AI News, Artificial Intelligence in Healthcare: the future is amazing ... artificial intelligence

JMIR Publications

Although debate about the future of medicine persists, much of the discussion still focuses on recurrent themes such as how health care is paid for and organizational management.

In recent years, however, researchers working in the fields of artificial intelligence (AI) and biomedical informatics have begun to raise questions about the potential impact of technology on the medical workforce [1-3].

Although a minority of experts in these fields remain more skeptical that health care is on the cusp of a technological revolution [4], the overwhelming majority of informaticians predict that big data, machine learning, and innovations in AI are poised to significantly overhaul the delivery of medicine [5].

Many AI researchers suggest that future technology will augment current work practices and eliminate the need for many routine patient visits, but in complex clinical cases, physicians will still be needed to coordinate care and provide empathic support to patients [10-12].

Other AI experts hint at a stronger forecast, suggesting that, in the long term, primary care is vulnerable to disintermediation with physicians eventually being replaced by machine learning algorithms and paraprofessionals [3,8,13].

however, older GPs (aged 45 years and older) were more likely to leave comments than younger GPs: 83% (55/66) of older GPs left comments, compared with 48.0% (314/654) of older GPs who did not;

A series of Mann-Whitney U tests verified that those who provided qualitative feedback did not differ from those who did not provide qualitative feedback, on the perceived likelihood that future technology would replace human doctors for any of the 6 tasks: p s>.19.

views were identified in relation to primary care: (1) limitations of future technology, (2) potential benefits of future technology, and (3) social and ethical issues.

a process which, in turn, was deemed indispensable to diagnostics: Comments expressed varying degrees of cynicism about how technology might provide care that is respectful of, and responsive to, individual patient preferences, needs, and values.

Some individuals expressed this viewpoint forcefully: In this way, some GPs identified a positive paraprofessional role for technology in streamlining access to physicians: Supporting this perspective, some respondents were adamant that advancements in AI would buttress rather than replace the core roles of GPs and “help GPs with workload issues”

Interestingly, many participants chose not to interpret the question as directly asking about the impact of AI on the future of primary care, and instead, commented on the growing pressures on the GP workforce, including the risks that this was believed to pose to professionals and patients: Perhaps, consistent with the prevalent viewpoint that technology would play a limited role in primary care, some respondents stated that increased recruitment of physicians would relieve current demands on the GP workforce: The social implications for patients of possible advancements in AI received much less attention: comments instead were focused on whether the public would be satisfied with, or open to new technology or different ways of obtaining primary care: Some GPs conveyed greater certainty that AI would lead to major disruptions within general practice though they were unspecific about the nature of these disruptions;

Taking a different perspective, other comments predicted that radical change to primary care was imminent with some GPs claiming that embracing technological innovations is an ethical responsibility to reduce workloads and prevent patient harm.

Mobile health (mHealth) apps already allow patients to track and monitor a growing number of their own signs and symptoms (eg, blood glucose levels, blood pressure, and levels of physical activity) without the need for traditional checkups with their physician.

For example, recent research indicates that home monitoring may be preferable for controlling and preventing chronic conditions: evidence from systematic reviews and meta-analyses of patients with type 1 and type 2 diabetes suggests that mHealth provides clear improvement over clinical and nonmobile interventions in glycemic control and patient self-management [33,34].

In summary: in contrast with GPs, AI health researchers predict that wearable devices with the capacity for real-time monitoring will improve precision in information gathering while also driving down unnecessary appointments and health care costs [36].

Again, this perspective is diametrically opposed to the views of biomedical informaticians who argue that the accumulation of “big data”—the collection of vast amounts of information about individual patients (from the genomic and molecular levels, to information about diet, lifestyle, and other environmental factors)—when combined with machine learning, will yield more precise patterns about our individual health and medical outcomes and do so more quickly than humans are able to discern [5-7,9-11,35].

Indeed, aside from medical histories and patient reports, an exponentially increasing volume of health-related information generated from social media posts, apps, purchases, and credit card usage is already being used to support predictions about patient behavior and well-being [37].

In short, beyond the intentional use of digital health devices to undertake diagnostic and prognostic assessments, a vast range of nonmedical data are beginning to yield inferences about patient health, thereby challenging the traditional boundaries of medical expertise [35].

Many AI experts argue that humanistic care will be improved with developments in machine learning: they suggest that by outsourcing precision clinical decision making to machine learning algorithms, physicians will be set to invest greater time attending to the needs of their patients.

For example, some researchers working in the field of affective computing point to findings that computers can already outperform humans when it comes to accurate discernment of facial expressions [38] and judgments about personality [39].

Given the often short, yet diverse range of opinions articulated in this survey, and in light of omissions of key questions about the potential impact of technology on primary care and the professional roles of physicians, further qualitative work is warranted.

We recommend that additional qualitative research focus on the attitudes of physicians working in other medical specialties as well as the views of nurse practitioners and physician assistants about how AI may encroach on both the future of patient care and the medical workforce.

Relatedly, comments that greater investment in primary care physicians could address workloads are challenged by findings of the World Health Organization, which claims that there will be a worldwide shortage of 18 million health care workers by 2030, over twice the current shortfall [47].

Increasing numbers of patients suffering from chronic illness and aging populations have therefore led many commentators to suggest that new strategies will be required to cope with growing national as well as global health care needs [9,48].

Therefore, we conclude that our survey results raise important questions about the adequacy of medical curricula to equip future physicians for potential changes to clinical practice and, thereby, to lead and shape crucial debates about the future of patient care.

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.

Artificial intelligence in healthcare

Artificial intelligence (AI) in healthcare is the use of algorithms and software to approximate human cognition in the analysis of complex medical data.

What distinguishes AI technology from traditional technologies in health care is the ability to gain information, process it and give a well-defined output to the end-user.

AI algorithms behave differently from humans in two ways: (1) algorithms are literal: if you set a goal, the algorithm can’t adjust itself and only understand what is has been told explicitly, (2) and algorithms are black boxes;

AI programs have been developed and applied to practices such as diagnosis processes, treatment protocol development, drug development, personalized medicine, and patient monitoring and care.

Additionally, hospitals are looking to AI solutions to support operational initiatives that increase cost saving, improve patient satisfaction, and satisfy their staffing and workforce needs.[8]

Companies like Hospital IQ are developing predictive analytics solutions that help healthcare leaders improve business operations through increasing utilization, decreasing patient boarding, reducing length of stay and optimizing staffing levels.[9]

During this time, there was a recognition by researchers and developers that AI systems in healthcare must be designed to accommodate the absence of perfect data and build on the expertise of physicians.[14]

An ability to interpret imaging results may aid clinicians in detecting a minute change in an image that a clinician might accidentally miss.

The emergence of AI technology in radiology is perceived as a threat by some specialists, as the technology can perform certain tasks better than human specialists, changing the role radiologists have currently.[28][29]

Recent advances have suggested the use of AI to describe and evaluate the outcome of maxillo-facial surgery or the assessment of cleft patients therapy in regard to facial attractiveness or age appearance.[30][31]

The subsequent motive of large based health companies merging with other health companies, allow for greater health data accessibility.[33]

A second project with the NHS involves analysis of medical images collected from NHS patients to develop computer vision algorithms to detect cancerous tissues.[42]

Intel's venture capital arm Intel Capital recently invested in startup Lumiata which uses AI to identify at-risk patients and develop care options.[43]

As an autonomous, AI-based system, IDx-DR is unique in that it makes an assessment without the need for a clinician to also interpret the image or results, making it usable by health care providers who may not normally be involved in eye care.

team associated with the University of Arizona and backed by BPU Holdings began collaborating on a practical tool to monitor anxiety and delirium in hospital patients, particularly those with Dementia[53].

The AI utilized in the new technology – Senior’s Virtual Assistant – goes a step beyond and is programmed to simulate and understand human emotions (artificial emotional intelligence)[54].

Doctors working on the project have suggested that in addition to judging emotional states, the application can be used to provide companionship to patients in the form of small talk, soothing music, and even lighting adjustments to control anxiety.

Virtual nursing assistants are predicted to become more common and these will use AI to answer patient’s questions and help reduce unnecessary hospital visits.

Overall, as Quan-Haase (2018) says, technology “extends to the accomplishment of societal goals, including higher levels of security, better means of communication over time and space, improved health care, and increased autonomy” (p. 43).

While research on the use of AI in healthcare aims to validate its efficacy in improving patient outcomes before its broader adoption, its use may nonetheless introduce several new types of risk to patients and healthcare providers, such as algorithmic bias, Do not resuscitate implications, and other machine morality issues.

As of November 2018, eight use cases are being benchmarked, including assessing breast cancer risk from histopathological imagery, guiding anti-venom selection from snake images, and diagnosing skin lesions.[66][67]

Stanford unveils new AI institute, built to create ‘a better future for all humanity’

Amid a worldwide race for supremacy in artificial intelligence, Stanford University on Monday will  unveil a new institute dedicated to using AI to build the best-possible future.

The institute will take advantage of Stanford’s strength in a variety of disciplines, including AI, computer science, engineering, robotics, business, economics, genomics, law, literature, medicine, neuroscience and philosophy, according to promotional materials.

“Our goal is for Stanford HAI to become an interdisciplinary, global hub for AI thinkers, learners, researchers, developers, builders and users from academia, government and industry, as well as leaders and policymakers who want to understand and leverage AI’s impact and potential,”

and around potentially harmful results algorithms can produce when their input includes human bias that those feeding in the data may not even be aware of.

“Artificial Intelligence has the potential to help us realize our shared dream of a better future for all of humanity, but it will bring with it challenges and opportunities we can’t yet foresee,”

success, and a world-wide battle to acquire the best AI talent and develop the most lucrative and useful AI technologies pits the U.S. against powerhouse China, and even Canada, which has led the way on machine learning and invests heavily in AI research.

78 faculty members assigned to the institute reflect the diversity of fields the university intends to cover in its research and teaching, coming from disciplines including computer science, medicine, law, business, economics, environmental science, linguistics, political science and philosophy.

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