AI News, Technology assessment: Artificial Intelligence in the medical sector ... artificial intelligence

Artificial Intelligence in Healthcare: the future is amazing - Healthcare Weekly

The role of artificial intelligence in healthcare has been a huge talking point in recent months and there’s no sign of the adoption of this technology slowing down, well, ever really.

That being said, many healthcare executives are still too shy when it comes to experimenting with AI due to privacy concerns, data integrity concerns or the unfortunate presence of various organizational silos making data sharing next to impossible.

When researchers, doctors and scientists inject data into computers, the newly built algorithms can review, interpret and even suggest solutions to complex medical problems.

At the highest level, here are some of the current technological applications of AI in healthcare you should know about (some will be explored further in the article while some use cases have gotten their own standalone articles on HealthcareWeekly already).

Drug discovery: There are dozens of health and pharma companies currently leveraging Artificial Intelligence to help with drug discovery and improve the lengthy timelines and processes tied to discovering and taking drugs all the way to market.

As it turns out, by leveraging virtual reality combined with artificial intelligence, we can create simulated realities that can distract patients from the current source of their pain and even help with the opioid crisis.

Artificial intelligence in the medical field relies on the analysis and interpretation of huge amounts of data sets in order to help doctors make better decisions, manage patient data information effectively, create personalized medicine plans from complex data sets and discover new drugs.

AI in healthcare can prove useful within clinical decision support to help doctors make better decisions faster with pattern recognition of health complications that are registered far more accurately than by the human brain.

With patients getting to doctors faster, or not at all when telemedicine is employed, valuable time and money are saved, taking the strain off of healthcare professionals and increasing comfort of patients.

There are 4 main machine learning initiatives within the top 5 pharmaceutical and biotechnology companies ranging from mobile coaching solutions and telemedicine to drug discovery and acquisitions.

With startups combining the world of AI and healthcare, there’s more choice for older and larger companies to acquire information, systems and even the people responsible for leaps and bounds in technology.

In early-stage drug discovery, start-ups such as BenevolentAI or Verge Genomics are known to adopt algorithms which comb through portions of data for patterns too complex for humans to identify, saving both time and innovating in a way that we otherwise may not have been able to.

Growth opportunities may be hard to come by without significant investment from companies, but a major opportunity exists in the self-running engine for growth within the artificial intelligence sector of healthcare.

With AI in healthcare funding reading historic highs of $600m in equity funding (Q2’18) there are huge projected equity funding deals and equity deals as the years continue.

Bill Gates Saliently, AI represents a significant opportunity for bottom line growth with the introduction of AI into the healthcare sector, with a combined expected 2026 value of $150bn: The growth, however, is not unexpected and with the needs of the healthcare industry of which AI fits the gap –

With the predicted 2026 value of robot-assisted surgery, virtual nursing assistants and administrative workflow assistance are expected  to be valued at $40bn, $20bn and $18bn respectively, it’s the numbers that come with claims that are the most impressive.

That said, there continues to be significant pushback when it comes to AI adoption in the clinical decision support process as scientists and medical personnel continue to approach the topic of AI with incredible caution.

With minimal operator training needed and design with common output formats that directly interface with other medical software and health record systems, the system is incredibly easy to use and simple to implement.

clear output from the system allows 60 seconds to identify whether the exam quality was of sufficient quality, the patient is negative for referable DR or the patient has signs of referable DR. Following signs of referable DR, further action in the form of a human grader over-reading, teleconsultation and/or referral to an ophthalmologist may be suggested.

And in all these examples, artificial intelligence is leveraged, ‘under the hood’, to collect, analyze and interpret massive amounts of data which can improve the quality of life of patients everywhere.

Integration into the health industry is simple and won’t require significant IT time and with additional hardware not required, it’s a simple resource that can be set up and maintained remotely.

The technology uses AI to assess breast density in order to identify patients that may experience reduced sensitivity to digital mammography due to dense breast tissue.

Ken Ferry, CEO of iCAD stated that “With iReveal, radiologists may be better able to identify women with dense breasts who experience decreased sensitivity to cancer detection with mammography.” Mr. also Ferry added that “The increasing support for the reporting of breast density across the US, there is a significant opportunity to drive adoption of iReveal by existing users of the PowerLook AMP platform and with new customers, which represents an incremental $100 million market opportunity over the next few years.

Longer-term, we plan to integrate the iReveal technology into our Tomosynthesis CAD product, which is the next large growth opportunity for our Cancer Detection business.” Ultimately, the system remains at the forefront of breast cancer identification in women in the U.S. and with so many lives expected to be saved, I think everyone can agree what a fantastic use of AI it is.

Using MR image data, QuantX uses a deep database of known outcomes and combines this with advanced machine learning and quantitative image analysis for real-time analytics during scans.

  Coronary calcium scoring is a biomarker of coronary artery disease and quantification of this coronary calcification is a very strong predictor for cardiovascular events, including heart attacks or strokes.

With EF noted as the single most widely used metric of cardiac function, used as the basis for numerous clinical decisions, Bay Labs’ AI based EchoMD and AutoEF algorithms work to reduce the errors and minimise workflow that surrounds the industry.

The algorithms automatically review all relevant information and digital clips from a patient’s echocardiography study and proceeds to rate accordingly with image quality as the focus criteria.

Neural Analytics, a medical device company tackling brain health, announced a device for paramedic stroke diagnosis back in 2017, revolutionising the way that paramedics diagnose stroke victims.

The system developed objectively quantifies brain white matter abnormalities in patients, decreasing the amount of time taken, increasing the accuracy and improving patient care for those with brain issues.

With a 24/7 synchronized team collaboration, a suite of AI powered products detects and alerts stroke teams when large vessel occlusions are suspected, vital with such time-sensitive issues.

Medical imaging company Arterys has been demonstrating its wide range of AI powered imaging services and solutions for a number of years with liver and lung cancer MRI and CT diagnosis as well as MRI heart interpretation, covering huge areas of potential health scares.

The solution allows for tedious manual tasks to be avoided, effectively managing workflow and quickly and easily identifying and determining treatment for track heart problems.

Mayo Clinic, an organization focused on the development of patient care and health technology, has developed an artificial intelligence based solution to identify precancerous changes in a woman’s cervix.

The campus is a med-tech hub designated to advance new ideas and products from the research lab, through product development, for the improvement of human health and well-being which includes various Artificial Intelligence initiatives.

The management of medical knowledge is vital to the continual growth of the healthcare industry with new ways of training and developing doctors whereas patient uses predominantly cover healthbots and self-assist apps.

Knowledge management for doctors: Johnson and Johnson are one of the pioneers of a VR module to train doctors with VR based headsets used to allow hands-on practice for medical professionals, reducing real-life mistakes and surgery complications.

By focusing on three unique VR training modules for orthopaedic surgery, total hip, total knee replacement and hip fracture, the VR experience has led to praise throughout the medical community.

The software provides a safe and convenient learning experience where doctors are able to receive instant feedback and make better progress with their practice, but the possibilities are endless.

The downloadable app allows for instant results in the palm of you hand with a photo of a skin spot being all that is needed to receive your risk indicator before getting free advice from in-house dermatologists.

MarketWatch site logo .svg1{fill:#ffffff;} .svg2{fill:#00AC4E;}

Mar 19, 2019 (AB Digital via COMTEX) -- Global Artificial Intelligence (AI) Market is anticipated to reach USD 35,870.0 million by 2025.

Also, Artificial Intelligence finds its driving force in consumer services, big data, and growing demand for intelligent virtual supporters.

Artificial Intelligence Market is anticipated to grow at a significant CAGR of 57.2% in the upcoming period as the scope, product types, and its applications are increasing across the globe.

segment led the market in 2017 and is anticipated to maintain its dominance by 2025 owing to rising adoption of Artificial Intelligence in diagnostic care, patient care, and drug discovery.

segment led the Artificial Intelligence (AI) industry in 2017 and is anticipated to maintain its dominance by 2025 due to high growth rates include high implementation of Natural language processing (NPL) in numerous applications like AI robots, AI-enabled, smartphones, car-infotainment system.

The factors that could be attributed to the growth include growing acceptance of NLP technologies and deep learning in agriculture, finance, law, and marketing applications in APAC.

The leading companies are taking up partnerships, mergers and acquisitions, and joint ventures in order to boost the inorganic growth of the industry.

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Dr AI will see you now: setting a path for healthcare’s artificial intelligence revolution

It is not unreasonable to think that by 2050, the laboratory of the future will not be in a pharmaceutical lab but a patient’s own body, where AI highlights health risks, enabling humans to make better care decisions.

Whilst the opportunity to drastically improve healthcare is too compelling to ignore, it does raise a host of complex questions around how we should approach AI’s healthcare revolution — and more significantly, how quickly we can come to trust the guidance of a machine when it concerns the wellbeing of a human.

We deployed a digital healthcare system that nudged an adolescent allergist, through a mobile device, to pass on dietary information to others during meal times, while reminding them to scan food to assess the potential for allergic reaction.

This resulted in two behaviour changes being observed — the adolescent became more receptive to dietary advice, while caregivers (the adult) became less overprotective once they saw a change in the adolescent’s adherence behaviour.

One of AI’s biggest opportunities in medicine is ‘predictive care guidance’ which allows both patient and healthcare providers to leverage technology and make better decisions when diagnosing patients.

Healthcare is awash with data — the US system alone generates one trillion gigabytes of information every year — but approximately 80% remains unstructured and split between various silos, from clinical notes and laboratory results to medical research.

At QuantumBlack, we put human, machine and performance at the centre of all our projects by bringing together data science, engineering and design teams from the outset to combine with insight from healthcare sector specialists.

This fusion encourages us to continuously question and experiment with the bigger picture — not just how an algorithm will sit in a human’s life or in a wider business, but where it sits in the broader healthcare industry, how sector professionals can interpret the algorithm’s results and its impact on the ecosystem.

Time will only tell, but this big picture approach may result in technology being incorporated in a measured yet ambitious way that prioritises health adherence — to deliver better outcomes for everyone.

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