Category Archives: Artificial Intelligence

CAMBRIDGE, Mass.--(BUSINESS WIRE)--Iterative Scopes, a pioneer in precision-medicine technologies for gastroenterology, announced today that it has partnered with One GI, a gastroenterology management services organization (MSO), to bring its AI Recruitment (AIR) technology to One GIs clinical research. Initially, One GI will use AIR at Gastro One, one of One GIs clinical research sites in Memphis, Tennessee, with plans to expand to additional research sites within the One GI network. The implementation of AIR will aid in the recruitment of patients for clinical trials focused on inflammatory bowel disease (IBD).

Both One GI and Iterative Scopes are fast-growing innovators in the gastroenterology space. One GI is a unique, rapidly expanding MSO that brings together community-based practices, including Gastro One, a network of six practices across Tennessee. The MSO currently has 40 sites within its network across Tennessee, Mississippi, Kentucky, Ohio, and Indiana. Iterative Scopes is a pioneer in the application of powerful, proprietary artificial intelligence tools to the practice of gastroenterology and drug development.

The partnership between the two organizations will allow One GI physicians to leverage the AI-based technology from Iterative Scopes to standardize the endoscopic scoring process for patients and identify likely eligible patients to enroll in IBD clinical trials. By initiating the collaboration at Gastro One, the organizations are taking initial steps to develop a hub and spoke recruitment model for One GI, which will democratize access to the novel IBD therapeutics for all patients, regardless of where they receive care.

Our partnership with Iterative Scopes enables the delivery of innovative technology to community-based gastroenterology practices, demonstrating One GIs commitment to physician-centered, patient-focused healthcare, said Robbie Allen, CEO of One GI. Our ultimate mission is to power practices regardless of their location to deliver world class GI care, and implementing AIR will allow a broader array of patients to access potentially life-changing clinical trials.

In addition to helping us offer the most advanced patient care, this partnership fuels the continuous growth of our research department so that we remain at the forefront of GI care, improving outcomes and patients lives, said Richard Aycock, MD, Gastro One senior physician. Dr. Ziad Younes, a global leader in gastroenterology, is pioneering the program within our practices, and were enthusiastic about the potential to help advance the GI field even further.

AIR works to simplify the process of matching patients to clinical trials by helping gastroenterologists better understand disease severity more quickly and consistently. The technology uses computational algorithms integrated with existing colonoscopy imagery and videos to determine clinical trial eligibility, speeding up the clinical trial timelines and allowing more patients to access potentially life-changing treatments. The companys technology automates scoring of a patients minimum threshold score of disease severity, as measured by the Mayo Endoscopic Score (MES), as an aid in determining their qualification for pre-screening for IBD clinical trials. Iterative Scopes has already partnered with multiple clinical research sites for IBD and is continuing to expand its reach with notable partners like One GI.

Iterative Scopes mission is to democratize access to high quality care for patients struggling with IBD and other GI conditions, ultimately reducing healthcare disparities in clinical practice, said Sahir Raoof, VP of Business Development at Iterative Scopes. We are thrilled to be partnering with Dr. Younes and One GI to drive clinical adoption of artificial intelligence to help us advance clinical trial recruitment in an effort to help bring novel therapeutics to IBD patients faster.

To learn more about Iterative Scopes and AI-Recruitment, visit the companys website: https://www.iterativescopes.com/

About Iterative ScopesIterative Scopes is a pioneer in the application of artificial intelligence-based precision medicine for gastroenterology with the aim of helping to optimize clinical trials investigating treatment of inflammatory bowel disease (IBD). The technology is also designed to potentially enhance colorectal cancer screenings. Its powerful, proprietary artificial intelligence and computer vision technologies have the potential to improve the accuracy and consistency of endoscopy readings. Iterative Scopes is initially applying these advances to impact polyp detection for colorectal cancer screenings and working to standardize disease severity characterization for inflammatory bowel disease. Longer term, the company plans to establish more meaningful endpoints for GI diseases, which may be better predictors of therapeutic response and disease outcomes. The company is based in Cambridge, Massachusetts.

About One GIOne GI, the Home for Independent Gastroenterology, is a gastroenterology management services organization that partners with GI physicians empowering them to manage, optimize, and grow their practices. One GI provides state of the art resources to physicians so they can focus on what they do best: providing excellent care to patients. For additional information on One GI, please visit http://www.onegi.com.

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Iterative Scopes Partners with One GI to Advance Gastrointestinal Care Through Artificial Intelligence - Business Wire

Review time shortened from an average of nine days to less than one day

BOSTON, Oct. 12, 2022 /PRNewswire/ --Blue Cross Blue Shield of Massachusetts("Blue Cross") today announced the completion of a proof-of-concept pilot called "FastPass," an automated prior authorization process from end-to-end, eliminating the need for faxes, phone calls and manual processes for payers and providers. The initiative, piloted at New England Baptist Hospital (NEBH), focused on addressing the major problem areas, including reducing the time from submission to decision, alleviating administrative burden, decreasing clinical review time, and increasing clinician satisfaction.

The ProblemPrior Authorization (also known as "Pre-Certification") is a process through which a clinician seeks advanced approval from a health plan to ensure that a service or treatment is covered, medically necessary, and not duplicated. Prior authorizations exist to manage excess health care costs and mitigate patient risk while also helping ensure consumers receive high-quality care. However, prior authorization can be cumbersome for clinicians.

"We realize that the prior authorization process is widely recognized as the single biggest administrative pain point for hospital staff," said Kathy Gardner, RN, vice president of clinical operations at Blue Cross. "We wanted to figure out a way to retain the value of prior authorizations ensuring our members receive treatments that are medically necessary and clinically effective while eliminating the administrative burden on our clinical partners and allowing members to get the care they need sooner."

How it worksBlue Cross engaged Olive, a leading automation and intelligence company bridging the divide in health care, to help streamline both clinician and payer processes and prior authorization decision-making using artificial intelligence (AI).

The technology automated the process of cross-checking Blue Cross' prior authorization requirements in real-time to identify if a prior authorization was required. If a prior authorization was not required, the provider received instant notification that they could proceed with scheduling the procedure. When prior authorizations were required, FastPass used AI to cross-check the clinical history in the electronic medical record against Blue Cross' medical necessity criteria and automatically generate a recommendation in real time, again giving the clinician the ability to proceed with scheduling the procedure. For the remaining prior authorization submissions that required more complex clinical review, FastPass automatically packaged and made available all the clinical documentation and notes to the clinical review team, significantly streamlining and accelerating the reviews.

The ResultsThe pilot at NEBH focused on hip and knee procedures for 32 orthopedic providers over the course of a four-month period. 88% of prior authorization submissions were processed automatically in real-time. The overall impact on prior authorization approval time went from an average of nine days to an average of less than one day. The associated impact on administrative burden and cost has been significant for Blue Cross.

"The FastPass proof-of-concept is just one step in our journey toward automating prior authorizations across BCBSMA to continue to make the process frictionless for our clinical partners and ultimately our members," said Deb Vona, senior director of business operations at Blue Cross.

About Blue Cross Blue Shield ofMassachusettsBlue Cross Blue Shield ofMassachusetts(http://www.bluecrossma.org) is a community-focused, tax-paying, not-for-profit health plan headquartered inBoston. We are committed to the relentless pursuit of quality, affordable and equitable health carewithan unparalleled consumer experience.Consistent with our promise to always put our members first, we are rated among the nation's best health plans for member satisfaction and quality.Connect with us onFacebook,Twitter,YouTube,andLinkedIn.

About OliveOlive delivers automation and intelligence to bridge the divide in healthcare. By addressing the most burdensome operational issues, Olive is reducing costs and increasing capacity for hospitals, health systems and payers, so the focus can remain on delivering the best, most effective care to patients. To learn more about Olive, visit oliveai.com.

SOURCE Blue Cross Blue Shield of Massachusetts

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BLUE CROSS BLUE SHIELD OF MASSACHUSETTS USES ARTIFICIAL INTELLIGENCE TO SPEED REVIEW TIME, AUTOMATE AUTHORIZATIONS & ELIMINATE ADMINISTRATIVE...

DUBAI, United Arab Emirates--(BUSINESS WIRE)--Hewlett Packard Enterprise (NYSE: HPE) today announced it is building a new supercomputer for Mohamed bin Zayed University of Artificial Intelligence (MBZUAI), a university dedicated solely to AI, to accelerate AI-driven scientific discovery and advance the UAEs goal to be a global AI leader. HPEs robust supercomputing and AI technologies will significantly enhance the universitys ability to run complex AI models with extremely large data sets, and increase predictability in research analyses in fields including energy, transportation and the environment.

Supercomputing is vital to delivering AI-at-scale, and driving global innovation, industry competitiveness, and economic growth. From accelerating vaccine discovery to fight a pandemic, and advancing clean energy systems to increase sustainability, to enabling new possibilities in AI, supercomputing is a core technology to solving the worlds most challenging scientific and engineering challenges.

Established in 2019, MBZUAI has already ranked 24th in the world for artificial intelligence, computer vision, machine learning, and natural language processing. The new supercomputer will help MBZUAI support the UAEs National Strategy for AI, bringing together the power of academia, government, and industry to strengthen the countrys global competitiveness. The advanced supercomputing and AI technologies will also expand resources for larger projects to allow MBZUAI to attract global talent and create new economic and societal opportunities for the UAE.

MBZUAIs new Campus Super Computing Center (CSCC), where the new supercomputer will be housed, will enable faculty members, researchers, and students to accelerate scientific discovery in many areas, including healthcare, structural engineering, law enforcement, supply chain, and sustainability. In addition to meeting the research needs of the faculty and students, the new supercomputer will help MBZUAI fulfill its role as an AI talent developer and innovation hub that brings together the business community to drive entrepreneurship in the AI sector. MBZUAI already is contributing to significant initiatives such as the Emirati Genome Program, which uses AI-based tools to extract and interpret large amounts of complex data resulting from DNA sequencing, and the Abu Dhabi Health Services Company, SEHA, which uses AI algorithms to predict heart attacks.

Supercomputing plays an essential role in unleashing AI to achieve significant breakthroughs for organizations worldwide, across public and private sectors, said Justin Hotard, executive vice president and general manager, HPC, AI & Labs, at HPE. HPE is leading the market in this next frontier by combining supercomputing performance and capabilities with solutions that are purpose-built for AI, to build and train machine learning models at-scale. MBZUAIs Campus Super Computing Center is demonstrating this capability to unlock new possibilities in AI and strengthen UAEs position as an AI-driven nation to advance key initiatives in healthcare, sustainability, and engineering.

HPE has a longstanding commitment to advancing the way people live and work in the United Arab Emirates, through our Emiratization program, Digital Life Garage, and industry and government partnerships, said Ahmad Alkhallafi, managing director for UAE, Hewlett Packard Enterprise. We are proud to support the UAEs national strategy for AI by helping MBZUAI grow its contribution to research and education. Supercomputing will play a crucial role in helping the UAE anticipate and take advantage of new AI technologies now and in the future. We look forward to helping the UAE meet its goal to be an AI leader by 2031.

As a recently established institution, MBZUAI is still building its team of in-house supercomputing specialists. By collaborating with HPE, the university is gaining access to a large local team for supercomputing and AI support, in addition to world-leading technologies.

The new supercomputer will deliver end-to-end technologies, based on the HPE Apollo 6500 Gen10 Plus, which is purpose-built for AI and analytics workloads, in addition to modeling and simulation workloads that are critical to scientific research. As part of the design, HPE will feature 2nd Gen AMD EPYC processors delivering advanced computational performance, and 384 NVIDIA A100 Tensor Core GPUs for accelerated compute to target AI model development, training, and inferencing.

To support AI training needs that require processing and storing large quantities of data, HPE will also deliver four petabytes of storage using HPEs Cray Clusterstor E1000 parallel storage system, which is built for large-scale systems, to enable expanded storage capacity. Additionally, with the new supercomputers design, MBZUAI will gain sophisticated liquid-cooling capabilities from HPE to efficiently remove heat from high-power devices, including CPUs, GPUs, memory, and switches.

About Hewlett Packard Enterprise

Hewlett Packard Enterprise (NYSE: HPE) is the global edge-to-cloud company that helps organizations accelerate outcomes by unlocking value from all their data, everywhere. Built on decades of reimagining the future and innovating to advance the way people live and work, HPE delivers unique, open and intelligent technology solutions as a service. With offerings spanning Cloud Services, Compute, High Performance Computing & AI, Intelligent Edge, Software, and Storage, HPE provides a consistent experience across all clouds and edges, helping customers develop new business models, engage in new ways, and increase operational performance. For more information, visit: http://www.hpe.com.

About Mohamed bin Zayed University of Artificial Intelligence (MBZUAI)

MBZUAI is a graduate, research university focused on artificial intelligence, computer science, and digital technologies across industrial sectors. The university aims to empower students, businesses, and governments to advance artificial intelligence as a global force for positive progress. MBZUAI offers various graduate programs designed to pursue advanced, specialized knowledge and skills in artificial intelligence, including computer vision, machine learning, and natural language processing. For more information, please visit http://www.mbzuai.ac.ae.

AMD, the AMD Arrow logo, EPYC, and combinations thereof, are trademarks of Advanced Micro Devices, Inc. Other names are for informational purposes only and may be trademarks of their respective owners.

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Mohamed bin Zayed University of Artificial Intelligence Advances the UAE's National Strategy for AI with New Supercomputer Built by Hewlett Packard...

Image by Gerd Altmann (CC0C/Pixabay)

Over the last decade or so, artificial intelligence (AI) has become more and more prevalent in everyday life, from the online ads that seem to know just what youre looking for to music composition and other creative applications.

The very notion of making music with AI raises questions about the nature of creativity, and of the future for human composers. From useful tools to pioneering prototypes, heres a look at some of the most recent innovations that use AI in the music writing process.

DoReMIR Music Research AB recently announced the launch of ScoreCloud Songwriter, a tool that turns original music into lead sheets. The software uses the information recorded with a single microphone, and can include vocals and instruments. Various AI protocols separate out the vocals, and then transcribes the music, including melody and chords, along with the lyrics in English. What youll get is a lead sheet with the melody, lyrics, and chord symbols.

Many established and emerging songwriters are brilliant musicians but struggle with notating their music to make it possible for others to play, explained Sven Ahlback, DoReMIR CEO, in a media release. Our vision is that ScoreCloud Songwriter will help songwriters, composers, and other music professionals, such as educators and performers. It may even inspire playful use by lovers of music who never thought they could write a song. Our hope is that it will become an indispensable tool for creating, sharing, and preserving musical works.

Harmonai is a company that creates open-source models for the music industry, and Dance Diffusion is their latest innovation in AI audio generation. It uses a combination of publicly available models to create audio bits so far, about 1-3 seconds long from nothing, as it were, which can then be interpolated into longer recordings. Since its AI, the more users enter audio files for it to learn from, the more it will evolve and develop. If youre interested in how Dance Diffusion came together, theres a video interview with the creators here.

Heres one of their projects, an infinite AI bass solo that has been playing since January 27, 2021. Its based on the work of the late musician Adam Neely.

Its still in the testing stages, but its implications are profound.

Googles new AudioLM bases its approach to audio generation on the way language is processed. It can generate music for piano with a short excerpt of input. Speech combines sounds into words and sentences, in the same way the music is about individual notes that come together to form melody and harmony. Google engineers used the concepts in advanced language modelling as their guide. The AI captures melody as well as overall structure, and the details of the audio waveform to create realistic sounds. It reconstructs sounds in layers designed to capture the nuances.

Metas new AudioGen uses a text-to-audio AI model to create sounds as well as music. The user enters a text prompt, such as wind blowing, or even a combination, such as wind blowing and leaves rustling and the AI responds with a corresponding sound. The system was developed by Met and the Hebrew University of Jerusalem, and it is able to generate sound from scratch. The AI can separate different sounds from a complex situation, such as several people speaking at once. Researchers trained the AI using a mix of audio samples, and it can generate new audio beyond its training dataset. Along with sounds, it can generate music, but that part of its functionality is still in its infancy.

With AI music generation in its infancy, its easy to dismiss its future impact on the industry. But, it cant be ignored.

An electronic band by the name of YACHT recorded a full album with AI in 2019, using technology thats already been surpassed. Essentially, they taught AI how to be YACHT, and it wrote the music. The band then turned it into their next album.

Im not interested in being a reactionary, YACHT member and tech writer Claire L. Evans mentioned that ambivalence in a documentary about their 2019 AI-assisted album Chain Tripping (as quoted in Tech Crunch). I dont want to return to my roots and play acoustic guitar because Im so freaked out about the coming robot apocalypse, but I also dont want to jump into the trenches and welcome our new robot overlords either.

The onslaught of new technology is relentless. The only choice is to hop on the train.

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Anya Wassenberg is a Senior Writer and Digital Content Editor at Ludwig Van. She is an experienced freelance writer, blogger and writing instructor with OntarioLearn.

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REPORT | Music And Artificial Intelligence: A Bond Thats Growing By Leaps And Bounds - Ludwig Van

A team of LSU researchers has developed a way to determine which drug therapies work best against an individual's unique type of cancer, possibly providing a way to find cures more quickly and make treatment more affordable.

The interdisciplinary team includes researchers from the School of Veterinary Medicine, College of Science, College of Engineering and the Center for Computation & Technology. It created CancerOmicsNet, a new drug discovery engine run by artificial intelligence.

Using algorithms originally designed to map complex social networks, like those utilized by Facebook, researchers generated three-dimensional graphs of molecular datasets that include cancer cell lines, drug compounds and interactions among proteins inside the human body.

The graphs are then analyzed and interconnected by AI, forming a much clearer picture of how a specific cancer would respond to a specific drug.

Dr. Michal Brylinski, associate professor of computational biology at LSU, said that the team used established datasets to train the CancerOmicsNet engine into using artificial intelligence.

"Once its trained, then you can ask for something that you dont know and this is the input data," he said. "So you ask what inhibitor you think is going to be effective against this cancer and then AI makes a prediction. Thats the implication to unseen data and then something like that goes to a wet lab and we can validate it.

Wet lab research was conducted by researchers at the LSU School of Veterinary Medicine and led by associate professor of research Brent Stanfield.

They developed the AI algorithm and everything, so our role in the study is just to be the practical applications of the technology," Stanfield said. "They developed the algorithm, identified the drugs and then we tested the drugs in our high-capacity systems to demonstrate their efficacy to kill cancer cells.

Researchers studied notoriously aggressive breast, prostate and pancreatic cell lines to train the AI to recognize connections between specific cancers and cancer drugs that control the production of the enzyme kinase within the body.

Kinase acts as a biological catalyst for cell communication and cell growth. Using drugs that lower kinase activity can suppress the growth of cancerous cells.

Brylinski said the research team used CancerOmicsNet to pick out six combinations of cancer cell lines with the drugs likely to be the most toxic to their gene expression profile and tested them, with encouraging results.

According to acceptable criteria, four out of six worked and this success rate is extremely high because if you just picked up six random drugs and say those drugs are going to work on this cancer, then theyre probably not going to work on that cancer," he said. "Four out of six was very encouraging and this is where we stand right now."

Using CancerOmicsNet like molecular speed dating, the AI can help researchers quickly match cancer cell lines with the drugs likely to be the most toxic to their growth and genetic profile.

Brylinski said knowledge gained through CancerOmicsNet can help overcome the challenge of determining how effective a particular kinase-inhibiting drug could be in the future.

The ultimate goal, he said, is to expand their research to potentially apply it in clinical settings.

"If we have a patient with a certain cancer, they can do a biopsy and then they can profile this cancer with respect to gene expression, genetic mutations and everything," Brylinski said. "Then they can input that data to CancerOmicsNet and it can suggest some therapy for this particular cancer and say this drug could be effective and 'another drug could not be effective.'

The effectiveness of various cancer drugs was initially believed to be tied to molecular consistency, the idea that cancer treatment should be targeted to a specific to a location in the body.

Michelle Collins, dean of the College of Nursing and Health at Loyola University New Orleans and a scientist not involved in the LSU research, said CancerOmicsNet is an example of how our current medical understanding of cancer treatment meets advances in genetic studies and AI.

When cancer drugs first came out, they were one size fits all and werent really tailored to the individual and so you see the medications work better on some people than others," she said. "And with the advent of genetics and genomics, which are the future of medicine, were now going to be able to tailor treatments to the patient and not just in oncology.

Collins said she sees CancerOmicsNet being extremely beneficial to oncological studies and treatment in the future.

I think it has the potential to really revolutionize the field of oncology, because well be able to treat people with medication that is more timely tailored to them," she said. "All of that is good if youre a patient with cancer.

Brylinksi said that the ability to treat cancer with a more direct, focused clinical approach makes him excited to see how CancerOmicsNet develops over time.

"I dont know if were going to make some major breakthrough in oncology any time soon, but were contributing pieces where if enough people are doing this, the whole field is moving forward towards the goal of improving human health," he said. "Were very happy that we can make some contribution, which might not be a huge breakthrough down the road, but definitely something that is useful to improving human health and thats really cool actually.

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Can artificial intelligence help identify best treatments for cancers? LSU researchers say yes - The Advocate

TheAmazonVirginia Tech Initiative for Efficient and Robust Machine Learninghas announced support for two Amazon Fellows and four innovative research projects led by Virginia Tech faculty that further the initiatives mission of advancing new directions in machine learning.

Funded by Amazon, housed in theCollege of Engineering,and directed by researchers at theSanghani Centerfor Artificial Intelligence and Data Analyticson Virginia Techs campus in Blacksburg and at the Innovation Campus in Alexandria, the initiative was launched in March to support student and faculty-led development and implementation of innovative approaches to robust machine learning such as ensuringthat algorithms and models are resistant to errors and adversaries that could address worldwide industry-focused problems.

An open call for student fellowship nominations and research projects went out concurrently across the Virginia Tech campuses. The initiatives advisory committee, comprised of Virginia Tech faculty and Amazon researchers, selected two Amazon Fellows from among 11 nominations and four faculty award recipients from 14 submissions.

Our inaugural cohort of fellows and faculty-led projects showcases the breadth of machine learning research happening at Virginia Tech, saidNaren Ramakrishnan, the Thomas L. Phillips Professor of Engineering and director of the Amazon-Virginia Tech Initiative. The areas represented include federated learning, meta-learning, leakage from machine learning models, and conversational interfaces.

This research will not only contribute to new algorithmic advances, but also study issues pertaining to practical and safe deployment of machine learning, said Ramakrishnan who also directs theSanghani Centerfor Artificial Intelligence and Data Analytics. We are very excited that the partnership between Amazon and Virginia Tech has enabled these projects.

"The talent and depth of scientific knowledge at Virginia Tech is reflected in the high-quality research proposals and Ph.D. student fellowship applications we have received, said Prem Natarajan, vice president of Alexa AI. I am excited about the new insights and advances in robust machine learning that will result from the work of the faculty and students who are contributing to this initiative."

The Amazon Fellows are:

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Amazon Fellows and faculty-led projects advance innovations in machine learning and artificial intelligence - Virginia Tech Daily

IRVINE, Calif.--(BUSINESS WIRE)--Genomic Testing Cooperative, LCA (GTC) announced that its innovative artificial intelligence (AI) algorithms are now formally implemented in daily use to aid pathologists in the diagnosis and interpretation of molecular findings of genomic profiling.

GTCs RNAnalysis algorithm is used to distinguish between 45 different diagnostic classes providing probability scores. This algorithm is complemented by a second algorithm called TraceWork. When needed, TraceWork is used to distinguish between two diagnostic entities determined by RNAnalysis to be of similar high probability score.

Results of validation of these algorithms are now published in The American Journal of Pathology, a part of Elsevier's Journal Network (DOI:https://doi.org/10.1016/j.ajpath.2022.09.006). For example, independent blind testing of RNAnalysis algorithm showed correct first-choice diagnosis in 100% of acute lymphoblastic leukemia, 88% of acute myeloid leukemia, 85% of diffuse large B-cell lymphoma, 82% of colorectal cancer, 49% of lung cancer, 88% of chronic lymphocytic leukemia and 72% of follicular lymphoma. The TraceWork algorithm distinguished between lung cancer and colorectal cancer with 97.2% sensitivity and 94.5% specificity, between Hodgkin lymphoma and normal lymph node with 95.4% sensitivity and 100% specificity, between follicular lymphoma and diffuse large B-cell lymphoma with 95.9% sensitivity and 93.1% specificity, and between breast cancer and ovarian cancer with 100% sensitivity and 94.2% specificity.

The information provided by these algorithms are used in the context of clinical and other molecular and pathologic findings and not meant to replace the need for physicians clinical decision, said Dr. Maher Albitar, founder, chief medical officer, and chief executive officer of GTC. We believe that transcriptomic data when combined with AI provides an efficient and effective information that can replace the need for large number immunohistochemical staining and flow cytometry testing, especially when tissue samples are scant, Dr. Albitar added.

Dr. Andre Goy, Chairman & Chief Physician Officer at John Theurer Cancer Center and Academic Chairman of Oncology at Hackensack Meridian School of Medicine, stated, Precision diagnosis is extremely important for the practice of precision medicine. Todays RNA and DNA profiling generates big data that requires sophisticated algorithms to decipher the clinical relevance of this data. GTCs molecular profiling and algorithms had helped us resolve numerous diagnostically challenging cases and the results made a difference in patients management and outcome.

Dr. Aamir Ehsan, CEO/ President, Medical Director and board-certified hematopathologist and molecular geneticist of CorePath laboratories, at San Antonio, Texas, who is a collaborator and coauthor on the published work, said, Unlike AI and imaging, transcriptomic data and AI incorporates immunohistochemistry and flow cytometry data as well as numerous additional biomarkers, but more importantly allows us to look at each biomarker individually to make the final pathologic decision. This represents major advances in the practice of pathology.

It is estimated that approximately 10% of all cancer cases are misdiagnosed and 4% of solid tumors are presented as cancer of unknown primary CUP.

About Genomic Testing Cooperative, LCA

Genomic Testing Cooperative (GTC) is a privately-owned molecular testing company located in Irvine, CA. The company operates based on a cooperative (co-op) business model. Members of the co-op hold type A shares with voting rights. The company offers its patron members a full suite of comprehensive genomic profiling based mainly on next generation sequencing. Molecular alterations are identified based on rigorous testing with the aid of specially developed algorithms to increase accuracy and efficiency. The clinical relevance of the detected alterations is pulled from numerous databases using internally developed software. Relevance of findings to diagnosis, prognosis, selecting therapy, and predicting outcome are reported to members. The co-op model allows GTC to make the testing and information platform available to members at a lower cost because of a lower overhead. For more information, please visit https://genomictestingcooperative.com/.

Forward Looking Statements

All of the statements, expectations and assumptions contained in this press release are forward-looking statements. Such forward-looking statements are based on the GTC managements current expectations and includes statements regarding the value of comprehensive genomic profiling, RNA profiling, DNA profiling, algorithms, therapy, the ability of testing to provide clinically useful information. All information in this press release is as of the date of the release, and GTC undertakes no duty to update this information unless required by law.

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Genomic Testing Cooperative and its Co-Op Members First to Use Artificial Intelligence to Distinguish Between 45 Hematologic and Solid Neoplasms Using...

In this article, John Bohlmann, founder and CEO, HawkenQA, dives into smart building technology, how it works, and how AI has empowered the tech to go mainstream while cutting costs, automating operations, and creating a healthy environment that impacts carbon emissions.

When most people think about carbon emissions, they think about rush hour traffic jams and industrial activities. Many experts urge people to look closer to home when considering how to make a greener world. However, few imagine that the building where they live or work is what experts are referring to.

The United Nations Environment Programme (UNEP) explains that buildings are responsible for one-third of global greenhouse gas emissions, using 40% of global energy, and the sector is the largest contributor to carbon emissions.

But how can buildings have such an impact on the global climate? The answer is simple: operation and construction. In operation, emissions can be constant. These are driven by electricity use, heating, ventilation, air conditioning (HVAC), and equipment. Buildings also have an embodied carbon, which refers to the carbon footprint generated during construction. This carbon factor includes everything from running the construction equipment to the carbon emissions generated through the supply chain, for example, when sourcing steel and concrete.

See More: Avoid Flightmares with AI-enabled Virtual Agents

Despite these severe impacts, buildings have one of the most significant potentials for green transformation. Today, data-driven technology empowered by machine learning models or artificial intelligence (AI) creates smart buildings where the software automatically integrates with the buildings components. HVAC, air quality, temperature, energy use, occupancy, downtime hours, ventilation, and many other factors can be continually monitored with sensors paired with monitoring technology and can make automated decisions to optimize performance.

Installing smart building platforms was challenging for years because every building is different. Technology has tackled this problem using AI, which can reduce the time and cost of installation. AI-based building solutions are a big step in the right direction toward reducing carbon emissions, not only in the U.S. but globally. Today, smart building platforms are more advanced, more affordable, easier to install, and easier to use than ever.

Reducing the carbon emissions of a building doesnt mean turning off the lights. It means optimizing resources like airflow, electricity, and water or installing solar panels. Indoor air quality technology is essential to a low carbon footprint due to the significant energy consumption that HVAC systems have. Indoor air monitoring technology uses AI to intelligently control the HVAC-energy balance without compromising the comfort and health of the people inside a building.

AI learns the habits of the occupants of a building and can predict when to increase, stabilize, or decrease the usage of heating and air conditioning systems. Additionally, these new systems can be space-specific, only applying changes in the rooms or common areas needed. The same AI can optimize air quality in the building by revealing and acting on sources of indoor air pollution like CO2, humidity, or high particulate matter.

AI can also improve operations and maintenance. When smart building systems are integrated with smart devices or the IoT, the AI will detect any abnormality in a device. For example, if a heating device malfunctions, it will use less or more energy and affect the temperature of the room. Therefore, the AI can identify if a device is having a problem, make adjustments, and notify management. Smart AI systems can also manage scheduled HVAC filter changes or other maintenance and review and approve work orders.

The automation processes not only eliminate human error from the equation but also save building owners a significant amount of labor, energy spending, and work costs that were previously done manually.

New trends in reducing carbon emissions in the building construction sector include using alternative green materials or sourcing materials from low-carbon producers. Additionally, architects and engineers are integrating solar, wind, and alternative renewable energy sources into construction to maximize the buildings operations and reduce their impact on the grid.

On the other hand, during construction, its essential to maximize natural resources through innovative ventilation mechanisms and manage temperature by using windows to keep the sunlight in during cold seasons and keep it out during summertime.

Decision makers should be following new developments closely in smart buildings due to the many benefits they provide. Smart buildings can bring down carbon emissions, improve performance, build a good reputation, and reduce liability.

Next-generation smart building platforms also enable radically better financial outcomes for real estate managers. Green buildings rents and sale prices are higher, and vacancy rates are significantly lower. On the other hand, smart-AI technology can have direct economic savings. Typically businesses pay $1 per square foot per month on energy, $10/sqft/month on office rent, and $100/sqft/month on employee salaries.

But leaders investing in smart-AI building tech are flipping this economic equation for buildings while adding significant wellness and health value and creating better green workplaces.

According to the U.S. Department of Energy, smart building technology can reduce energy by more than 60% in residential buildings and up to 59% in commercial buildings. Additionally, investing in AI smart building technology that provides good indoor air quality standards has proven to improve employee productivity and cognitive function in office buildings by more than 10%.

See More: From raw data to ML models: The magic behind AI-powered feature engineering

Deloitte Insights, an international professional services network, explains that smart buildings marry physical assets with the digital fabric that connects spaces. The organization explains that leaders shouldnt spend money on smart building technology without having a clear strategy. Before installing smart building technology, decision-makers should draft a solid business case outlining the benefits and savings, goals, revisions, and milestones.

Additionally, smart building technology should factor in technical needs (energy savings) and consider human elements such as wellness, comfort, health, and performance. AI is undoubtedly an innovation disruption, and data is the raw material that drives it. However, data governance and data management are essential. Buildings should never collect data without identifying what it will be used for. Finally, choosing a flexible technology that will allow users to install updates and keep up with modern trends is essential.

Have you seen smart building solutions in action lately? Tell us about it on Facebook, Twitter, and LinkedIn. Wed love to know!

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Reducing Carbon Emissions With AI And Smart Building Technology - Spiceworks News and Insights

About Focal Systems:

Founded in 2015 in San Francisco out of Stanfords Computer Vision Lab, Focal Systems is the industry leader in retail automation. Our mission is to automate and optimize brick and mortar retail with state-of-the-art deep learning and AI. We have pioneered the worlds first Self-Driving Store- an OS that revolutionizes how stores are run. Focal has raised more than $40M to date and scaled solutions on three continents in hundreds of stores, with over 100,000 cameras deployed. Learn more at: https://focal.systems.

Contact:

Lizzy Harris for Focal Systems

[emailprotected]

1-303-503-1136

About Piggly Wiggly:

Building on its more than 100-year history in the grocery business, Piggly Wiggly continues to grow its presence with stores throughout the Midwest, South and Northeast. C&S Wholesale Grocers, Inc. operates corporate stores and services independent franchisees under a chain-style model. This unique grocery store offers the selection and assortment of a national chain, with the service and local customization of a community-based retailer. Each store contains specialized local assortments to meet local shoppers needs.

Piggly Wiggly Midwest: https://www.shopthepig.com/

Piggly Wiggly Carolinas: https://www.thepig.net/

About C&S Wholesale Grocers, Inc. C&S Wholesale Grocers, Inc. is an industry leader in supply chain solutions and wholesale grocery supply in the United States. Founded in 1918 as a supplier to independent grocery stores, C&S now services customers of all sizes, supplying more than 7,500 independent supermarkets, chain stores, military bases and institutions with over 100,000 different products. We are an engaged corporate citizen, supporting causes that positively impact our communities. To learn more, please visit http://www.cswg.com.

C&S Media Contact:

Lauren La Bruno

Vice President of Communications, Change Management and Community Relations

C&S Wholesale Grocers, Inc.

[emailprotected]

C&S Investor Relations:

Julie Drake

Vice President, Assistant Treasurer

C&S Wholesale Grocers, Inc.

[emailprotected]

Piggly Wiggly Marketing Contact:

Molly Rippinger

Director, Marketing

Piggly Wiggly Midwest

[emailprotected]

Excerpt from:

Piggly Wiggly Midwest Partners with Focal Systems to Leverage Artificial Intelligence and Automation to Transform the Shopper Experience - Grocery...

MSPs, Edge Providers, and Large Enterprises will find CloudFabrix's domain-agnostic, distributed, Robotic Data Automation fabric-based AIOps model appealing

PLEASANTON, Calif., Oct. 13, 2022 /PRNewswire/ -- CloudFabrix the inventor of Robotic Data Automation Fabric Platform and the Data-centric AIOps Leader, was named a Leader and Innovator in the 2022 Gigaom Radar for AIOps report for a 2nd consecutive year. The report evaluated 24 major AIOps vendors across Key criteria and evaluation metrics that should be applied when selecting an AIOps solution. The report segregates the 24 vendors across Leaders, Challengers, and New Entrants. CloudFabrix was identified as a Leader, Fast Mover, and Innovator that provides a complete AIOps solution, with a unique Robotic Data Automation Fabric and Distributed AIOps model, including for AIOps at the edge.

According to the report, "This year proved to be one of explosive growth in AIOps tooling and solutions. In some cases, AIOps functionality was achieved by bolting an artificial intelligence and machine learning (AI/ML) engine to existing software, via acquisition or internal development, and marketing it as an AIOps solution. Other vendors built entire platforms around homegrown or acquired AI/ML, jumping into a crowded arena competing with pure AI/ML solutions and platform tools." CloudFabrix's platform is homegrown, built with microservices, is cloud native and can run entirely in the cloud, in a hybrid deployment, or on-premises.

This year's report points out one key differentiation, among the 24 surveyed vendors dividing them into domain-agnostic and platform solutions and what it means for end users. The domain-agnostic solutions can be added to any environment with minimal interruption to the business, while platforms may require the displacement of several existing monitoring solutions.

CloudFabrix scored high ranks across the 3 categories, as identified by the report -

The report identifies CloudFabrix's key capabilities -

Supporting Quotes

"CloudFabrix continues to impress us with its innovation and its ability. They have shown a pulse on the AIOps market and a quest to constantly improvise in the areas where they were at a disadvantage, across the 2 years we have evaluated them. We are hopeful they continue on this path as Digital Transformation and AIOps are becoming mainstream for enterprises," said Ron Williams, Principal Analyst, Gigaom.

Shailesh Manjrekar, Vice President of AI and Marketing, CloudFabrix said, "We are delighted and honored to be recognized as a leader by Gigaom for 2 consecutive years. This endorses us as a "Fast Mover," demonstrated by our recent launch of "Persona-based Composable Analytics for AIOps." He further asserted, "Our success and scalability are demonstrated by our recent wins with large global MSPs and Enterprises. We continue to strive to delight our customers and make their autonomous enterprise journey, a reality by democratizing Data-First, AI-First, and Automation everywhere strategies."

Resources:

About CloudFabrix

CloudFabrix is the leading Data-centric AIOps Platform vendor and the inventor of Robotic Data Automation Fabric (RDAF). RDAF delivers integrated, enriched and actionable data pipelines to operational and analytical systems. RDAF unifies Observability, AIOps and Automation for Operational Systems and enriches analytical systems. CloudFabrix empowers Business and IT leaders with AI-powered actionable intelligence to make faster and better decisions and accelerate IT planning and Autonomous operations. For more information, visit cloudfabrix.com

Media Contact / Press Enquiry:Shailesh Manjrekar[emailprotected]408-421-4214

SOURCE CloudFabrix

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CloudFabrix Was Named a Leader and Innovator in the 2022 Gigaom Radar for Artificial Intelligence for Operations (AIOPs) - PR Newswire