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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

Machine learning startup Tecton has entered into a strategic partnership with Google Cloud to make its Tecton Feature Platform available to Google Cloud users. The platform automates the process of collecting, preparing, managing, and updating high-quality data required for training machine learning models. It ensures that the models have access to real-time predictive and generative AI applications. Tectons partnership with Google Cloud will help solution providers speed up the development of machine learning models while keeping costs under control.

Tecton was founded in 2019 by the developers behind Ubers Michelangelo machine learning platform. The company has raised $160 million through multiple funding rounds. Its platform is used for various applications, such as pricing, customer scoring, recommendation engines, automated loan processing, and fraud detection systems. These applications involve making complex decisions at scale and with high reliability. Tectons platform automates the process of creating machine learning features that power these models.

Google Cloud offers its Vertex AI system for training and deploying machine learning models and customizing large language models. Its data processing infrastructure services like DataProc and BigQuery are also commonly used in machine learning projects. The Tecton platform serves as a connective fabric, integrating these systems to build production-ready ML features. It automates the entire ML feature lifecycle, from definition and data transformation to online serving and operational monitoring.

Using the Tecton platform helps developers build better machine learning models by leveraging high-quality data. By automating data transformation and management, ML systems can be deployed into production faster. The platform also provides enterprise management and collaboration features that are often missing in ML initiatives.

Solution providers and strategic service providers performing AI and machine learning development work can use the Tecton-Google Cloud combination to work more efficiently. This partnership offers advanced machine learning feature engineering capabilities and accelerates the building of machine learning applications. It provides solution providers with another option to help their customers succeed in their ML initiatives.

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Tecton Partners with Google Cloud to Accelerate Machine Learning ... - Fagen wasanni

ReportLinker

Global Machine Learning Chips Market 2023-2027. The machine learning chips market is forecasted to grow by USD 22276.52 million during 2022-2027, accelerating at a CAGR of 30.91% during the forecast period.

New York, July 24, 2023 (GLOBE NEWSWIRE) -- Reportlinker.com announces the release of the report "Global Machine Learning Chips Market 2023-2027" - https://www.reportlinker.com/p06478500/?utm_source=GNW The report on the machine learning chips market provides a holistic analysis, market size and forecast, trends, growth drivers, and challenges, as well as vendor analysis covering around 25 vendors.The report offers an up-to-date analysis regarding the current market scenario, the latest trends and drivers, and the overall market environment. The market is driven by the increasing adoption of machine learning chips in data centers, growing investment in smart cities, and the development and integration of machine learning chips in autonomous vehicles.

The machine learning chips market is segmented as below:By End-user BFSI IT and telecom Media and advertising Others

By Technology System-on-chip (SoC) System-in-package Multi-chip module Others

By Geographical Landscape North America Europe APAC South America Middle East and Africa

This study identifies the increasing investments in semiconductors as one of the prime reasons driving the machine learning chips market growth during the next few years. Also, increasing investments in ai start-ups and growing adoption of socs in robotics will lead to sizable demand in the market.The report on the machine learning chips market covers the following areas: Machine learning chips market sizing Machine learning chips market forecast Machine learning chips market industry analysis

The robust vendor analysis is designed to help clients improve their market position, and in line with this, this report provides a detailed analysis of several leading machine learning chips market vendors that include Advanced Micro Devices Inc., Alphabet Inc., Baidu Inc., Broadcom Inc., Cerebras Systems Inc., Fujitsu Ltd., Graphcore Ltd., Huawei Technologies Co. Ltd., Intel Corp., International Business Machines Corp., MediaTek Inc., Microchip Technology Inc., NVIDIA Corp., NXP Semiconductors NV, Qualcomm Inc., SambaNova Systems Inc., Samsung Electronics Co. Ltd., SenseTime Group Inc., Taiwan Semiconductor Manufacturing Co. Ltd., and Tesla Inc.. Also, the machine learning chips market analysis report includes information on upcoming trends and challenges that will influence market growth. This is to help companies strategize and leverage all forthcoming growth opportunities.The study was conducted using an objective combination of primary and secondary information including inputs from key participants in the industry. The report contains a comprehensive market and vendor landscape in addition to an analysis of the key vendors.The publisher presents a detailed picture of the market by the way of study, synthesis, and summation of data from multiple sources by an analysis of key parameters such as profit, pricing, competition, and promotions. It presents various market facets by identifying the key industry influencers. The data presented is comprehensive, reliable, and a result of extensive research - both primary and secondary. The market research reports provide a complete competitive landscape and an in-depth vendor selection methodology and analysis using qualitative and quantitative research to forecast the accurate market growth.Read the full report: https://www.reportlinker.com/p06478500/?utm_source=GNW

About ReportlinkerReportLinker is an award-winning market research solution. Reportlinker finds and organizes the latest industry data so you get all the market research you need - instantly, in one place.

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The Global Machine Learning Chips Market is forecasted to grow by USD 22276.52 million during 2022-2027, accelerating at a CAGR of 30.91% during the...

NEW YORK LSU Online & Continuing Education and national tech education provider Fullstack Academy have announced the launch of an Artificial Intelligence and Machine Learning Bootcamp program.

The curriculum, designed and delivered by industry-experienced tech practitioners, is designed to provide the skills and hands-on training needed to build specialized data career paths in AI and machine learning in 26 weeks.

Demand for AI and machine learning professionals is projected to increase by nearly 36% over the next decade, according to the U.S. Bureau of Labor Statistics, far surpassing the average growth rate of roughly 6% for all occupations. Notably, this AI boom also has the potential to contribute a staggering $15.7 trillion to the global economy by 2035, according to PwC.

The rapid, widespread adoption and influence of AI and machine learning technologies are revolutionizing the way we work, live, and interact with technology every day. This unfolding potential across various industries has prompted companies and organizations worldwide to intensify their investments, including efforts to expand talent pools rather than reducing them, said Nelis Parts, CEO of Fullstack Academy. This new program with LSU Online & Continuing Education will enable professionals from all skill levels and interests to embark on a rewarding career path and contribute to an ever-evolving sector.

Graduates of the LSU AI & Machine Learning Bootcamp can qualify for entry-level positions across the country, where the U.S. median salaries for Data Analyst, Artificial Intelligence Engineer, and Machine Learning Engineer roles range from $71,034 to $151,063 (ZipRecruiter). Many positions are available with prominent companies, including Cox Communications, United Rentals, Inc., Veusol Technologies Inc., and the Internal Revenue Service of Louisiana.

The LSU AI & Machine Learning Bootcamp powered by Fullstack Academy will teach students practical and theoretical machine learning with hands-on, application-based training using real-world tools. Designed for both beginners and experienced tech professionals, students of the 26-week, part-time program will learn practical skills used by AI professionals in the fieldincluding Applied Data Science with Python, Machine Learning, Deep Learning, and Deep Neural Networksand their applications within Artificial Intelligence technology.

We are thrilled to add to our successful portfolio of program offerings in partnership with Fullstack Academy. The LSU AI & Machine Learning Bootcamp presents a comprehensive curriculum encompassing the entire spectrum of the field, from foundational principles to advanced concepts, said Kappie Mumphrey, vice president of LSU Online & Continuing Education. By equipping students with knowledge and skills in AI, we empower them to become the next generation of AI experts and problem solvers. Emphasizing the importance of AI education not only cultivates a skilled workforce but also ensures that our future leaders are equipped to navigate the opportunities and ethical considerations of an AI-driven world.

Applications are now open for the live online LSU AI & Machine Learning Bootcamp. The deadline to apply is July 25, 2023, for the programs inaugural cohort commencing July 31, 2023.

The LSU AI & Machine Learning Bootcamp does not require university enrollment. Scholarships are available to current LSU students and alumni, as well as active-duty service members and veterans. Interested learners can see these details and more on the LSU AI & Machine Learning Bootcamp website.

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LSU Partners to Launch AI and Machine Learning Program - Biz New Orleans

Superconductors, known for their ability to exhibit zero electrical resistance when cooled below a critical temperature, have tremendous potential for applications in energy, transportation, and cutting-edge electronics. Researchers from Georgia Tech and Hanoi University of Science and Technology have taken the first step towards incorporating atomic-level information into machine learning pathways to discover new conventional superconductors.

To overcome the barrier of lacking atomic level information, the researchers curated a dataset of 584 atomic structures with over 1100 computed values of and log at different pressures. Machine learning models were developed for and log and used to screen over 80,000 entries in the Materials Project database. Through first-principles computations, the researchers identified two materials that may exhibit superconductivity at a critical temperature of approximately 10^-15K and ambient pressure.

The researchers used the machine learning models to predict superconducting properties for 35 candidates, with six of them having the highest predicted critical temperatures. Further stabilization calculations were required for some candidates. After verifying the stability of two remaining candidates, CrH and CrH2, the researchers calculated their superconducting properties using first-principles calculations. The accuracy of the predictions was validated within 2-3% of the reported values through additional calculations using the local-density approximation (LDA) XC functional.

Additionally, the researchers investigated the synthesizability of the superconductors by tracing their origin in the Inorganic Crystalline Structure Database (ICSD). They found that these materials had been experimentally synthesized in the past, providing hope for future tests to confirm their predicted superconductivity.

In future research, the researchers plan to enhance their machine learning approach by expanding and diversifying the dataset, employing deep learning techniques, and integrating an inverse design strategy for more efficient exploration of materials. They also aim to collaborate with experimental experts for real-world testing and synthesis of high critical temperature superconductors.

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Machine Learning Used to Discover New Superconductors - Fagen wasanni