Vocal Biomarker Manufacturers to Rely on Artificial Intelligence and Machine Learning: Fact.MR

Growing mortality rates arising due to neurological and cardiovascular diseases, such as Parkinson’s disease and pulmonary hypertension, is prompting governments to fund research & development programs to mitigate this global burden

Fact.MR, Rockville MD: Award winning market research company Fact.MR has conducted a study on the global vocal biomarker market. As per the analysis, recent breakthroughs in the healthcare sector has fostered the sales for vocal biomarkers. Long-term expansion prospects are expected to remain positive, experiencing high growth rate until 2027.

According to the U.S. National Library of Medicine, there was a 39% of increase in the death rate due to the neurological disorder. Neurological disorders were the leading cause of DALYs (Disability Adjusted Life Years) and second leading cause of deaths. Hence, accelerating the production for vocal biomarkers.

According to the study, researchers are focusing on voice-based biomarkers. Start-ups are using Artificial Intelligence (AI) and machine learning (ML) to develop enhanced tools to detect a wide range of diseases. For instance, Vocalis Health, a start-up is working with developers to identify voice-based biomarkers for pulmonary hypertension.

“Manufacturers are accelerating their research and development capacities for more enhanced technology to detect neurological diseases like Alzheimer’s and Parkinson’s. Healthcare professionals and manufacturing companies are using the AI and ML tools to propel the production for vocal biomarkers,” says the Fact.MR analyst.

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Key Takeaways

Global vocal biomarker to exhibit a spectacular growth at nearly 23.3% CAGR through 2021
Contract research organizations (CROs) to emerge as highly sought after vocal biomarker drugs availability channel
North America to retain dominance for vocal biomarker, attributed to advanced technology used by manufacturing companies
Psychiatric disorders to emerge as a lucrative tool
U.S. to emerge as highly lucrative market, fueled by tie-ups of US government agencies with voice-based companies

Prominent Drivers

Increasing cardiovascular and psychological diseases to foster the sales
Rising demand for MRI, CT, and X-Ray to propel the growth of vocal biomarker
Emerging applications in healthcare and life sciences to deploy voice assistant platforms in clinical trial settings, penetrating the vocal biomarker market

Key Restraints

Low stability over long period for patients with chronic neurological diseases such as Alzheimer’s to hamper sales
Expensive techniques used in vocal biomarker’s application resulting in healthcare burden on economy, thus limiting penetration

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Competitive Landscape

Prominent vocal biomarker solutions providers include Beyond Verbal Communication Ltd., Sonde Health Inc., IBM Corporation, Cogito Corporation, and US Cargo Control among others. Variety of expansion strategies and advanced development are being deployed by the aforementioned market players.

In October 2020, Mayo Clinic collaborated with Vocalis Health for clinical development of vocal biomarkers. The AI-based company announced a collaboration to research and develop new voice voice-based tools for screening, detecting and monitoring patient’s health.

More Insights on the Vocal biomarker Market

In its latest report, Fact.MR offers unbiased analysis of the global vocal biomarker market. In order to understand the global market potential, its growth, and scope, the market is segmented on the basis of technique (frequency, amplitude, error rate, vocal rise or fall time, phonation time, voice tremor, pitch and other types), end-user (hospitals & clinics, CROs, academic & research center, and other end-users), indication (psychiatric disorders, respiratory disorders, cardiovascular disorders, traumatic brain injury, neurological disorders and others), and region (North America, Latin America, Europe, Japan, APEJ, and MEA).

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Published at Tue, 16 Mar 2021 06:22:30 +0000

Machine Learning Adoption will Influence These Five Industries

Some industries will have to do significant machine learning adoption.

Gradually recovering from the effects of COVID-19 pandemic, will be a top priority for practically every firm and industry in 2021. A few organizations may get stale or never recuperate. Others will see the purge as a remarkable opportunity to comprehend and improve their data and analytical assets, operationalize and update their model production process, and promise clients that their machine learning adoption can be trusted. Everybody is hoping to improve over their present AI and ML insights, for example, a bank improving fraud detection, a medical care provider moving to telehealth, a retailer or manufacturer attempting to make your supply chain more proficient.

All through the recent years, there have been a couple of revelations in machine learning and artificial intelligence. Several companies have so far been able to apply those to achieve the fundamental business targets.

With the rising demand of ML and interest in these advances, different ML trends in 2021 are climbing. Basically, in case you’re a tech able or related to innovation in some capacity, it’s overwhelming to see what’s next inside in ML for business. 2021 will see more machine learning adoption in industries that are fundamental to the functioning of society as a whole.

Banking

As the world starts its recuperation from the pandemic in 2021, sensational swings will happen across the macro-economy. A significant topic will be the impacts of fiscal stimulus and the reverberations that will be felt by families and bigger organizations. Banks and other financial institutions will be searching for both generous opportunities and huge threats, and the persistent suppression of interest rates will be a significant challenge as compressed spreads will burden profitability.

Utilizing obsolete models of machine learning will make banks quickly lose profit, market share of the overall industry and, now and again, reputation. Hence, the skill to quickly update models in sectors, for example, fraud, underwriting, customer management, etc., will be crucial.

Healthcare services

The worldwide pandemic has underscored the significance of investing in and streamlining our healthcare systems. ML for business is viewed as the most encouraging technology that permits healthcare suppliers to beat the enormous volumes of data and infer important clinical insights. ML and AI offer remarkable advancement in drug discovery, chopping down the long discovery and development pipeline and lessening cost. It can likewise altogether improve healthcare delivery systems and thus lift the overall quality of medical care while controlling cost. One of the ML trends in 2021 is that it can be used in clinical trials also. Machine learning will immensely affect almost all parts of medical care including pharma and biotech, experts underline.

Retail

The retail business is in crisis, yet there is a lot of strength and opportunity, and the retail landscape will keep on seeing sensational trends in consumer behavior. A few unsure variables will keep on challenging the business in 2021: jobs, the economy, and the logistics of facilitating pandemic restrictions in individual districts. Retailers will be compelled to do machine learning adoption for their business decisions, particularly to comprehend the always changing, underlying data. MLOps will be a key ML trend in 2021 in retail to operationalize the model update process, identify changes in economic and consumer data, and comprehend the significance of those changes.

Manufacturing

With the monstrous adoption of IoT devices set to additionally grow in the manufacturing industry, machine learning will be the most crucial technology that analyzes the enormous volumes of data produced. ML for business fills in as the incredible building block of Industry 4.0 alongside automation and data connectivity. While predictive maintenance is the most common use case up until this point, manufacturers will see more developed use cases of ML like supply chain visibility, cost reduction, real-time error detection, warehousing efficiency, and asset tracking among others. As traditional manufacturing plants shift to smart factories, ML will fuel more noteworthy advancement and productivity in the days to come.

Transportation

If you think self-driving vehicles are the results of a distant future, smart cars have effectively penetrated into the markets. Back in 2015, the execution of AI-driven systems in cars and vehicles were simply 8%, yet by 2025, the rates are expected to leap to 109%. Connected vehicles are the in-thing in the automobile business at the present time, where predictive mechanisms precisely tell drivers the likely breaking down of spare parts, routes and driving directions, emergency crisis and disaster prevention protocols and much more. Gartner anticipated that connected cars with embedded wireless connectivity and networks would be the benchmarks for vehicles by 2021. This is likewise gradually transforming into a reality with the prototypes of autonomous cars hitting the streets.

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Published at Tue, 16 Mar 2021 06:22:30 +0000

Vocal Biomarker Manufacturers to Rely on Artificial Intelligence and Machine Learning: Fact.MR