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September 29, 2020 -Across the country today, it is widely acknowledged that access to mental healthcare is just as important as clinical care when it comes to overall wellness.

Mental health conditions are incredibly common in the US, impacting tens of millions of people each year, according to the National Institutes of Mental Health (NIMH). However, estimates suggest that only half of people with these conditions receive treatment, mainly due to barriers like clinician shortages, fragmented care, and societal stigma.

For the many individuals suffering from anxiety and depression, these existing barriers coupled with the current healthcare crisis can significantly interfere with the ability to carry out life activities.

The prevalence of mental health disorders particularly depression and anxiety is high. If anything, the prevalence of these conditions has only increased as a result of COVID-19. The need is greater than ever now, Jun Ma,PhD, Beth and GeorgeVitouxProfessor ofMedicineat the University of Illinois Chicago (UIC) department of medicine, told HealthITAnalytics.

To broaden mental healthcare access for people with moderate depression or anxiety, UIC researchers are testing an artificial intelligence-powered virtual agent called Lumen. The team will train the tool to provide patients with problem-solving therapy, a structured approach designed to help people focus on learning cognitive and behavioral skills.

READ MORE: Machine Learning May Support Personalized Mental Health Therapies

The two-phase, five-year project is funded by a $2 million grant from NIMH.

The goal is to meet the many challenges of people who dont have ready access to proven psychotherapy, which has been a longstanding issue, said Ma.

Over the years, my research team has done clinical trials testing the effectiveness and dissemination of different behavioral and psychosocial interventions. The results of that work, combined with the gaps that exist in practice and patient access, have really catalyzed the idea for this project.

Using the same technology as Amazons Alexa, researchers will develop an app that will act as a virtual mental health agent, talking through steps and strategies with patients following a validated treatment protocol.

If we prove this way of delivering problem-solving treatment is a safe and effective, once we put it into production anyone with access to Alexa would be able to access the program. We're very early in the development phase, so it will probably be another few years before its widely available, said Ma.

READ MORE: US Patients See Rising Burdens of Mental Health, Chronic Disease

We're making good strides. Were starting to conduct a user study on a small scale. And the immediate next step after this initial user development and the user testing phase will be a small scale randomized controlled trial (RCT), in which we'll enroll patients with depressive symptoms and/or anxiety.

Individuals will complete eight one-on-one counseling sessions over 12 weeks. In each session, participants will identify a problem they view as affecting their life and as a source of emotional distress, and the counselor will help them define goals and possible solutions. Solutions are then compared, and counselors and patients work to make an action plan to implement the chosen solution.

Researchers will program Lumen using the Alexa Skills Kit to act as the virtual counselor working with participants, taking them through problem-solving steps and encouraging them to engage in meaningful and enjoyable activities to improve their emotional well-being.

During the first phase, 80 study participants who report elevated depressive and anxiety symptoms will test the Lumen tool, with the potential for wider use going forward.

The researchers hope that the project will increase access to mental healthcare for those who need it most.

READ MORE: Applying Artificial Intelligence to Chronic Disease Management

One of the main advantages of using AI as a platform to provide therapy is the ability to scale and reduce significant barriers to access, as well as sustainability of proven psychotherapy such as problem-solving treatment, said Ma.

The technology can also be quite adaptable to individuals depending on when they need it and how they want to access it, and can potentially reduce barriers due to stigma.

Despite the serious potential for these tools to broaden the availability of mental healthcare, Ma also noted that the use of AI in this area comes with several concerns just as the technology does in any part of healthcare delivery.

Like any novel treatment in early development, it's unknown at this point what the effectiveness and the sustained impact of AI in psychotherapy. It's certainly very worth exploring, as we are doing now, she said.

Patient privacy is a very important area that warrants not only additional research, but also additional legislation and regulation. Additionally, AI and the underlying algorithms are trained using existing data and information, and there could be unintended consequences due to implicit or explicit bias. Its very important to have transparency in how the models are trained, as well as to ensure the data used to train such models is representative of the population.

Mas statements align with those of other industry experts, who consistently highlight the necessity of safety, data privacy, and health equity when building and using these tools.

In a recent viewpoint published in JAMA, authors noted that chatbots and other AI-powered virtual agents are still relatively new, and much of the data available comes from research rather than widespread clinical implementation. For these reasons, healthcare leaders must continually evaluate the capacity of these tools to improve care delivery, the authors stated.

In the development stage of the Lumen tool, Mas team at UIC plans to do just that.

If the small-scale RCT proves promising, then we'll go on to a larger-scale RCT in which we'll recruit 200 patients, again with that depressive symptoms and/or anxiety, to further test the potential impact and effectiveness of Lumen, Ma said.

Ultimately, the success of these tools in healthcare will depend on the industrys ability to weigh possible risks and rewards.

Given the potential concerns, it's worth emphasizing the importance of balancing excitement for such novel treatments with caution. It's a fine line between ensuring protection of patient privacy and confidentiality and not restricting the innovation in this area, Ma concluded.

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Expanding Access to Mental Healthcare with Artificial Intelligence - HealthITAnalytics.com

When matrix signs start blinking above a motorway, the average commuter already knows what time it is. Traffic jams. Although the adjusted speed limit does help to improve traffic flow. There is one more indirect effect of this dynamic traffic flow management system: Fewer emissions due to improved traffic flow. That is why a trial is starting in Germany where environmental data will be incorporated into traffic flow management.

The air quality in the vicinity of motorways could also be considerably improved in the Netherlands. This is why the government wants to tackle this problem through the National Air Quality Cooperation Program (NSL). For example, by promoting electric vehicles or offering alternative means of transport. Air quality values around motorways can be found via this link.

According to German scientists, the incorporation of environmental data into traffic flow management can reduce noise and pollution. They are now going to research this in the U-SARAH live project, coordinated by the Karlsruhe Institute of Technology (KIT). The Ministry of Infrastructure and Water Management of the Netherlands is funding the project to the tune of almost 1.1 million.

The aim of this study is to optimize and implement an environmental control system in an existing traffic route control system so as to reduce the environmental impact on the sections in question, explains Professor Peter Vortisch, head of the KIT Institute for Transport.

A microscopic traffic flow model developed over the course of a preliminary study with our partner Hessen Mobil enables the effects of the newly developed environmental control system to be simulated. This makes it possible to optimize the control system in such a way that both traffic flow and environmental effects are taken into account.

We want to test and evaluate the new control system under real conditions in a practical test, says Matthias Glatz. He is a project manager at Hessen Mobil. EDI GmbH, a spin-off of KIT, uses the extensive traffic data to model the road users reactions to dynamic speed limits by using artificial intelligence (AI). On the basis of this data, we plan to develop an AI-based acceptance model and a prediction model as modules for guiding the SBA, says Dr. Thomas Freudenmann. He is one of the founders and managing director of EDI GmbH. The existing control system will be expanded with these modules.

The simulation model developed in U-SARAH live can be used in future both for quality management and the optimization of route control systems. The results of the project will benefit not only the population, public authorities, and scientific institutes but also all manufacturers of traffic control systems. Thanks to the AI-based approach, the traffic situation can be estimated a few minutes in advance so that traffic can be controlled even better. The simulation-based development facilitates the easy integration of emissions data into traffic control systems. And without incurring high acquisition costs for measuring technology, explains Sebastian Buck of the KIT Institute for Transport.

By reducing emissions and optimizing the flow of traffic, the economic damage caused by traffic congestion and excess emissions can be reduced. An analysis platform developed within the project will help to examine the large data files from different angles across all steps. The platform will be made available to the public via the Ministry of Transports data cloud.

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Cleaner air on motorways thanks to matrix signs with artificial intelligence - Innovation Origins

New York, Sept. 30, 2020 (GLOBE NEWSWIRE) -- Reportlinker.com announces the release of the report "North America Artificial Intelligence in Healthcare Diagnosis Market Forecast to 2027 - COVID-19 Impact and Regional Analysis by Diagnostic Tool ; Application ; End User ; Service ; and Country" - https://www.reportlinker.com/p05974389/?utm_source=GNW

The healthcare industry has always been a leader in innovation.The constant mutating of diseases and viruses makes it difficult to stay ahead of the curve.

However, with the help of artificial intelligence and machine learning algorithms, it continues to advance, creating new treatments and helping people live longer and healthier.A study published by The Lancet Digital Health compared the performance of deep learning a form of artificial intelligence (AI) in detecting diseases from medical imaging versus that of healthcare professionals, using a sample of studies carried out between 2012 and 2019.

The study found that, in the past few years, AI has become more precise in identifying disease diagnosis in these images and has become a more feasible source of diagnostic information.With advancements in AI, deep learning may become even more efficient in identifying diagnosis in the coming years.

Moreover, it can help doctors with diagnoses and notify when patients are weakening so that the medical intervention can occur sooner before the patient needs hospitalization. It can save costs for both the hospitals and patients. Additionally, the precision of machine learning can detect diseases such as cancer quickly, thus saving lives.In 2019, the medical imaging toolsegment accounted for a larger share of the North America artificial intelligence in healthcare diagnosis market. Its growth is attributed to an increasing adoption of AI technology for diagnosis of chronic conditions is likely to drive the growth of diagnostic tool segment in the North America artificial intelligence in healthcare diagnosis.In 2019, the radiology segment held a considerable share of the for North America artificial intelligence in healthcare diagnosis market, by the application. This segment is also predicted to dominate the market by2027 owing to rising demand for AI based application for radiology.A few major primary and secondary sources for the artificial intelligence in healthcare diagnosis market included US Food and Drug Administration, and World Health Organization.Read the full report: https://www.reportlinker.com/p05974389/?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 North America artificial intelligence in healthcare diagnosis market is projected to reach from US$ 1,716.42 million in 2019 to US$ 32,009.61...

With its data and innovation capabilities, India can become the Artificial Intelligence (AI) laboratory of the world, NITI Aayog CEO Amitabh Kant said on Saturday (3 October).

Speaking at a press conference to unveil the upcoming global virtual summit on AI -- 'RAISE 2020' -- Kant noted that AI can help transform lives.

"India is developing AI-based solutions for social empowerment across spheres like healthcare, education, finance, agriculture and governance. On the strength of its data and innovation prowess, India can become the AI laboratory of the world, delivering intuitive solutions to a wide range of societal issues," he said.

The virtual summit, RAISE 2020 (Responsible AI for Social Empowerment 2020), will be organised from 5-9 October by the Ministry of Electronics and IT and the NITI Aayog.

The aim of the summit is to kick-start discussion on the creation of robust AI-powered public infrastructure that benefits all, not just in India but across the world, an official statement said.

"India, one of the founding members of the Global Partnership on Artificial Intelligence, aims to implement AI-based solutions not just domestically, but in countries around the world so that these lead to widespread social empowerment and prosperity," it said.

Over the course of the five-day summit, several leading AI experts from across the world will deliberate over topics of vital importance such as strategies for using AI for social benefit, the importance of creating reliable AI infrastructure and the transformative effect of AI in empowering communities, it added.

Ajay Prakash Sawhney, Secretary, Ministry of Electronics and IT, said that India has the talent and the institutional capacity to build an AI-ready workforce that innovates and delivers solutions to solve societal issues.

"We are taking steps to promote the development and integration of AI into important spheres of life, in order to improve the ease of living and the overall quality of life. RAISE 2020 will serve as a starting point for India to become a data-driven society that leverages AI for social good," Sawhney said.

(This story has been published from a wire agency feed without modifications to the text. Only the headline has been changed.)

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With Its Data And Innovation Capabilities, India Can Become Artificial Intelligence Lab Of The World: NITI Aayog CEO - Swarajya

Americans encounter some form of artificial intelligence and machine learning technologies in nearly every aspect of daily life: We accept Netflixs recommendations on what movie we should stream next, enjoy Spotifys curated playlists and take a detour when Waze tells us we can shave eight minutes off of our commute.

And it turns out that were fairly comfortable with this new normal: A survey released last year by Innovative Technology Solutions found that, on a scale of 1 to 10, Americans give their GPS systems an 8.1 trust and satisfaction score, followed closely by a 7.5 for TV and movie streaming services.

But when it comes to higher stakes, were not so trusting. When asked about whether they trust an AI doctor diagnosing or treating a medical issue, respondents scored it just a 5.4.

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Overall skepticism about medical AI and ML is nothing new. In 2012, we were told that IBMs AI-powered Watson was being trained to recommend treatments for cancer patients. There were claims that the advanced technology could make medicine personalized and tailored to millions of people living with cancer. But in 2018, reports surfaced that indicated the research and technology had fallen short of expectations, leaving users to speculate the accuracy of Watsons predictive analytics.

RELATED:Investors poured $4B into healthcare AI startups in 2019

Patients have been reluctant to trust medical AI and ML out of fear that the technology would not offer a unique or personalized recommendation based on individual needs. A piece in Harvard Business Review in 2019 referenced a survey in which 200 business students were asked to take a free health assessment to perform a diagnosis40% of students signed up for the assessment when told their doctor would perform the diagnosis, while only 26% signed up when told a computer would perform the diagnosis.

These concerns are not without basis. Many of the AI and ML approaches that are being used in healthcare todaydue to simplicity and ease of implementationstrive for performance at the population-level by fitting to the characteristics most common among patients. They look to do well in the general case, failing to serve large groups of patients and individuals with unique health needs. However, this limitation of how AI and ML is being applied is not a limitation of the technology.

If anything, what makes AI and ML exceptionalif done rightis its ability to process huge sets of data comprising a diversity of patients, providers, diseases and outcomes and model the fine-grained trends that could potentially have a lasting impact on a patients diagnosis or treatment options. This ability to use data in the large for representative populations and to obtain inferences in the small for individual-level decision support is the promise of AI and ML. The whole process might sound impersonal or cookie-cutter, but the reality is that the advancements in precision medicine and delivery will make care decisions more data-driven and thus more exact.

Consider a patient choosing a specialist. Its anything but data-driven: Theyll search for a provider in-network or maybe one that is conveniently located, without understanding potential health outcomes as a result of their choice. The issue is that patients lack the proper data and information they need to make these informed choices.

RELATED:The unexpected ways AI is impacting the delivery of care, including for COVID-19

Thats where machine intelligence comes into playan AI/ML model that is able to accurately predict the right treatment, at the right time, by the right provider for a patient, which could drastically help reduce the rate of hospitalizations and emergency room visits.

As an example, research published last month in AJMC looked at claims data from 2 million Medicare beneficiaries between 2017 and 2019 to evaluate the utility of ML in the management of severe respiratory infections in community and post-acute settings. The researchers found that machine intelligence for precision navigation could be used to mitigate infection rates in the post-acute care setting.

Specifically, at-risk individuals who received care at skilled nursing facilities (SNFs) that the technology predicted would be the best choice for them had a relative reduction of 37% for emergent care and 36% for inpatient hospitalizations due to respiratory infections compared to those who received care at non-recommended SNFs.

This advanced technology has the ability to comb through and analyze an individuals treatment needs and medical history so that the most accurate recommendations can be made based on that individuals personalized needs and the doctors or facilities available to them. In turn, matching a patient to the optimal provider has the ability to drastically improve health outcomes while also lowering the cost of care.

We now have the technology where we can use machine intelligence to optimize some of the most important decisions in healthcare. The data show results we can trust.

Zeeshan Syed is the CEO and Zahoor Elahi is the COO of Health at Scale.

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Industry VoicesAI doesn't have to replace doctors to produce better health outcomes - FierceHealthcare