Daily Archives: January 27, 2022

Artificial intelligence leverages computers and machines to mimic the problem-solving and decision-making capabilities of the human mind.

While a number of definitions of artificial intelligence (AI) have surfaced over the last few decades, John McCarthy offers the following definition in this 2004 paper(PDF, 106 KB) (link resides outside IBM), " It is the science and engineering of making intelligent machines, especially intelligent computer programs. It is related to the similar task of using computers to understand human intelligence, but AI does not have to confine itself to methods that are biologically observable."

However, decades before this definition, the birth of the artificial intelligence conversation was denoted by Alan Turing's seminal work, "Computing Machinery and Intelligence" (PDF, 89.8 KB)(link resides outside of IBM), which was published in 1950. In this paper, Turing, often referred to as the "father of computer science", asks the following question, "Can machines think?" From there, he offers a test, now famously known as the "Turing Test", where a human interrogator would try to distinguish between a computer and human text response. While this test has undergone much scrutiny since its publish, it remains an important part of the history of AI as well as an ongoing concept within philosophy as it utilizes ideas around linguistics.

Stuart Russell and Peter Norvig then proceeded to publish, Artificial Intelligence: A Modern Approach(link resides outside IBM), becoming one of the leading textbooks in the study of AI. In it, they delve into four potential goals or definitions of AI, which differentiates computer systems on the basis of rationality and thinking vs. acting:

Human approach:

Ideal approach:

Alan Turings definition would have fallen under the category of systems that act like humans.

At its simplest form, artificial intelligence is a field, which combines computer science and robust datasets, to enable problem-solving. It also encompasses sub-fields of machine learning and deep learning, which are frequently mentioned in conjunction with artificial intelligence. These disciplines are comprised of AI algorithms which seek to create expert systems which make predictions or classifications based on input data.

Today, a lot of hype still surrounds AI development, which is expected of any new emerging technology in the market. As noted in Gartners hype cycle (link resides outside IBM), product innovations like, self-driving cars and personal assistants, follow a typical progression of innovation, from overenthusiasm through a period of disillusionment to an eventual understanding of the innovations relevance and role in a market or domain. As Lex Fridman notes here (link resides outside IBM) in his MIT lecture in 2019, we are at the peak of inflated expectations, approaching the trough of disillusionment.

As conversations emerge around the ethics of AI, we can begin to see the initial glimpses of the trough of disillusionment. To read more on where IBM stands within the conversation around AI ethics, read more here.

Weak AIalso called Narrow AI or Artificial Narrow Intelligence (ANI)is AI trained and focused to perform specific tasks. Weak AI drives most of the AI that surrounds us today. Narrow might be a more accurate descriptor for this type of AI as it is anything but weak; it enables some very robust applications, such as Apple's Siri, Amazon's Alexa, IBM Watson, and autonomous vehicles.

Strong AI is made up of Artificial General Intelligence (AGI) and Artificial Super Intelligence (ASI). Artificial general intelligence (AGI), or general AI, is a theoretical form of AI where a machine would have an intelligence equaled to humans; it would have a self-aware consciousness that has the ability to solve problems, learn, and plan for the future. Artificial Super Intelligence (ASI)also known as superintelligencewould surpass the intelligence and ability of the human brain. While strong AI is still entirely theoretical with no practical examples in use today, that doesn't mean AI researchers aren't also exploring its development. In the meantime, the best examples of ASI might be from science fiction, such as HAL, the superhuman, rogue computer assistant in 2001: A Space Odyssey.

Since deep learning and machine learning tend to be used interchangeably, its worth noting the nuances between the two. As mentioned above, both deep learning and machine learning are sub-fields of artificial intelligence, and deep learning is actually a sub-field of machine learning.

Deep learning is actually comprised of neural networks. Deep in deep learning refers to a neural network comprised of more than three layerswhich would be inclusive of the inputs and the outputcan be considered a deep learning algorithm. This is generally represented using the following diagram:

The way in which deep learning and machine learning differ is in how each algorithm learns. Deep learning automates much of the feature extraction piece of the process, eliminating some of the manual human intervention required and enabling the use of larger data sets. You can think of deep learning as "scalable machine learning" as Lex Fridman noted in same MIT lecture from above. Classical, or "non-deep", machine learning is more dependent on human intervention to learn. Human experts determine the hierarchy of features to understand the differences between data inputs, usually requiring more structured data to learn.

"Deep" machine learning can leverage labeled datasets, also known as supervised learning, to inform its algorithm, but it doesnt necessarily require a labeled dataset. It can ingest unstructured data in its raw form (e.g. text, images), and it can automatically determine the hierarchy of features which distinguish different categories of data from one another. Unlike machine learning, it doesn't require human intervention to process data, allowing us to scale machine learning in more interesting ways.

There are numerous, real-world applications of AI systems today. Below are some of the most common examples:

The idea of 'a machine that thinks' dates back to ancient Greece. But since the advent of electronic computing (and relative to some of the topics discussed in this article) important events and milestones in the evolution of artificial intelligence include the following:

IBM has been a leader in advancing AI-driven technologies for enterprises and has pioneered the future of machine learning systems for multiple industries. Based on decades of AI research, years of experience working with organizations of all sizes, and on learnings from over 30,000 IBM Watson engagements, IBM has developed the AI Ladder for successful artificial intelligence deployments:

IBM Watson gives enterprises the AI tools they need to transform their business systems and workflows, while significantly improving automation and efficiency. For more information on how IBM can help you complete your AI journey, explore the IBM portfolio of managed services and solutions

Sign up for an IBMid and create your IBM Cloud account.

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What is Artificial Intelligence (AI)? - India | IBM

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Jan 25, 2022 (SUPER MARKET RESEARCH via COMTEX) --According to IMARC Group's latest report, titled "Artificial Intelligence Market: Industry Trends, Share, Size, Growth, Opportunity and Forecast 2022-2027," the global artificial intelligence market reached a value of US$ 56.5 Billion in 2021. Looking forward, IMARC Group expects the market to reach US$ 308.5 Billion by 2027, exhibiting at a CAGR of 31.9% during 2022-2027.

We are regularly tracking the direct effect of COVID-19 on the market, along with the indirect influence of associated industries. These observations will be integrated into the report.

Artificial Intelligence (AI) is a computer science division that replicates human intelligence in machines. AI-integrated machines are capable of speech-recognition, learning, decision-making, and problem-solving applications. The hardware components of AI are various chipsets, such as graphics processing unit (GPU), central processing unit (CPU), application-specific integrated circuits (ASIC), and field-programmable gate array (FPGA). The software components are core technologies that include natural language processing (NLP), deep learning, machine learning (ML), augmented and virtual reality (AR/VR).

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Market Trends

The thriving IT industry, along with rapid digitization across several industries, is primarily driving the market for artificial intelligence. The rising adoption of cloud-based solutions has propelled the demand for AI-integrated systems. Furthermore, the rapid shift towards AI-integrated systems to analyze data and extract insights for enhanced operational efficiencies and consumer experience also drives the market. Moreover, the introduction of 5G technology, coupled with the increasing penetration of virtual assistants, has bolstered the market growth. Numerous social media and e-commerce platforms are employing AI-based algorithms in their applications to improve customer engagement. Additionally, the development of AI-powered industrial and surgical robots is further anticipated to drive the artificial intelligence market.

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Artificial Intelligence Market 2022-27: Global Size, Share ...

WASHINGTON--(BUSINESS WIRE)--Women from across the US, Mexico and Canada are launching the Women in AI (WAI) Awards North America to honor female pioneers who take the road less traveled and pave the way for others to reach even further.

The kick-off event takes place virtually on February 1, 2022 at 5pm ET when applications for category nominations will also open for: AI in Startups, AI in Research, AI for Good, AI in Government, AI in Industry, Young Role Model in AI.

Winners will be announced at a hybrid event on May 13, 2022. Please email us at waiawardsna@womeninai.co or visit our website for more information.

Susan Verdiguel, WAI Ambassador to Mexico says, Through this collaboration, we are able to amplify the AI ecosystem in Mexico for a more robust, informed, and organized community.

Sponsors/partners include Mila - Quebec Artificial Intelligence Institute, Alberta Machine Intelligence Institute, Topcoder, The Institute for Education, IVOW AI and GET Cities, an initiative designed to accelerate the leadership and representation of women, trans, and nonbinary people in tech.

"We know that AI and machine learning are the future, and we know the risks of not including diverse perspectives in designing solutions for this future. That's why we're thrilled to be a strategic partner for the Women in AI Summit and to celebrate all of the amazing people leading the way in co-creating an inclusive tech economy." - Leslie Lynn Smith, National Director of GET Cities

Davar Ardalan, CEO of IVOW AI and Senior Advisor to WAI North America says, Our ultimate goal is to recognize the role women are playing in AI and to encourage more young women to enter the field of computer science and AI.

This collaborative endeavor of Women in AI will provide an exclusive platform for every women AI professional across North America, no matter their age, role or field of work, to be recognized for their contributions in AI, says Frincy Clement, WAI Ambassador to Canada.

Our diverse communities at the grassroots level drive amazing societal impact by applying AI, ML and Data Science integrated with United Nations Sustainable Development Goals, noted Bhuva Subram, Founder of Wallet Max and the Regional Head of Women in AI North America & USA.

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Women in AI Awards to Honor Top Female Innovators in the Field of Artificial Intelligence in North America - Business Wire

Pune, India, Jan. 27, 2022 (GLOBE NEWSWIRE) -- The global cryptocurrency market size is expected to gain momentum by reaching USD 1,902.5 million by 2028 while exhibiting a CAGR of 11.1% between 2021 to 2028. In its report titled Cryptocurrency Market, Fortune Business Insight mentions that the market stood at USD 826.6 million in 2020.

The demand for crypto has increased due to rising investments in venture capital. Additionally, the increasing popularity of digital assets such as bitcoin and litecoin is likely to accelerate the market in upcoming years. Furthermore, it has been seen that the digital currency is also used in the integration of blockchain technology to get decentralization and control efficient transactions. Thus, advantages such as these are also encouraging people to invest in crypto. For instance, In October 2018, Qtum Chain Foundation made a partnership with Amazon Web Services (AWS) China to use blockchain systems on the AWS cloud. With this collaboration, AWS will be able to help its users in using Amazon Machine Images (AMI) to develop and publish smart contracts easily and efficiently.

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Companiesin Cryptocurrency Market:

What does the Report Provide?

The market report offers in depth analysis of various factors, which are influencing the market growth. Additionally, the report provides insights into the regional analysis of different regions. It includes the competitive landscape that involves the leading companies and the adoption of strategies to introduce new products, announce partnerships, and collaboration that contribute in boosting the market.

COVID-19 Impact

The COVID-19 pandemic adversely affected the world economy. However, the relationship between Bitcoin and the equity market expanded amid pandemic. For example, in March 2020, the price of Bitcoin declined and went below USD 4,000 after a decline in the S&P Index in the U.S. Thus, as the Initial Coin Offering (ICO) market crashed, blockchain companies are emerging as major alternative to raise investment capital.

Click here to get the short-term and long-term impact of COVID-19 on this Market.

Please visit: https://www.fortunebusinessinsights.com/industry-reports/cryptocurrency-market-100149

Market Segmentation:

By component, the market is bifurcated into hardware, and software. By type, it is divided into bitcoin, ether, litecoin, ripple, ether classic, and others. By end-use, it is divided into trading, E-commerce and retail, peer-to-peer payment, and remittance.

Based on end use, the trading segment held the market share of 42.8% in 2020, because it focuses on crypto solutions that are used for trading such as Pionex, Cryptohopper, Bitsgap, Coinrule, and others.

Lastly, in terms of geography, the market is divided into North America, Europe, Asia Pacific, the Middle East & Africa and Latin America.

Driving Factor

Focus on Mitigating Financial Crisis and Regional Instability Drives the Demand for Virtual Currency

In recent times, financial disaster is one of the primary issues that occurs in the conventional banking system. This financial instability disrupts the economy by lowering the value of money. For instance, ICICI bank of India, in the year 2008, confronted the Lehman brother crisis, which hugely impacted the nations economy. But with using bitcoins, and other cryptocurrency, such situations of economic downfall can be avoided. Therefore, Cryptocurrencies are emerging as alternative options in the regions with unstable economical structure, and this has been a major driving factor for the cryptocurrency market growth.

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Regional Insights

North America to Dominate Backed by Presence of Prominent Players

North America is expected to remain at the forefront and hold the largest position in the market during the forecast period. This is because in most parts of the region bitcoins have become a medium of exchange for tax purposes rather than the actual currency. Although these are not legally regulated by the government, still many of the countries in the region are focused on using digital currencies. The regions market stood at USD 273.0 million in 2020.

Asia Pacific is expected to showcase significant cryptocurrency market share in upcoming years, owing to several technological developments and acceptance of virtual currency for some platforms within Japan and Taiwan. Additionally, the strategic collaborations, partnerships by key players are also fueling the regional market. For instance, in January 2020, Z Corporation, Inc. and TaoTao, Inc. collaborated with the financial service agency to widen the crypto market by confirming regulatory compliance in the Japanese market.

Competitive Landscape

Key Players to Focus on Introduction of New Services to Strengthen the Market Growth

The market is consolidated by major companies striving to maintain their position by focusing on new launches, collaborations & partnerships and acquisitions. Such strategies taken up by key players are expected to strengthen its market prospects. Below is the industry development:

March 2021 Visa Inc. aims to introduce crypto as a direct payment. With this key initiative, the company aims to accept cryptocurrencies as a payment method for the finance industry.

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Part II: Artificial Intelligence Market

The global artificial intelligence market size is expected to reach USD 360.36 billion by 2028. As per the report, the market size was valued at USD 35.92 billion in 2019 and is estimated to display a stellar CAGR of 31.9% during the forecast period. This information is presented by Fortune Business Insights, in its report, titled, Artificial Intelligence Market, 2021-2028. The increasing number of linked devices and rising implementation ofInternet of Things (IoT)are steering the market growth. Multiplying usage of cloud-based applications in various industries such as medical, online retail, production, and Banking, Financial Services, & Insurance (BFSI) coupled with rising complexity of cyber-crimes are presenting exciting opportunities to expand the utilization of artificial intelligence in the market. For example, use of machine learning (ML) in precisely identifying cancerous cells is anticipated to propel its demand in the healthcare industry.

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AI Technology that Traces COVID-19 Patients Set to Promote Market Growth

The medical industry is projected to considerably benefit from AI applications during the COVID-19 pandemic. For example, in the clinical health care procedures, AI will assist in improving the precision and efficacy in diagnosing the disease, suggesting treatments, and predicting results. In the United States, the government is employing essential data from detachable devices to trace COVID-19 positive patients. AI assists in developing and mining the coronavirus stress and using it to improve and scale the testing equipment. The extracted data can be useful for drug discovery. For example, the TCSI lab is making use of AI capabilities to recognize potential molecules and to target it against the COVID stress. Therefore, amid pandemic, the artificial intelligence market is anticipated to observe substantial growth.

Report Coverage

The report provides a thorough study of the market segments and detailed analysis of the market overview. A profound evaluation of the current market trends as well as the future opportunities is presented in the report. It further shares an in-depth analysis of the regional insights and how they shape the market growth. The COVID-19 impacts have been added to the report to help investors and business owners understand the threats better. The report sheds light on the key players and their prominent strategies to stay in the leading position.

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CompaniesCovered in the Artificial Intelligence Market Report

Segmentation

By component, the artificial intelligence market is divided into hardware, software and services. The services segment is estimated to gain momentum during the forecast period. The incorporation of AI with the prevailing systems in companies needs suitable skillset and expertise. Furthermore, for maintenance and to support artificial intelligence, an insightful set of expertise is essential. Additionally, the software segment held a share of 40.9% in the year 2019.

On the basis of technology, the market is segregated into computer vision, machine learning and natural language processing. Based on deployment, it is further bifurcated into cloud and on-premise. By industry, the market is separated into healthcare, retail, IT and telecom, BFSI, automotive, advertising and media, and manufacturing among others. In terms of region, the global market is categorized into North America, Europe, Asia Pacific, Latin America, and the Middle East and Africa.

Drivers and Restraints

Budding BFSI Industry to Inflate Opportunities for Artificial Intelligence Market

The BFSI industry is estimated to extend the applications of artificial intelligence (AI). It is already consuming the technology for making trading decisions, for chatting robots, credit scoring applications, and to study the financial market impact analysis, among others. For example, several banks are utilizing ML tools to generate trading robots that are capable of self-analysing and to teach trading, based on past data. Moreover, BFSI is making use of AI technology to provide personalized guidance to its users concerning debt administration, investment tactics, refinancing, and much more. The technology is also efficient in detecting fraud activities. This is expected to create widespread opportunities for the application of the technology, thereby initiating in the artificial intelligence market growth in the near future.

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Regional Insights

North America to Hold Command Backed by Active Government Initiatives

The artificial intelligence market share in the North American region was USD 11.40 billion in 2019, where the U.S. was a major contributor due to increasing government initiatives and investments in the market. This is expected to boost demand for artificial intelligence in the near future.

Europe is estimated to be an equal contributor to the global economy in the artificial intelligence market. Countries in the European region are tactically financing in AI. For example, the European Investment Fund, assigned USD 111 million for the AI-based start-ups in 2020.

Asia Pacific is estimated to witness speedy growth during the forecast period. In this region, China is responsible for generating the main income share, owing to collective investments by leading players in the technology. Furthermore, to offer strong outcomes in the field, it also presented the New Generation Artificial Intelligence Development Plan.

Competitive Landscape

Partnerships and Mergers to Help Developers Innovate New Ideas and Expand Business

Prominent players in the market often come up with efficient strategies that include partnerships, acquisitions and mergers, product launches, etc. These strategies bolster their position as leading players and also benefit the other involved companies as well.

For instance, in May 2020, IPsoft Inc. protracted its collaboration with Unisys Corporation to apply AI capabilities in InteliServe and Amelia. The incorporated suite will aid organizations to solve workplace concerns with its intellectual technology.

Industry Development

June 2020: Microsoft Corporation made an investment in the Mount Sinai Health System. The company is a healthcare based firm and will be using AI to improve the COVID-19 related care through its advanced digital tools. This is likely to boost demand for artificial intelligence in the upcoming years.

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How Artificial Intelligence Will Boost the Cryptocurrency Market to Reach USD 1902.5 Million by 2028 - GlobeNewswire

Introduction

According to the International Agency for Research on Cancer, nasopharyngeal carcinoma (NPC) is the twenty-third most common cancer worldwide. The global number of new cases and deaths in 2020 were 133,354 and 80,008, respectively.1,2 Although it is not uncommon, it has a distinct geographical distribution where it is most prevalent in Eastern and South-Eastern Asia, accounting for 76.9% of global cases. It was also found that almost half of the new cases occurred in China.2 Because of its late symptoms and anatomical location, it makes it difficult to be detected in the early stages. Radiotherapy is the primary treatment modality, and concomitant/adjunctive chemotherapy is often needed for advanced locoregional disease.3 Furthermore, there are many organs-at-risk (OARs) nearby that are sensitive to radiation; these include the salivary glands, brainstem, optic nerves, temporal lobes and the cochlea.4 Hence, it is of interest whether the use of artificial intelligence (AI) can help improve the diagnosis, treatment process and prediction of outcomes for NPC.

With the advances of AI over the past decade, it has become pervasive in many industries playing both major and minor roles. This includes cancer treatment, where medical professionals search for methods to utilize it to improve treatment quality. AI refers to any method that allows algorithms to mimic intelligent behavior. It has two subsets, which are machine learning (ML) and deep learning (DL). ML uses statistical methods to allow the algorithm to learn and improve its performance, such as random forest and support vector machine. Artificial neural network (ANN) is an example of ML and is also a core part of DL.5 DL can be defined as a learning algorithm that can automatically update its parameters through multiple layers of ANN. Deep neural networks such as convolutional neural network (CNN) and recurrent neural network are all DL architectures.

Besides histological, clinical and demographic information, a wide range of data ranging from genomics, proteomics, immunohistochemistry, and imaging must be integrated by physicians when developing personalized treatment plans for patients. This has led to an interest in developing computational approaches to improve medical management by providing insights that will enhance patient outcomes and workflow throughout a patients journey.

Given the increased use of AI in cancer care, in this systematic literature review, papers on AI applications for NPC management were compiled and studied in order to provide an overview of the current trend. Furthermore, possible limitations discussed within the articles were explored.

A systematic literature search was conducted to retrieve all studies that used AI or its subfields in NPC management. Keywords were developed and combined using boolean logic to produce the resulting search phrase: (artificial intelligence OR machine learning OR deep learning OR Neural Network) AND (nasopharyngeal carcinoma OR nasopharyngeal cancer). Using the search phrase, a search of research articles from the past 15 years to March 2021 was performed on PubMed, Scopus and Embase. The results from the three databases were consolidated, and duplicates were removed. The Preferred Reporting Items for Systematic Review and Meta-Analyses (PRISMA) was followed where possible, and the PRISMA flow diagram and checklist were used as a guidelines to consider the key aspects of a systematic literature review.6

Exclusion and inclusion criteria were determined to assess the eligibility of the retrieved publications. The articles were first checked to remove those that were not within the exclusion criteria. These included book chapters, conference reports, literature reviews, editorials, letters to the editors and case reports. In addition, articles in languages other than English or Chinese and papers with inaccessible full-texts were also excluded.

The remaining studies were then filtered by reading the title and abstract to remove any articles that were not within the inclusion criteria (applications of AI or its subfield and experiments on NPC). A full-text review was further performed to confirm the eligibility of the articles based on both these criteria. The process was conducted by two independent reviewers (B.B & H.C.).

Essential information from each article was extracted and placed in a data extraction table (Table 1). These included the author(s), year of publication, country, sample type, sample size, AI algorithm used, application type, study aim, performance metrics reported, results, conclusion, and limitations. The AI model with the best performance metrics from each study was selected and included. Moreover, the performance results of models trained with the training cohort were obtained from evaluating the test cohort instead of the training cohort. This was to prevent overfitting by avoiding to train and test models with the same dataset.

The selected articles were assessed for risk of bias and applicability using the Quality Assessment of Diagnostic Accuracy Studies (QUADAS)-2 tool in Table 2.7 Studies with more than one section rated high or unclear were eliminated. Further quality assessment was also completed to ensure the papers meet the required standard. This was performed using the guidelines for developing and reporting ML predictive models from Luo et al and Alabi et al (Table 3).8,9 The guideline was summarised, and a mark was given for each guideline topic followed. The threshold was set at half of the maximum marks, and the score was presented in Table 4.

Table 2 Quality Assessment via the QUADAS-2 Tool

Table 3 Quality Assessment Guidelines

The selection process was performed using the PRISMA flow diagram in Figure 1. 304 papers were retrieved from the three databases. After 148 duplicates were removed, one inaccessible article was rejected. The papers not meeting the inclusion (n=59) and exclusion (n=20) criteria were also filtered out. Moreover, two additional studies found in literature reviews were included after removing one for being duplicated and another that did not meet the exclusion criteria. Finally, 78 papers were then assessed for quality (Figure 1).

Figure 1 PRISMA flow diagram 2020.

Notes: Adapted from Page MJ, McKenzie JE, Bossuyt PM, et al.The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. BMJ. 2021;372:n71. Creative Commons license and disclaimer available from: http://creativecommons.org/licenses/by/4.0/legalcode.6

18 papers failed due to having more than one section with a high or unclear rating, leaving 60 studies to be further evaluated. The QUADAS-2 tool showed that 48.3% of articles showed an overall low risk of bias, while 98.3% of them had a low concern regarding applicability (Table 2).

An additional evaluation was performed based on Table 3, which was adapted from the guidelines by Luo et al and the modified version from Alabi et al8,9 Of the 60 relevant studies, 52 of them scored greater than 70% (Table 4). It should also be noted that 23 papers included the evaluation criteria items but did not fully follow the structure of the proposed guidelines.1032 However, this only affects the ease of reading and extracting information from the articles, but not the content and quality of the papers.

The characteristics of the 7articles finally included in the current study were shown in Table 1. The articles were published in either English (n=57)1066 or Chinese (n=3);6769 3 studies examined sites other than the NPC.10,17,34

When observing the origins of the studies, 45 were published in Asia, while Morocco and France contributed one study each. Furthermore, 13 papers were collaborated work from multiple countries. The majority of the studies were from the endemic regions.

The articles used various types of data to train the models. 66.7% (n=40) only used imaging data such as magnetic resonance imaging, computed tomography or endoscopic images.15,16,18,19,2124,2628,30,32,34,3739,4143,4556,5863,67,69 There were also four studies that included clinicopathological data as well as images for training models,25,31,36,40 while three other studies developed models using images, clinicopathological data, and plasma Epstein-Barr virus (EBV) DNA.29,33,35 Furthermore, 4 studies used treatment plans,6466,68 while proteins and microRNA expressions data were each extracted by one study.10,44 There were also four articles that trained with both clinicopathological and plasma EBV DNA/serology data,1214,17 while one article trained its model with clinicopathological and dosimetric data.57 Risk factors (n=2), such as demographic, medical history, familial cancer history, dietary, social and environmental factors, were also used to develop AI models.11,20

The studies could be categorized into 4 domains, which were auto-contouring (n=21),15,16,18,22,24,3032,4555,67,69 diagnosis (n=17),10,15,16,23,26,27,49,52,54,5663 prognosis (n=20)1214,17,19,25,28,29,3344 and miscellaneous applications (n=7),11,20,21,6466,68 which included risk factor identification, image registration and radiotherapy planning (Figure 2A). Five studies examined both diagnosis and auto-contouring simultaneously.15,16,49,52,54

Figure 2 Comparison of studies on AI application for NPC management. (A) Application types of AI and its subfields on NPC; (B) Main performance metrics of application types on NPC.

Abbreviations: AI, artificial intelligence; AUC, area under the receiver operating characteristic curve; DSC, dice similarity coefficient; ASSD, average symmetric surface distance; NPC, nasopharyngeal carcinoma.

Notes: aMore than one AI subfield (artificial intelligence, machine learning and deep learning) was used in the same study. bAuto-contouring and diagnosis accuracy values were found in the same study.54.

Analyses on the purpose of the application showed that, only in auto-contouring, DL is the most heavily used (with 19 out of 22 instances). For the rest of the categories (NPC diagnosis, prognosis and miscellaneous applications), ML is the most common technique (more than half of the publications in each category) (Figure 2A). In addition, studies applying DL models selected in this literature review were published from 2017 to 2021, where there was a heavier focus on experimenting with DL. It was observed that the majority of the papers applying DL models used various forms of CNN (n=30),15,18,19,2124,2834,36,4553,55,56,60,65,67,69 while the main ML method used was ANN (n=12).13,16,26,4244,54,6164,68

The primary metrics reported were the area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, specificity, dice similarity coefficient (DSC) and average symmetric surface distance (ASSD), as shown in Figure 2B.

AUC was used to evaluate the models capabilities in 25 papers, with the majority measuring the prognostic (n=13)1214,19,28,3335,37,39,40,42,44 and diagnostic abilities (n=10).15,23,26,27,49,5660 Similarly, accuracy was the parameter most frequently reported in the diagnosis and prognosis application: 11 and 5 out of 20 articles respectively.10,12,15,2628,35,43,44,49,54,56,6063 Sensitivity was the most common studied parameter for diagnostic performance: 15 out of 23 papers.10,15,16,23,26,27,49,52,54,56,5963 The specificity was only reported for prognosis (n=7)12,14,28,34,39,40,43 and diagnosis (n=15).10,15,16,23,26,27,49,52,54,56,5963 In addition, the DSC (n=20)15,18,22,24,3032,4553,55,65,67,69 and ASSD (n=10)18,22,24,31,32,45,46,48,51,69 were the primary metrics reported in studies on auto-contouring (Figure 2B).

Performance metrics with five or more instances of each application method were presented in a boxplot (Figure 3). The median AUC, accuracy, sensitivity and specificity of prognosis were 0.8000, 0.8300, 0.8003 and 0.8070 respectively, while their range were 0.63300.9510, 0.75590.9090, 0.34400.9200 and 0.52001.000 respectively. For diagnosis, the AUCs median was 0.9300, while the median accuracy was 0.9150. In addition, the median sensitivity and specificity were 0.9307 and 0.9413, respectively. The range for diagnosis AUC, accuracy, sensitivity and specificity were 0.69000.9900, 0.65000.9777, 0.02151.000 and 0.80001.000, respectively. The median DSC value for auto-contouring was 0.7530, while the range was 0.62000.9340. Furthermore, the median ASSD for auto-contouring was 1.7350 mm, and the minimum and maximum values found in the studies were 0.5330 mm and 3.4000 mm, respectively.

Figure 3 Performance metric boxplots of AI application types on NPC. (A) Prognosis and diagnosis: accuracy, AUC, sensitivity and specificity metric; (B) Auto-contouring: DSC metric; (C) Auto-contouring: ASSD metric.

Abbreviations: AI, artificial intelligence; ASSD, average symmetric surface distance; AUC, area under the receiver operating characteristic curve; DSC, dice similarity coefficient; NPC, nasopharyngeal carcinoma.

Publications on auto-contouring experimented on segmenting gross tumor volumes, clinical target volume, OARs and primary tumor volumes. The target most delineated was the gross target volume (n=7),30,48,49,51,53,55,69 while the second most were the OARs (n=3).50,52,67 The clinical target volumes and the primary tumor volume were studied in two and one articles respectively.46,55,56 However, nine articles did not mention the specific target volume contoured.15,16,18,22,24,31,32,47,54 Two out of three articles reported that the DSC for delineating optic nerves was substantially lower than the other OARs.52,67 In contrast, for the remaining paper, although the segmentation of the optic nerve is not the worst, the paper reported that the three OARs it tested, which included optic nerves, were specifically more challenging to contour.50 This is because of the low soft-tissue contrast in computed tomography images and their diverse morphological characteristics. When analyzing the OARs, automatic delineation of the eyes yielded the best DSC. Furthermore, apart from the spinal cord, optic nerve and optic chiasm, the AI models have a DSC value greater than 0.8 when contouring OARs.50,52,67

As for the detection of NPC, six papers compared the performance of AI and humans. Two of them found that AIs had better diagnostic capabilities than humans (oncologists and experienced radiologists),15,49 while another two reported that AIs had similar performances to ear, nose and throat specialists.16,62 However, the last two papers found that it depends on the experience of the person. For example, senior-level clinicians performed better than the AI, while junior level ones were worse.23,60 This is because of the variations in possible sizes, shapes, locations, and image intensities of NPC, making it difficult to determine the diagnosis. These factors make it challenging for clinicians with less experience, and it showed that AI diagnostic tools could support junior-level clinicians.

On the other hand, within the 17 papers experimenting on the diagnostic application of AI, three articles analyzed radiation-induced injury diagnosis.27,57,58 Two of which were concerned with radiation-induced temporal lobe injury,57,58 while the remaining one predicted the fibrosis level of neck muscles after radiotherapy.27 It was suggested that through early detection and prediction of radiation-induced injuries, preventive measures could be taken to minimize the side effects.

For studies on NPC prognosis, 11 out of 20 publications focused on predicting treatment outcomes, with the majority including disease-free survival as one of the study objectives.12,13,17,19,29,33,36,3942 The rest studied treatment response prediction (n=2),35,43 predicting patients risk of survival (n=5),14,25,37,38,44 T staging prediction and the prediction of distant metastasis (n=2).28,34 Therefore, the versatility of AI in different functionalities was demonstrated. The performances of the models were reported in (Table 1) and the main metric analyzed was AUC with 13 out of 25 articles (Figure 2B).

In addition to the above aspects, AI was also used to study risk factor identification (n=2),11,20 image registration (n=1)21 and dose/dose-volume histogram (DVH) distribution (n=4).6466,68 In particular, dose/DVH distribution prediction was frequently used for treatment planning. A better understanding of the doses given to the target and OARs can help clinicians give a more individualized treatment plan with better consistency and a lower planning duration. However, further development is required to obtain similar plan qualities as created by people. This is because one papers model showed the same quality as manual planning by an experienced physicist,64 but another study using a different model was unable to achieve a similar plan quality designed by even a junior physicist.68

As evident in this systematic review, there is an exponential growth in interest to apply AI for the clinical management of NPC. A large proportion of the articles collected were published from 2019 to 2021 (n=45) compared to that from 2010 to 2018 (n=15).

A heavier focus is also placed on specific fields of AIs, such as ML and DL. There are only three reports on AI, while there are 31 studies on ML and 37 on DL. The choice of AI subfield sometimes depends on the task. For example, 86% of the papers focused on DL for NPC auto-contouring (n=19), while although the majority of the studies in the other applications used ML, they were more evenly distributed (Figure 2A). The reason why there is such a significant difference in the type of AI used in auto-contouring may be due to the capability of the algorithms and the nature of the data. The medical images acquired have many factors affecting the auto-contouring quality; these include the varying tumour sizes and shapes, image resolution, contrast between regions, noise and lack of consistency during data acquisition being collected from different institutions.70 Because of these challenges, ML-based algorithms have difficulty in performing automated segmentation on NPC as image processing before training is required, which is time-consuming. Furthermore, handcrafted features are necessary to precisely contour each organ or tumour as there are significant variations in size and shape for NPC. On the other hand, DL does not have this issue as they can process the raw data directly without the need for handcrafted features.70

ANN is the backbone of DL, as DL algorithms are ANNs with multiple (2 or more) hidden layers. In the development of AI applications for NPC, 80% of the studied articles incorporated either ANN or DL technique in their models12,13,1519,2126,2834,36,38,39,4256,6069 because neural networks are generally better for image recognition. However, one study cautioned that ANNs were not necessarily better than other ML models in NPC identification.61 Hence, even though DL-based models and ANNs should be considered the primary development focus, other ML techniques should still not be neglected.

Based on the literature collected, the integration of AI applications in each category is beneficial to the practitioner. Automated contouring by AIs not only can make contouring less time-consuming for clinicians,46,51,53,64 it can also help to improve the users accuracy.51 Similarly, AI can be used to reduce the treatment planning time for radiotherapy,64 thus improve the efficiency and effectiveness of the radiotherapy planning process.

For some NPC studies, additional features from images and parameters were extracted to further improve the performances of models. However, it should be noted that not all features are suitable as some features have a more significant impact on the models performance than others.40,57,58,61 Therefore, feature selection should be considered where possible.

At its current state, AI cannot yet replace humans to perform the most complex and time-consuming tasks. This is because multiple articles which compared the performance of their developed model with medical professionals showed conflicting results. The reason for this is that the experience of the clinician is an important factor that affects the resulting comparison. The models developed by Chuang et al and Diao et al performed better than junior-level professionals, but performed worse when compared to more experienced clinicians.23,60 One article even showed that an AI model had a lower capability than a junior physicist.68 Furthermore, the quality of the training data and the experience of the AI developers are critical.

The review revealed that AI at its current state still has several limitations. The first concern was the uncertainty regarding the generalizability of the models, because datasets of many studies are retrospective and single institutional in nature.15,19,28,33,3538,41,48,5759 The dataset may not represent the true population and may only represent a population subgroup or a region. Hence, this reduces the applicability of the models and affects their performance when applied to other datasets. Another reason was the difference in scan protocol between institutions. Variations in tissue contrasts or field of views may affect the performance as the model was not trained for the same condition.45,56 Therefore, consistency of scan protocols among different institutions is important to facilitate AI model training and validation.

Another limitation was the small amount of data used to train the models. 33% (n=20) of the articles chosen had 150 total samples for both training and testing the model. The reason for this was not only were the articles usually based on single-centre data, but also because NPC is less common compared to other cancers. This particularly affects DL-based models as they are more reliant on a much larger dataset to achieve their potential when compared to ML models; over-fitting will likely occur when there is only limited data; thus, data augmentation is often used to increase the dataset size. In addition, some studies had patient selection bias, while others had concerns about not implementing multi-modality inputs into the training model (Table 1).

Future work should address these issues when developing new models. Possible solutions include incorporating other datasets or cooperating with other institutions for external validation or to expand the dataset, which were lacking in most of the analysed papers in this review. The former suggestion can boost generalizability and avoid any patient selection bias, while the latter method can increase the capability of the AI models by providing more training samples. Other methods to expand dataset have also been explored, one of which is by using big data which can be done at a much larger scale. Big data can be defined as the vast data generated by technology and the internet of things, allowing easier access to information.71 In the healthcare sector, it will allow easier access to an abundance of medical data which will facilitate AI model training. However, with the large collection of data, privacy protection becomes a serious challenge. Therefore, future studies are required to investigate how to implement it.

The performances of the AI models could also be improved by increasing the amount of data and diversifying it with data augmentation techniques which were performed in some of the studies. However, it should be noted that with an increase in training samples, more data labelling will be required, making the process more time-consuming. Hence, one study proposed the use of continual learning, which it found to boost the models performance while reducing the labelling effort.47 However, continual learning is susceptible to catastrophic forgetting, which is a long-standing and highly challenging issue.72 Thus, further investigation into methods to resolve this problem would be required to make it easier to implement in other research settings.

There are several limitations in this literature review. The metric performance results extracted from the publications were insufficient to perform a meta-analysis. Hence, the insight obtained from this review is not comprehensive enough. The quality of the included studies was also not consistent, which may affect the analysis performed.

There is growing evidence that AI can be applied in various situations, particularly as a supporting tool in prognostic, diagnostic and auto-contouring applications and to provide patients with a more individualized treatment plan. DL-based algorithm was found to be the most frequently used AI subfield and usually obtained good results when compared to other methods. However, limited dataset and generalizability are key challenges that need to be overcome to further improve the performances and accessibility of AI models. Nevertheless, studies on AI demonstrated highly promising potential in supporting medical professionals in the management of NPC; therefore, more concerted efforts in swift development is warranted.

Dr Nabil F Saba reports personal fees from Merck, GSk, Pfizer, Uptodate, and Springer, outside the submitted work; and Research funding from BMS and Exelixis. Professor Raymond KY Tsang reports non-financial support from Atos Medical Inc., outside the submitted work. The authors report no other conflicts of interest in this work.

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Artificial intelligence in the management of NPC | CMAR - Dove Medical Press

The Indian Navy conducted a three-day-long virtual workshop at Indian naval station (INS) Valsura in Jamnagar of Gujarat on leveraging artificial intelligence, an official statement said on Thursday.

The workshop, which was conducted under the aegis of southern naval command, concluded on January 21, the Indian Navy's statement noted.

"Speakers from renowned IT companies like Google, IBM, Infosys and TCS shared the industry perspective during the three-day event," it said.

Distinguished academicians from IIT Delhi, New York University, Amrita University and DA-IICT also spoke about the latest trends and applications of artificial intelligence, it said.

"The webinar conducted saw online participation by over 500 participants from across the country," it added.

The Indian Navy is focused on incorporation of artificial intelligence and machine learning in critical mission areas, it stated.

(Only the headline and picture of this report may have been reworked by the Business Standard staff; the rest of the content is auto-generated from a syndicated feed.)

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Indian Navy conducts workshop on artificial intelligence in Jamnagar - Business Standard

In an exclusive video interview with Express Computer, Seema Prem, Co-founder and CEO, FIA Global shares about the companys investment in Artificial Intelligence and Machine Learning in the last five years for financial inclusivity in the country.

FIA, a financial inclusivity neo bank delivers financial services through its app, Finvesta. The app employs AI, facial recognition and Natural Language Processing to aggregate, redesign, recommend and deliver financial products at scale. The app uses icons for user interface, for ease of use where literacy levels are low.

Seema Prem, Co-founder and CEO, FIA says, We have reaped significant benefits by incorporating AI and ML in our operations. So we handle very tiny transactions and big data. The algorithm modules, especially rule-based modules have reached a certain performance plateau. AI and ML have been incorporated for smart bot applications for servicing the customers, audit where we look at embedding facial recognition, pattern detection for predicting the performance of business, analysing large volumes of data and many more. It helps us to ensure that manual intervention comes down significantly. Last year, after the pandemic we automated like there is no tomorrow and that automation has resulted in huge productivity for us.

FIAs role in the financial inclusivity in India is largely associated with Pradham Mantri Jan Dhan Yojana where they tie-up with banks to set up centres in very remote and secluded regions of India like Uri, Kargil, Kedarnath, Kanyakumari, etc.

Prem states, We work in 715 districts of the country in areas like a bank branch that have never been there. Once the bank account opens in such areas then people get the confidence in remote areas for banking. Eventually, we try to fulfil the needs of people for other products like pension, insurance, healthcare, livestock loans, vehicle insurance and property insurance. We provide doorstep delivery of pension to our customers. So our services also endure community engagement besides financial inclusivity targeting various special groups like women and old age people.

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MONTRAL, Jan. 27, 2022 /CNW Telbec/ - TheGlobal Partnership on Artificial Intelligence (GPAI) has announced thatMila researcher Catherine Rgis, full professor at theUniversity of Montral Law Faculty and a Canada Research Chair, is the new co-chair of its working group on responsible AI. Her term will extend until 2024. The GPAI was established in 2020 to support collaboration among experts from 25 countries and promote responsible practices in artificial intelligence.

"We are grateful for this recognition of Qubec expertise in responsible artificial intelligence," says Benjamin Prud'homme, Executive Director, AI for Humanity at Mila. "As a legal scholar with expertise in AI governance and regulation, Catherine Rgis is an outstanding choice for this position."

"The priorities for 2022 will include ensuring that AI development respects human rights and encouraging responsible innovation," explains Catherine Rgis. "We must also work to make sure AI development is equitable and inclusive."

The other co-chair of the working group will be Raja Chatila of France, emeritus professor of robotics, artificial intelligence and ethics at Sorbonne University.

The working group on responsible AI is conducting several projects on timely issues, including:

SOURCE Mila - Quebec AI Institute

For further information: Diep Truong, 514 436-2121, [emailprotected]

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Catherine Rgis to co-chair the Global Partnership on Artificial Intelligence Working Group on Responsible AI - Canada NewsWire