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A decade ago, a significant study was conducted and published in the New England Journal of Medicine. The study investigated the correlation between a surgeon's motor skills and the outcomes of gastric bypass surgeries performed using a minimally invasive method.

This approach involves making several small incisions, with one containing a camera and the others holding the necessary surgical instruments for the procedure. Unlike the traditional method that requires a large incision in the abdominal wall, this approach offers a different perspective for the surgeon and avoids exposing the entire area.

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Doctors at work in the operating room

(Photo: shutterstockS)

For this study, 20 experienced doctors from Michigan submitted videos showcasing their surgical work. Each video was reviewed by 10 other senior doctors who assessed various technical aspects, including coordination, instrument handling and movement efficiency, assigning scores to each surgeon's abilities.

The researchers also analyzed post-surgery complications resulting from the surgeries performed by the participating doctors. They found a correlation between the technical skills of the surgeons and the occurrence of complications after the surgery. Although all subjects were experienced doctors, the study clearly demonstrated that surgeons with better technical abilities had fewer post-surgery complications.

A proficient surgeon must possess a diverse set of skills, including medical knowledge, quick decision-making under pressure, effective team management in both routine and emergency situations and complex motor skills.

While simulators are increasingly used for training medical professionals in motor skills and decision-making, the main training for interns occurs in the operating room under the guidance of specialists. The study demonstrated the feasibility of using video footage to assess the technical abilities of doctors. However, it necessitates the commitment of senior doctors to invest time and effort in the evaluation process.

In the past few years, the field of computer vision has experienced a profound transformation due to the artificial intelligence revolution. Significant breakthroughs have allowed computers to interpret and understand visual information extracted from videos.

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From Dr. Shlomi Laufer's research: a computer analyzes a doctor's work

(Photo: Eddie Bahit)

This cutting-edge technology has wide-ranging applications, such as assisting drivers of vehicles with advanced driver-assistance systems, facilitating the development of autonomous driving technology, and even diagnosing security situations in sophisticated surveillance systems. The potential of artificial intelligence in computer vision has opened up new possibilities and is reshaping various industries and sectors.

In a controlled laboratory setting, we are currently conducting experiments to determine the feasibility of training computers to analyze visual data from medical simulators. Our aim is to develop autonomous systems that can provide valuable feedback to medical professionals regarding the quality of their work. This approach enables interns to independently train in a safe and simulated environment before they begin treating actual patients.

During the initial phase of our research, we conducted a comprehensive study involving doctors at the early stages of their training. They were filmed alongside experienced doctors while performing basic surgical tasks, such as inserting needles into tissues with varying resistance and suturing them. The setup comprised a simple camera and a computer equipped with a graphics processor, similar to those found in gaming computers.

In collaboration with senior doctors, we devised a range of performance indicators to assess the execution of tasks. These indicators covered general aspects, such as coordination and movement efficiency, as well as more specific elements like the technique used for holding the surgical tool at different stages of the procedure. Subsequently, we employed machine-learning techniques to teach the computer to automatically recognize and interpret these actions.

Our research revealed the computer's proficiency in discerning the distinction between the work of a resident doctor and that of a senior doctor. Remarkably, it could pinpoint specific elements that serve as indicators of technical abilities and offer precise feedback to assist doctors in their training. These significant findings were published in the prestigious International Journal of Computer-Assisted Radiology and Surgery, a prominent publication in the field of computer-assisted surgery.

Undoubtedly, the integration of computer vision and artificial intelligence in medical simulators has the potential to revolutionize doctors' training processes. This technological advancement enables interns to independently practice a diverse range of skills and receive precise feedback without the risk of harming real patients. While simulators cannot replicate all the complexities encountered with actual patients, the focus is on developing analysis and feedback tools to assist doctors during their work in the operating room, rather than replacing their expertise.

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Adoption of natural language processing(NLP) technology has accelerated across many industries in recent years as stakeholders seek to improve the speed and accuracy of documentation reviews. In healthcare, however, NLP adoption has been a bit slower.v

Yet several trends are now emerging that make the use of NLP more essential for healthcare organizations seeking to improve risk adjustment and other critical business functions. For example, patient populations are increasing and the number of patients eligible to enroll in risk-bearing programs such as Medicare Advantage is growing. In addition, the amount of healthcare data is exploding and outpacing many other industries, according to RBC Capital Markets. In this blog, I will outline some key changes in the Risk Adjustment market that make NLP a necessity in this space. First a bit more on NLP in healthcare.

In short, NLP is a technology that ingests unstructured text, processes it using artificial intelligence (AI) and other techniques, and converts that text into structured information suitable for analysis by algorithms or humans.

However, its important to note that one person's NLP is not necessarily another person's NLP. The technology encompasses far more than just finding words and key terms it is much more sophisticated than that. For example, NLP is able to understand all of the different synonyms, words, abbreviations, and misspellings in medical records, as well as the many different contexts that doctors write in patient notes such as negation and family history.

At Linguamatics, we combine best-in-class artificial intelligence with NLP, then package it within a lean and scalable solution. Our solution can ingest a variety of different healthcare document formats that the industry processes on a daily basis to deliver improved accuracy of coding. This technology provides a significant depth of understanding and processing of records to put as much relevant information in front of coders to substantially reduce the time spent analyzing manual charts.

Risk Adjustment is one area in healthcare where the uptake of NLP has been faster than others. This rate of adoption is only going to increase, thanks largely to two changes in the risk adjustment market.

Firstly, perhaps the most pressing trend is the recent final Risk Adjustment Data Validation (RADV) rule issued by the U.S. Centers for Medicare and Medicaid Services (CMS), which has increased regulatory pressure on healthcare organizations to ensure accurate risk adjustment. The rule is intended to make it easier for CMS to claw back overpayments to healthcare organizations that were awarded as a result of faulty risk adjustment. Therefore the use of accurate NLP to identify clinical conditions and their supporting evidence (as per the Monitor Evaluate Assess Treat MEAT framework) is vital. Secondly, the Medicare Advantage risk adjustment model is due to change from V24 to V28 over the upcoming three years. These changes will significantly reduce the number of risk adjustable conditions, therefore, technologies which support accurate and complete capture of a members health are a necessity for organizations looking to ensure they dont lose funding needed to provide care for their chronically ill members.

It is important that NLP is not thought of as a tool to just add codes to members. It is a tool, that if used appropriately and fairly, can:

In addition to risk adjustment, there are numerous healthcare use cases that can benefit from the implementation of NLP. Here are three notable examples:

Improving STARS ratings: NLP can scour clinical documentation to find both denominator and numerator criteria from unstructured medical records in quality programs such as HEDIS. For example identifying patients who had had Falls Screenings or mammograms.

Closing care gaps: NLP algorithms can mine clinical data to find specific disease features that indicate growing patient risk, enabling earlier interventions that can sometimes be lifesaving for patients.

Identifying social determinants of health: On a daily basis in healthcare settings, doctors capture huge volumes of clinically important information that provides insights into patients social circumstances and risk, such as transportation access, employment status, and living situation. NLP surfaces this data, which is growing increasingly important in value-based care arrangements and will soon become part of NCQA quality measures.

Given an environment characterized by a growing patient population, a vast expansion of healthcare data, and a tightening regulatory climate, now is the time for organizations to consider how they can adopt and implement NLP technology to optimize risk adjustment.

If you would like to learn more, attend our upcoming webinar.

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Risk Adjustment in the Age of Artificial Intelligence - IQVIA

These days you cant escape hearing or reading about the accelerating impact ofartificial intelligence (AI) in both the commercial and consumer sectors. Of course, the one custom integrators should be curious about is how AI will impact the smart home, and CEDIA is partnering with leading stakeholders in the industry to spotlight the AI possibilities during the CEDIA AI Symposium atCEDIA Expo 2023in Denver, Sept. 6-9.

The CEDIA AI Symposium is expected to deliver an engaging afternoon of thought-provoking discussions led by industry visionaries, AI experts, top integrator and manufacturers. It is a rich opportunity for home tech professionals to gain insights into the rapidly evolving landscape of AI and how to leverage it for maximum impactin their own businesses.

We see news about AI every day, but little about its impact on home technology. At CEDIA, we believe knowledge is power, says Daryl Friedman,CEDIAAssociations global president and CEO.

The CEDIA Symposium will bring together the best and brightest to help attendees stay ahead of the curve in AI, an increasingly crucial tool for smart home professionals to deliver exceptional experiences.

This exclusive event will be held on Wednesday, Sept. 6 in the Four Seasons Ballroom inside the Colorado Convention Center, home of this years CEDIA Expo show floor and education slate in Denver. Cost to attend the invitation-only event is $250 and The Symposium will offer participants a platform to learn, network and exchange ideas with leaders from the industry.

As the co-founder of CEDIA, I have witnessed its remarkable journey, bringing together thought leaders across decades to navigate the dynamic shifts in home technologies. From pioneering home theater to embracing the forefront of AI, CEDIA has remained at the forefront of innovation, says Tom Doherty, HTSAs director of new technology initiatives.

The upcoming CEDIA AI Symposium, uniting the industrys top 100 thought leaders, signifies the enduring spirit of CEDIA and its commitment to advancing our homes for the future.

The Symposium is presented byAzione,HTSA,ProSourceand the CEDIA Association. Supporting organizations include leading forward-thinking channel companiesJosh.aiandOne Firefly,

11 a.m. 12 p.m.: Summit Registration

12 12:30 p.m.: Networking Lunch

12:30 1 p.m.: Opening Remarks: The Impact of AI on Luxury Residential Home Automation

Hear from Josh.ais co-founder Alex Capecelatro, who will delve into the history, current state of AI and the potential impact on the luxury residential home automation market.

1 1:55 p.m.: Marketing: 10 Ways to Leverage AI for Productivity and Performance

Discover the power of AI in marketing strategies as One Fireflys CEO, Ron Callis shares ten practical approaches to enhance productivity and performance.

2 2:55 p.m.: Panel Discussion: Custom Integrators AI Implementations

Participate in a dynamic panel discussion moderated by Alex Capecelatro, in which industry-leading integrators will share real-world examples of AI implementations in their businesses.

3 3:15 p.m.: Curated AI Tools Presentation

Join One Fireflys Callis as he provides an overview of curated AI tools, highlighting their applications and potential possibilities for those in attendance.

3:15 3:30 p.m.: Break and Networking

3:30 4:15 p.m.: Manufacturer Panel Discussion: AI Innovation for Improved Implementation and Commissioning

Hear from HTSAs Doherty, who is also CEDIAs co-founder, as he moderates a panel discussion featuring smart home industry vendors as they share their current AI efforts, their vision for leveraging AI to enhance implementation and commissioning processes to maximize value for clients.

4:20 5:00 p.m.: Closing Remarks and CEDIA Keynote Announcement

Closing remarks will be given by industry veteran Rich Green, who will provide invaluable insights into the future of luxury residential home automation.

5:00 5:30 p.m.: Networking Reception

The CEDIA Symposium is a vital event for professionals seeking to stay ahead of the curve in the fast-paced world of AI and home automation, the announcement stresses.

For those working with in the residential technology space interested in receiving an invitation, please emailai@cedia.org. Please note that the names and specific details mentioned above are subject to change. Register for theCEDIA Expo here.

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"Artificial Intelligence in Manufacturing Market"

The global AI in manufacturing market in terms of revenue was estimated to be worth $2.3 billion in 2022 and is poised to reach $16.3 billion by 2027, growing at a CAGR of 47.9% from 2022 to 2027.

The Artificial Intelligence in Manufacturing Market Size is valued at USD 2.3 billion in 2022 and is anticipated to USD 16.3 billion by 2027; growing at a CAGR of 47.9% from 2022 to 2027. Improving computing power of AI chipsets and intensifying need to handle increasingly large and complex datasetacross the globe is expected to boost the market during the forecast period.

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Informational PDF Brochure :- https://www.marketsandmarkets.com/pdfdownloadNew.asp?id=72679105

Artificial Intelligence in Manufacturing Market based on offering has been segmented into hardware, software and services. The hardware segment has been classified into processor, memory and network; software segment into AI platform and AI solution and services into deployment & integration and support & maintenance. The software segment is estimated to account for the largest share of the artificial intelligence in manufacturing market during the forecast period. The growing adoption of AI solutions and platforms in various industries and the widening application scope of AI in the manufacturing sector are the prime factors driving the growth of the AI in Manufacturing Market for the software segment.

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Browse195 market data Tables and58 Figures spread through270 Pages and in-depth TOC on "Artificial Intelligence in Manufacturing Market by Offering, Industry, Application, Technology & Region - Global Forecast to 2027"

View detailed Table of Content here - https://www.marketsandmarkets.com/Market-Reports/artificial-intelligence-manufacturing-market-72679105.html

The Artificial Intelligence in Manufacturing Market has been segmented on the basis of application. Based on application, the artificial intelligence in manufacturing market has been segmented into inventory optimization, predictive maintenance and machinery inspection, production planning, field services, reclamation, quality control, cybersecurity and industrial robots. The predictive maintenance and machinery inspection segment is estimated to account for a largest share of the artificial intelligence in manufacturing market during the forecast period. The extensive use of computer vision and machine learning technologies in machinery inspection application is the prime factors driving the growth of the AI in manufacturing market for predictive maintenance and machinery inspection application.

The Artificial Intelligence in Manufacturing Market based on technology is classified into machine learning, natural language processing, context-aware computing and computer vision. In this segment the machine learning account for the highest market share in the forecast period. Machine learning has been further segmented into deep learning, supervised learning, unsupervised learning, reinforcement learning and others (semi supervised, federated and other machine learning). The growing use of machine learning technology in various applications and the rapid adoption of robotics in the manufacturing industry are driving the growth of the AI in Manufacturing Market for machine learning technology.

The Artificial Intelligence in Manufacturing Market based on industry has been segmented into semiconductor & electronics, energy & power, pharmaceuticals, automotive, heavy metas & machine manufacturing, food & beverage and others (mining, aerospace and textile). The automotive segment is estimated to account for a largest share of the artificial intelligence in manufacturing market during the forecast period. Increased deployment of machine learning and computer vision in automotive industry is driving the market growth.

Asia Pacific is projected to account for the largest size of the Artificial Intelligence in Manufacturing Market from 2022 to 2027. The growth of the market in this region can be attributed to regional markets such as focus on quality manufacturing and rapid expansion of the semiconductor & electronics manufacturing industries in South Korea and introduction of robotics and big data in manufacturing sector of China.

Media Contact Company Name: MarketsandMarkets Research Private Ltd.Contact Person: Mr. Aashish MehraEmail: Send EmailPhone: 18886006441Address:630 Dundee Road Suite 430City: NorthbrookState: IL 60062Country: United StatesWebsite: https://www.marketsandmarkets.com/Market-Reports/artificial-intelligence-manufacturing-market-72679105.html

Press Release Distributed by ABNewswire.comTo view the original version on ABNewswire visit: Artificial Intelligence in Manufacturing Market Surges to USD 16.3 Billion by 2027, Driven by a Remarkable CAGR of 47.9%

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Artificial Intelligence in Manufacturing Market Surges to USD 16.3 Billion by 2027, Driven by a Remarkabl - Benzinga

Artificial intelligence, machine learning, and similar technologies (collectively, AI) are poised to transform industries, institutions, and our day-to-day lives. The past year alone has shown the many ways that these technologies will change how we communicate, function, and obtain and understand information. While government stakeholders grapple with whether and how to regulate AI, they are also contemplating how to use these technologies to enhance their licensing and supervisory responsibilities. The FCC is doing just that. This past week, the FCC released a draft notice of inquiry (NOI) on how to leverage AI to bolster its knowledge and analytic capabilities regarding commercial radiofrequency spectrum usage. Why? Publicly available commercial spectrum usage data remain scarce; therefore, the FCC relies on third-party studies when considering new spectrum uses.

This proceeding will not address the Commissions underlying spectrum policies or service rules. It will, however, advance the FCCs 2023 Spectrum Policy Statement advocating for modern technologies to deepen FCC commercial spectrum usage knowledge in a cost-effective, accurate, scalable, and actionable manner.

Obtaining real-time spectrum use data is challenging. For instance, the Universal Licensing System, International Communications Filing System, and other FCC databases lack real-time licensed spectrum use information. White space databases and automated frequency coordination systems track available spectrum for secondary use but do not assign users to specific channels. Recognizing these deficiencies, the FCC authorizes third-party administrators to develop and maintain spectrum access systems for monitoring and coordinating shared uses in one band. The FCC also conducts speed and drive tests through mobile operators to gather network coverage and broadband speed data. Despite these efforts, real-time spectrum usage data is unavailable for nearly all spectrum bands.

Other U.S. agencies and international bodies have explored or are exploring similar opportunities. For example, the National Telecommunications and Information Administration surveys federal spectrum data through its Spectrum Analysis Program and the Institute for Telecommunication Sciences, which recently studied the Citizens Broadband Radio Service. The National Science Foundation (NSF) and National Institute of Standards and Technology (NIST) have addressed the benefits and challenges of spectrum data with more recent initiatives, including NISTs assessment of spectrum usage during the COVID-19 pandemic.

Multiple international bodies are also monitoring spectrum usage. The International Telecommunication Union has emphasized the role of spectrum monitoring for effective spectrum management and interference resolution. Its member state administrations operate the International Monitoring System with approximately 400 stations in 81 countries collecting data, sending reports, and publishing summaries regarding spectrum use. The United Kingdom deploys spectrum detectors throughout the country to help measure and understand spectrum use in particular areas. Canadas Communications Research Centre has advanced a prototype system for visualizing spectrum data.

Taken together, these efforts highlight the importance of data-driven spectrum management domestically and globally.

In the draft NOI, the FCC seeks input on four areas.

Defining spectrum usage. How should spectrum usage be defined? What are the benefits and drawbacks of previous initiatives to define, understand, and measure spectrum usage? Should the FCC break down spectrum usage into components, such as geographic usage, frequency usage, and time usage? What other radiofrequency engineering metrics beyond the mere presence of a signal at a particular strength could evaluate spectrum uses? Can spectrum usage metrics be combined to generate a holistic understanding of the radiofrequency landscape?

Band-specific issues. What are best practices, operational considerations, and technical parameters that might correspond to different aspects of spectrum usage across different radio services? How should the FCC prioritize data collection when each issue or band has its own unique challenges? Are there comments on the conclusions from the 2016 NSF workshop that support measuring traditional fixed and mobile terrestrial transmitters and bands below 6 GHz? How should the Commission adapt its research techniques based on licensing and use characteristics for specific bands?

Data deliberations. What data sources could facilitate understanding of spectrum usage? What are existing data sources? What are the data-related challenges, such as cost and burden, standardization, and technical accuracy? What are the benefits and drawbacks of various methods to gather data, including crowdsourcing, external data sources, modeling, and direct observation?

Other matters. What are other practical, technical, and legal concerns for spectrum utilization, including data privacy and security, FCC statutory authority, and digital equity and inclusion considerations? How should the FCC facilitate and incentivize data sharing? Should it launch a field monitoring pilot program in the near term to study non-federal spectrum use? What criteria should it use when examining how other stakeholders (agencies, universities, private entities) could assist with their research and reporting? Should it issue non-binding guidance in the longer term that may outline best practices for evaluating spectrum usage?

The FCC will consider the draft NOI at its August 3, 2023 open meeting.

* * *

Many thanks to summer associates Jordyn Johnson, Isabelle Dean, and Ryan Campbell for their valuable contributions to this publication.

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