will inform discussion amongst researchers and policymakers about anticipating and managing trends in AI. This article is part of the special track on AI and Society.

The Washington Consensus, liberal ideas and terms of the military-technological policy

2020 ◽  
Vol 16 (11) ◽  
pp. 2103-2123
Author(s):  
V.L. Gladyshevskii ◽  
E.V. Gorgola ◽  
D.V. Khudyakov
Keyword(s):  
United States ◽  
Production Management ◽  
Developed Countries ◽  
The United States ◽  
Internal Quality ◽  
Industrial Complex ◽  
Active Involvement ◽  
Military Industrial Complex ◽  
Industrial Complexes ◽  
The Military

Subject. In the twentieth century, the most developed countries formed a permanent military economy represented by military-industrial complexes, which began to perform almost a system-forming role in national economies, acting as the basis for ensuring national security, and being an independent military and political force. The United States is pursuing a pronounced militaristic policy, has almost begun to unleash a new "cold war" against Russia and to unwind the arms race, on the one hand, trying to exhaust the enemy's economy, on the other hand, to reindustrialize its own economy, relying on the military-industrial complex. Objectives. We examine the evolution, main features and operational distinctions of the military-industrial complex of the United States and that of the Russian Federation, revealing sources of their military-technological and military-economic advancement in comparison with other countries. Methods. The study uses military-economic analysis, scientific and methodological apparatus of modern institutionalism. Results. Regulating the national economy and constant monitoring of budget financing contribute to the rise of military production, especially in the context of austerity and crisis phenomena, which, in particular, justifies the irrelevance of institutionalists' conclusions about increasing transaction costs and intensifying centralization in the industrial production management with respect to to the military-industrial complex. Conclusions. Proving to be much more efficient, the domestic military-industrial complex, without having such access to finance as the U.S. military monopolies, should certainly evolve and progress, strengthening the coordination, manageability, planning, maximum cost reduction, increasing labor productivity, and implementing an internal quality system with the active involvement of the State and its resources.

Evaluation of Ambulatory Care Classification Systems for the Military Health Care System

1990 ◽  
Author(s):  
James M. Georgoulakis ◽  
Atanacio C. Guillen ◽  
Cherry L. Gaffney ◽  
Sue E. Akins ◽  
David R. Bolling ◽  
...  
Keyword(s):  
Health Care ◽  
Ambulatory Care ◽  
Health Care System ◽  
Classification Systems ◽  
Military Health ◽  
Military Health Care ◽  
The Military ◽  
Care System

The Power of Matrix Factorization: Methods for Deconvoluting Genetic Heterogeneous Data at Expression Level

2021 ◽  
Vol 15 (8) ◽  
pp. 841-853
Author(s):  
Yuan Liu ◽  
Zhining Wen ◽  
Menglong Li
Keyword(s):  
Artificial Intelligence ◽  
Matrix Factorization ◽  
In Silico ◽  
Genetic Data ◽  
Heterogeneous Data ◽  
Expression Level ◽  
Mathematical Methods ◽  
Challenges And Opportunities ◽  
Factorization Methods ◽  
Genetic Profiles

Background:: The utilization of genetic data to investigate biological problems has recently become a vital approach. However, it is undeniable that the heterogeneity of original samples at the biological level is usually ignored when utilizing genetic data. Different cell-constitutions of a sample could differentiate the expression profile, and set considerable biases for downstream research. Matrix factorization (MF) which originated as a set of mathematical methods, has contributed massively to deconvoluting genetic profiles in silico, especially at the expression level. Objective: With the development of artificial intelligence algorithms and machine learning, the number of computational methods for solving heterogeneous problems is also rapidly abundant. However, a structural view from the angle of using MF to deconvolute genetic data is quite limited. This study was conducted to review the usages of MF methods on heterogeneous problems of genetic data on expression level. Methods: MF methods involved in deconvolution were reviewed according to their individual strengths. The demonstration is presented separately into three sections: application scenarios, method categories and summarization for tools. Specifically, application scenarios defined deconvoluting problem with applying scenarios. Method categories summarized MF algorithms contributed to different scenarios. Summarization for tools listed functions and developed web-servers over the latest decade. Additionally, challenges and opportunities of relative fields are discussed. Results and Conclusion: Based on the investigation, this study aims to present a relatively global picture to assist researchers to achieve a quicker access of deconvoluting genetic data in silico, further to help researchers in selecting suitable MF methods based on the different scenarios.

Sign in / Sign up

Export Citation Format

Share Document