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Overview

The incorporation of Artificial Intelligence (AI) and Machine Learning (ML) in the healthtech industry has sparked an innovation in patient care, medical research, and healthcare efficiency. These pioneering technologies are strengthening the healthcare providers and researchers with several treatment options. However, as the healthtech segment continue to evolve, maintaining a balance between innovation and security is extremely important in order to protect sensitive patient data and ensure ethical AI practices. Here, we explore five leading brands that leverage Artificial Intelligence and Machine Learning to drive innovation in healthtech while prioritizing data privacy and security.

IBM Watson Health stands at the forefront of AI and ML-driven healthtech innovation. Their flagship project, Watson for Oncology, harnesses cognitive computing to analyze vast volumes of medical literature, clinical trials, and patient data to offer personalized treatment options for cancer patients. The system can suggest evidence-based treatment plans, helping oncologists make well-informed decisions. With a strong emphasis on data security and privacy, IBM Watson Health adheres to regulatory standards, ensuring the protection of patient data and compliance with HIPAA (Health Insurance Portability and Accountability Act) guidelines. The brands commitment to transparency in AI decision-making processes fosters trust among healthcare providers and patients alike.

NVIDIA Clara is a comprehensive AI platform designed explicitly for healthcare. Leveraging the power of NVIDIAs high-performance GPUs, Clara provides healthcare professionals with advanced imaging and visualization tools. These tools enable faster and more accurate medical imaging diagnosis, surgical planning, and drug discovery.

Recognizing the sensitivity of medical data, NVIDIA has implemented strong security measures within the Clara platform, ensuring data encryption, access control and audit trails. Additionally, the platform adheres to industry standards, such as DICOM (Digital Imaging and Communications in Medicine), to facilitate seamless integration with existing healthcare systems while safeguarding patient privacy.

Noventiq is a leading global provider of solutions and services in the realms of digital transformation and cybersecurity. Noventiqs expertise lies in facilitating and enabling digital transformation processes, empowering their customers to adapt to the evolving digital landscape. It provides cloud protection services and AI algorithms, ensuring that customer data and applications hosted in the cloud are secure and protected from unauthorized access to health related data to maintain patient privacy.

Siemens Healthineers combines AI and ML technologies to enhance medical imaging, diagnostics, and precision medicine. Their AI-Rad Companion platform assists radiologists by automating image analysis, facilitating faster diagnosis, and reducing the chance of human error.

Recognizing the importance of data security in the healthcare domain, Siemens Healthineers adheres to international data protection standards and implements state-of-the-art encryption protocols to protect patient data at all stages of processing and transmission. Their robust compliance measures assure both healthcare providers and patients that their data remains secure and private.

Cerner Corporation is a global leader in electronic health record (EHR) systems and clinical information solutions. Through their AI-enabled HealtheDataLab, they empower healthcare researchers with access to vast amounts of anonymized patient data for population health studies and medical research.

Cerner Corporation places utmost importance on data privacy and compliance with healthcare regulations, ensuring that all data is de-identified and anonymized before use in research. Their commitment to patient data security has gained the trust of healthcare institutions worldwide, enabling valuable AI-driven insights without compromising patient privacy.

AI and Machine Learning have undoubtedly ushered in a new era of innovation in healthtech, promising improved patient care, faster diagnoses, and groundbreaking medical research. The five brands mentioned above illustrate the balance between innovation and security, setting the gold standard for responsible AI deployment in healthcare. As technology continues to advance, these brands serve as beacons, guiding the healthtech industry toward a future that respects patient privacy, complies with regulations, and harnesses the full potential of AI to revolutionize healthcare for the better.

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Advancing Patient Care: 5 Brands Harnessing AI and Machine ... - Microbioz India

The new application, TargetSelector, streamlines target-protein discovery and enables researchers in various industries to identify novel targets for innovative products

REHOVOT, Israel, July 25, 2023 /PRNewswire/ --Evogene Ltd. (Nasdaq: EVGN) (TASE: EVGN), a leading computational biology company targeting to revolutionize life-science product discovery and development across multiple market segments, is proud to announce the latest addition to its ChemPass AI tech-engine a breakthrough technology for target-protein discovery. The integration of TargetSelector, a new application that streamlines target-protein discovery for active molecule identification, assists researchers in finding suitable target proteins for new products while reducing development time, resources and most importantly, increasing the probability of success.

Proteins play a fundamental role in a wide array of biological processes and serve as the primary targets for developing innovative therapeutics, ag-chemical, ag-biological, and other life science solutions. The precise identification of these protein targets is pivotal in advancing research and discovery across various domains, including pharmaceuticals, agriculture, and environmental applications.

The challenge of finding a target-protein that is novel, safe, and druggable from the thousands of proteins in a relevant organism is enormous. Leveraging predictive machine learning algorithms and genomic data, users gain valuable insights into product requirements such as homology, druggability, essentiality, and biological pathways, efficiently narrowing down the list of potential target-protein, thus optimizing the discovery process.

"ChemPass AI tech-engine is a cutting-edge platform for the identification of small molecules. The addition of the TargetSelector application now enables a broader scope of finding the optimal target-protein for these molecules," said Dr. Nir Arbel, CPO at Evogene. "Our subsidiary AgPlenus, which focuses on developing ag chemicals, will be the first to benefit from this new improvement, applying it to identify novel mechanismsof action for pesticides. I believe that this significant advancement in Evogene's ChemPass AI tech-engine, positions us to forge strategic partnerships with industry leaders, unlocking innovation, expediting product development, and delivering groundbreaking solutions that tackle pressing global challenges."

About ChemPass AI:

ChemPass AI tech engine is a cutting-edge computational platform for discovering and optimizing small molecules for various life-science products, such as therapeutics and ag-chemicals. Developed at the intersection of docking techniques and machine learning, ChemPass AI brings together the power of artificial intelligence, predictive biology, and molecular interactions to accelerate target-protein and active molecule discovery processes like never before.

ChemPass AIhas been trained on vast repositories of molecular data encompassing diverse chemical structures and biological targets. This wealth of knowledge empowers the platform to recognize intricate patterns, subtle interactions, and complex relationships between small molecules and their target-proteins. As a result, ChemPass AI can rapidly evaluate an organism's protein set (proteome) as well as billions of potential candidates, ranking them according to their likelihood of success and shortening the time needed to identify promising target-proteins and leads (small molecules).

About Evogene:

Evogene Ltd. (Nasdaq: EVGN) (TASE: EVGN) is a computational biology company leveraging big data and artificial intelligence,aiming to revolutionize the development of life-science based products by utilizing cutting-edge technologies to increase the probability of success while reducing development time and cost.

Evogene established three unique tech-engines - MicroBoostAI,ChemPass AIandGeneRator AI. Each tech-engineis focused on the discovery and development of products based on one of the following core components: microbes (MicroBoost AI), small molecules (ChemPass AI), and genetic elements (GeneRator AI).

Evogene uses its tech-engines to develop products through strategic partnerships and collaborations, and its five subsidiaries including:

For more information, please visit: http://www.evogene.com.

Forward-Looking Statements: This press release contains "forward-looking statements" relating to future events. These statements may be identified by words such as "may", "could", "expects", "hopes" "intends", "anticipates", "plans", "believes", "scheduled", "estimates", "demonstrates" or words of similar meaning. For example, Evogene and its subsidiaries are using forward-looking statement in this press release when it discusses TargetSelector's ability to assist researchers in finding suitable target proteins for new products while reducing development time, resources and increasing the probability of success, TargetSelector's ability to enable a broader scope of finding the optimal protein target for hit small molecules, AgPlenus' success in identifying novel mechanism of action pesticides, and ChemPass AI's ability to accelerate drug discovery processes by reducing the time and resources required. Such statements are based on current expectations, estimates, projections and assumptions, describe opinions about future events, involve certain risks and uncertainties which are difficult to predict and are not guarantees of future performance. Therefore, actual future results, performance or achievements of Evogene and its subsidiaries may differ materially from what is expressed or implied by such forward-looking statements due to a variety of factors, many of which are beyond the control of Evogene and its subsidiaries, including, without limitation, those risk factors contained in Evogene's reports filed with the applicable securities authority. In addition, Evogene and its subsidiaries rely, and expect to continue to rely, on third parties to conduct certain activities, such as their field-trials and pre-clinical studies, and if these third parties do not successfully carry out their contractual duties, comply with regulatory requirements or meet expected deadlines, Evogene and its subsidiaries may experience significant delays in the conduct of their activities. Evogene and its subsidiaries disclaim any obligation or commitment to update these forward-looking statements to reflect future events or developments or changes in expectations, estimates, projections, and assumptions.

Logo - https://mma.prnewswire.com/media/1947468/Evogene_Logo.jpg

Contact: Rachel Pomerantz Gerber Head of Investor Relations at Evogene [emailprotected] +972-8-9311901

SOURCE Evogene

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Evogene's ChemPass AI Tech-Engine is Introduced with New ... - PR Newswire

Exploring the Future of Semiconductor Testing: A Comprehensive Analysis of Machine Learning Applications

The future of semiconductor testing is poised for a significant transformation, thanks to the advent of machine learning applications. As the semiconductor industry continues to evolve, the need for more efficient and accurate testing methods has become increasingly apparent. Machine learning, a subset of artificial intelligence, is emerging as a promising solution to meet these demands.

Semiconductor testing is a critical process in the manufacturing cycle, ensuring the functionality and reliability of semiconductor devices. However, traditional testing methods are time-consuming, costly, and often unable to detect subtle defects that could lead to device failure. Machine learning, with its ability to learn from data and make predictions, offers a new approach to semiconductor testing that could overcome these challenges.

Machine learning algorithms can be trained to recognize patterns in data, enabling them to predict outcomes with high accuracy. In the context of semiconductor testing, these algorithms could be used to analyze data from the manufacturing process and predict potential defects in the devices. This predictive capability could significantly reduce the time and cost associated with testing, as well as improve the overall quality of the devices.

Moreover, machine learning can also be used to optimize the testing process itself. By analyzing data from previous tests, machine learning algorithms can identify the most effective testing strategies and adapt them to new devices. This adaptive testing approach could further enhance the efficiency and accuracy of semiconductor testing.

The application of machine learning in semiconductor testing is not without its challenges. One of the main hurdles is the need for large amounts of high-quality data to train the machine learning algorithms. This data is often difficult to obtain due to the proprietary nature of semiconductor manufacturing processes. However, collaborations between semiconductor manufacturers and machine learning researchers are starting to address this issue, paving the way for more widespread adoption of machine learning in semiconductor testing.

Another challenge is the complexity of the machine learning algorithms themselves. These algorithms require significant computational resources and expertise to develop and implement, which may be beyond the capabilities of many semiconductor manufacturers. However, advances in cloud computing and the development of user-friendly machine learning platforms are making these technologies more accessible.

Despite these challenges, the potential benefits of machine learning in semiconductor testing are too significant to ignore. The ability to predict defects and optimize testing strategies could revolutionize the semiconductor industry, leading to more reliable devices and lower manufacturing costs. Furthermore, the use of machine learning in semiconductor testing could also have broader implications for the electronics industry, potentially leading to more efficient production processes and higher-quality electronic devices.

In conclusion, the future of semiconductor testing is likely to be shaped by the application of machine learning. While there are challenges to overcome, the potential benefits of this technology are substantial. As the semiconductor industry continues to evolve, the adoption of machine learning in semiconductor testing could play a crucial role in driving this evolution, leading to significant improvements in device quality and manufacturing efficiency.

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The Future of Semiconductor Testing: A Deep Dive into Machine ... - Fagen wasanni

In this episode:

There are many methods for better night-vision, but often these rely on enhancing light, which may not be present, or using devices which can interfere with one another. One alternative solution is to use heat, but such infrared sensors struggle to distinguish between different objects. To overcome this, researchers have now combined such sensors with machine learning algorithms to make a system that grants day-like night-vision. They hope it will be useful in technologies such as self-driving cars.

Research article: Bao et al.

News and Views: Heat-assisted imaging enables day-like visibility at night

Benjamin Franklins anti-counterfeiting money printing techniques, and how much snow is on top of Mount Everest really?

Research Highlight: Ben Franklin: founding father of anti-counterfeiting techniques

Research Highlight: How much snow is on Mount Everest? Scientists climbed it to find out

We discuss some highlights from the Nature Briefing. This time, the cost to scientists of English not being their native language, and the mysterious link between COVID-19 and type 1 diabetes.

Nature News: The true cost of sciences language barrier for non-native English speakers

Nature News: As COVID-19 cases rose, so did diabetes no one knows why

Subscribe to Nature Briefing, an unmissable daily round-up of science news, opinion and analysis free in your inbox every weekday.

Never miss an episode. Subscribe to the Nature Podcast on Apple Podcasts, Google Podcasts, Spotify or your favourite podcast app. An RSS feed for the Nature Podcast is available too.

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AI-enhanced night-vision lets users see in the dark - Nature.com

Last Updated on June 12, 2023

Are you eager to know more about the differences between machine learning and Deep learning? If so, then this article is for you. Well provide you with everything you need to know about the two types of AI models and the key differences that differentiate them.

In recent years, theres been a lot of buzz on the internet concerning machine learning and deep learning. However, its not common knowledge as to what these terms actually mean. This brings us to the question, what exactly are machine learning and deep learning?

Before we dive into that, its best to give you a broad overview of artificial intelligence (AI) since machine learning and deep learning are both subsets of artificial intelligence.

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In its simplest form, artificial intelligence utilizes computer science and data to solve problems in machines. It enables machines to act and think like humans. At the moment, artificial intelligence is yet to match human intelligence. But in the future, artificial intelligence may eventually match or even surpass human intelligence due to the exponential rate of its development.

Currently, when compared to humans, AI excels in certain areas. For example, AI can complete a select number of tasks much more efficiently than humans, excelling especially in repetitive tasks.A great example of a service powered by Machine Learning is OpenAIs ChatGPT.

However, despite AIs proficiency in this area, it is still limited in its ability to perform a great number of functions and often requires some sort of human input or moderation.This is where machine learning and deep learning come into the picture. They can help AI refine their systems to become more discerning and more efficient at carrying out tasks.

Machine learning is a subset of artificial intelligence that focuses on computers that are able to learn from experience without being programmed. Machine learning artificial intelligence enables scientists to train machines on large amounts of data. The machine learning model is made to use an algorithm in analyzing and drawing inferences from the available data. And as the machine parses more data, the better it becomes at completing a task.

Machine learning is of 3 different types; supervised learning, unsupervised learning, andreinforcement learning.

Today, machine learning is used for a broad range of things, such as automated recommendations, malware threat detection, fraud detection, spam filtering, generalized trend-based predictions, and more.

Deep learning is a subset of machine learning that is modeled on the workings of the human brain. It can be considered to be an advanced version or evolution of machine learning. A deep learning model works similarly to human brains, in that it layers algorithms and computing units, also known as neurons, into a large web of interconnected systems. This web of data is known artificial neural network. These deep neural networkscontinually analyze datasets in a logical fashion to draw conclusions and predictions based on them.

A great example of deep learning artificial intelligence is Googles AlphaGo, which can beat professional human players at the board game Go, the oldest board game known to be continually played.

There are different types of deep learning algorithms. Some of which include convolutional neural networks (CNNs), recurrent neural networks (RNNs),generative adversarial networks(GANs), long short-term memory networks (LSTMs), multilayer perceptrons (MLPs), radial basis function networks (RBFNs), and more.

Deep learning is used for a broad range of things today, such as automated driving, the military, consumer electronics, speech recognition, image recognition, and more.

Lets take a look at some of the key differences between machine learning and deep learning.

Getting results from machine learning algorithms requires a fair amount of human intervention, more so than with a Deep Learning Model. On the other hand, the setup process for deep learning is vastly more complex. But after that, only very little human intervention is required.

Machine learning systems are very easy and fast to set up. However, the results they produce are often limited. While deep learning systems take a longer time to set up, their results are usually instantaneous.

Machine learning uses traditional algorithms and usually relies on structured data. Deep learning uses neural networks and is designed to accommodate huge amounts of unstructured data.

As we have seen, machine learning and deep learning are quite similar but also differ in many ways. As we have seen with technologies such as Siri and Alexa, these types of machine learning have the potential to make great leaps forward in the advancement of the tech we have today.For generations to come, machine learning deep learning will impact our lives in so many ways and will become an increasingly important part of almost every industry.

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Machine learning vs Deep learning in AI - what are the differences? - PC Guide - For The Latest PC Hardware & Tech News