healthcare technology

Google's AI-powered tool that will be available later this year helps anyone identify skin conditions using their phone’s camera.

Artificial intelligence (AI) has the potential to help clinicians care for patients and treat disease — from improving the screening process for breast cancer to helping detect tuberculosis more efficiently.

When we combine these advances in AI with other technologies, like smartphone cameras, we can unlock new ways for people to stay better informed about their health, too.  

Google's AI-powered dermatology assist tool is a web-based application that they hope to launch as a pilot later this year, to make it easier to figure out what might be going on with their skin.

Once the user launchs the tool, simply use their phone’s camera to take three images of the skin, hair or nail concern from different angles. They are  then  asked questions about their skin type, how long they’ve had the issue and other symptoms that help the tool narrow down the possibilities. The AI model analyzes this information and draws from its knowledge of 288 conditions to give the user a list of possible matching conditions that they can then research further.

For each matching condition, the tool will show dermatologist-reviewed information and answers to commonly asked questions, along with similar matching images from the web.

The tool is not intended to provide a diagnosis nor be a substitute for medical advice as many conditions require clinician review, in-person examination, or additional testing like a biopsy. Rather Google hopes it gives users access to authoritative information so they can make a more informed decision about their next step.

Developing an AI model that assesses issues for all skin types 

Google's tool is the culmination of over three years of machine learning research and product development. To date, Google has published several peer-reviewed papers that validate their AI model and they claim more are in the works. 

Recently, the AI model that powers the tool successfully passed clinical validation, and the tool has been CE marked as a Class I medical device in the EU.

more at https://blog.google/technology/health/ai-dermatology-preview-io-2021/

The Mayo Clinic has launched a new mHealth platform aimed at helping healthcare providers improve their use of connected health devices in remote patient monitoring and other mobile health programs.

The Remote Diagnostic and Management Platform (RDMP) connects devices to AI resources that would help providers with clinical decisions support and diagnoses in what the Minnesota-based health system calls “event-driven medicine.” It’s designed to help providers in and outside the health system analyze and act on data collected by mHealth devices.

“The dramatically increased use of remote patient telemetry devices coupled with the rapidly accelerating development of AI and machine learning algorithms has the potential to revolutionize diagnostic medicine,With RDMP, clinicians will have access to best-in-class algorithms and care protocols and will be able to serve more patients effectively in remote care settings. The platform will also enable patients to take more control of their health and make better decisions based on insights delivered directly to them.”

read more at https://mhealthintelligence.com/news/mayo-clinic-launches-new-platform-for-analyzing-data-from-mhealth-devices

Imagine you’re a scientist who needs to discover a new antibiotic to fight off a scary disease. How would you go about finding it?

Typically, you’d have to test lots and lots of different molecules in the lab until you find one that has the necessary bacteria-killing properties. You might find some contenders that are good at killing the bacteria only to realize that you can’t use them because they also prove toxic to humans. It’s a very long, very expensive, and probably very aggravating process.

But what if, instead, you could just type into your computer the properties you’re looking for and have your computer design the perfect molecule for you?

That’s the general approach IBM researchers are taking, using an AI system that can automatically generate the design of molecules for new antibiotics.

In a new paper, published in Nature Biomedical Engineering, the researchers detail how they’ve already used it to quickly design two new antimicrobial peptides — small molecules that can kill bacteria — that are effective against a bunch of different pathogens in mice.

Normally, this molecule discovery process would take scientists years. The AI system did it in a matter of days.

That’s great news, because we urgently need faster ways to create new antibiotics.

How IBM’s AI system works

IBM’s new AI system relies on something called a generative model. To understand it at its simplest level, we can break it down into three basic steps.

First, the researchers start with a massive database of known peptide molecules.

Then the AI pulls information from the database and analyzes the patterns to figure out the relationship between molecules and their properties. It might find that when a molecule has a certain structure or composition, it tends to perform a certain function.

This allows it to “learn” the basic rules of molecule design.

Finally, researchers can tell the AI exactly what properties they want a new molecule to have. They can also input constraints (for example: low toxicity, please!). Using this info on desirable and undesirable traits, the AI then designs new molecules that satisfy the parameters. The researchers can pick the best one from among them and start testing on mice in a lab.

The IBM researchers claim that their approach outperformed other leading methods for designing new antimicrobial peptides by 10 percent. They found that they were able to design two new antimicrobial peptides that are highly potent against diverse pathogens, including multidrug-resistant K. pneumoniae, a bacterium known for causing infections in hospital patients. Happily, the peptides had low toxicity when tested in mice, an important signal about their safety (though not everything that’s true for mice ends up being generalizable to humans).

read the original unedited article at  https://www.vox.com/future-perfect/22360573/ai-ibm-design-new-antibiotics-covid-19-treatments

read the paper by the IBM researchers - Accelerated antimicrobial discovery via deep generative models and molecular dynamics simulations

Roughly 600,000 people in the U.S. are diagnosed with Parkinson’s every year, contributing to the more than 10 million people worldwide already living with the neurodegenerative disease. Early detection can result in significantly better treatment outcomes, but it’s notoriously difficult to test for Parkinson’s.

Tencent and health care firm Medopad have committed to trialing systems that tap artificial intelligence (AI) to improve diagnostic accuracy. They announced a collaboration with the Parkinson’s Center of Excellence at King’s College Hospital in London to develop software that can detect signs of Parkinson’s within minutes. (Currently, motor function assessments take about half an hour.)

This technology can help promote early diagnosis of Parkinson’s disease, screening, and daily evaluations of key functions.

Medopad’s tech, which uses a smartphone camera to monitor patients’ fine motor movements, is one of several apps and wearables the seven-year-old U.K. startup is actively developing.

It instructs patients to open and close a fist while it measures the amplitude and frequency of their finger movements, which the app converts into a graph for clinicians. The goal is to eventually, with the help of AI, teach the system to calculate a symptom severity score automatically.

Tencent and Medopad are far from the only firms applying AI to health care. Just last week, Google subsidiary DeepMind announced that it would use mammograms from Jikei University Hospital in Tokyo, Japan to refine its AI breast cancer detection algorithms. And last month Nvidia unveiled an AI system that generates synthetic scans of brain cancer

read the original story at https://venturebeat.com/2018/10/08/tencent-partners-with-medopad-to-improve-parkinsons-disease-treatment-with-ai/

As data and analytics are increasingly leveraged in various aspects of the healthcare system, some companies are  making use of such capabilities to help clinicians make the best decisions for patients.

One such company is naviHealth.  Based in Brentwood, Tennessee, naviHealth provides both payers and providers with post-acute care management expertise. Its nH Predict tool allows clinicians to better predict a patient’s outcomes in order to craft a personalized post-acute care plan.

Using NaviHealths nH Predict tool, clinicians are better able to predict a patient's outcomes and generate a personalized post-acute care plan.

The result of the tool is a simple outcome report that is generated at the beginning of the patient’s stay in a facility or hospital. The report breaks down the patient’s basic information as well as how they’re doing in a variety of categories.

For instance, nH Predict outlines the individual’s gender, date of birth and admission date. It also includes their primary diagnostic group (such as COPD) and their usual living setting (like at home alone or in an assisted living facility).

Finally, the outcome report provides a score for a few of the patient’s functions based on the data of similar patients. It gives a score on the patient’s basic mobility (such as wheelchair skills or ability to take the stairs); daily activity (like bathing and dressing); and applied cognition (including memory and communication).

Additionally, the report creates a total average score for the patient based on their mobility, activity and cognition scores.

read the complete story at https://medcitynews.com/2018/10/navihealth-data-patient-outcomes/

At the World Medical Innovation Forum this week, participants were polled with a loaded question:

“Do you think healthcare will become better or worse from the use of AI?”

Across the respondents, 98 percent said it would be either “Better” or “Much Better” and not a single one thought it would become “Much Worse.” This is an interesting statistic, and the results were not entirely surprising, especially given that artificial intelligence was the theme for the meeting.

This continual stream of adoption of new technologies in both clinical and post clinical settings is remarkable. Today, healthcare is a technology operation. As a case in point, outside of the array of MDs and medical professionals presenting at the forum, there was clearly a strong, advanced technology thread weaved throughout the conversations of the traditional topics of pathology, radiology, bioinformatics, electronic medical records (EMR), and standard healthcare provider issues.

As an example, a panel of senior technology experts from Microsoft, Cisco Systems, Dell EMC, Qualcomm, and Google joined research and information officers from Partners Healthcare and Massachusetts General Hospital to discuss the challenges in what they called “Data Engineering in Healthcare: Liberating Value.” That is a serious title for a panel.

Data portability was clearly a key topic, as was security and the public cloud.

The underlying issue with the cloud is that the EMR was never really designed to be portable.

Health records existed with institutional walls, and were not originally intended for real time care, but more as a means of tracking costs and transactions as the patient traveled through the various systems. As the EMR has not only become more feature rich, the ability to mine that data inside of them with ML and AI methods is clearly at the forefront of everyone’s mind right now.

There was discussion of episodic systems wrapped in policy and technology – this really isn’t quite how we can gain the maximum knowledge from the healthcare version of a Digital Me. A digital object containing all of our many and varied health related attributes. The challenges of discussing how to best build a “marketplace” and healthcare data exchanges and how to integrate “data marts” with existing EMR systems was obvious.

Scientists have fabricated a device that can mimic human brain cognitive actions and is more efficient than conventional techniques in emulating artificial intelligence, thus enhancing the computational speed and power consumption efficiency.

Artificial intelligence is now a part of our daily lives, starting from email filters and smart replies in communication to helping battle the Covid-19 pandemic. But AI can do much more such as facilitate self-driving autonomous vehicles, augmented reality for healthcare, drug discovery, big data handling, real-time pattern/image recognition, solving real-world problems, and so on.

These can be realised with the help of a neuromorphic device which can mimic the human brain synapse to bring about brain-inspired efficient computing ability. The human brain comprises of nearly a hundred billion neurons consisting of axons and dendrites. These neurons massively interconnect with each other via axons and dendrites, forming colossal junctions called synapse.

This complex bio-neural network is believed to give rise to superior cognitive abilities.

Software-based artificial neural networks (ANN) can be seen defeating humans in games or helping handle the Covid-19 situation. However, the power-hungry (in megawatts) von Neumann computer architecture slows down ANNs performance due to the available serial processing while the brain does the job via parallel processing consuming just 20 W. It is estimated that the brain consumes 20% of the total body energy. From the calory conversion, it amounts to 20 watts. While the conventional computing platforms consume megawatts, i.e., 1 million watts of energy, to mimic basic human cognition.

To overcome this bottleneck, a hardware-based solution involves an artificial synaptic device that, unlike transistors, could emulate the functions of human brain synapse. Scientists had long been trying to develop a synaptic device that can mimic complex psychological behaviors without the aid of external supporting (CMOS) circuits.

To address this challenge, Scientists from Jawaharlal Nehru Centre for Advanced Scientific Research, Bengaluru, an autonomous institute of the Department of Science & Technology, Government of India, devised a novel approach of fabricating an artificial synaptic network (ASN) resembling the biological neural network via a simple self-forming method (the device structure is formed by itself while heating). This work has been recently published in the journal ‘Materials Horizons’.

“Nature has had an incredible amount of time and diversity to engineer ever new forms and functions through evolution. Learning and emulating new processes, technologies, materials and devices from the nature and biology are the important pathways to the significant advances of the future which will increasingly integrate the worlds of the living with the man-made technologies,” said Prof Ashutosh Sharma, Secretary, DST.

read the original unedited post at https://www.indianext.co.in/2021/06/scientists-develop-efficient-artificial-synaptic-network-that-mimics-human-brain/

Since the start of the pandemic, new technologies have been developed to help reduce the spread of the infection.

Some of the most common safety measures today include measuring a person’s temperature, covering your nose and mouth with a mask, contact tracing, disinfection, and social distancing. Many businesses have adopted various technologies, including those with artificial intelligence (AI) underneath, helping to adhere to the COVID-19 safety measures.

As an example, numerous airlines, hotels, subways, shopping malls, and other institutions are already using thermal cameras to measure an individual’s temperature before people are allowed entry. In its turn, public transport in France relies on AI-based surveillance cameras to monitor whether or not people are social-distancing or wearing masks. Another example is requiring the download of contact-tracing apps delivered by governments across the globe.

However, there are a number of issues.

While many of these solutions help to ensure that COVID-19 prevention practices are observed, many of them have flaws or limits. In this article, we will cover some of the issues creating obstacles for fighting the pandemic.

Issue #1. Manual temperature scanning is tricky

Issue #2. Monitoring crowds is even more complex

Issue #3. Contact tracing leads to privacy concerns

Issue #4. UV rays harm eyes and skin

Issue #5. UVC robots are extremely expensive

Issue #6. No integration, no compliance, no transparency

Regardless of the safety measures in place and existing issues, innovations are already playing a vital role in the fight against COVID-19. By improving on existing technology, we can make everyone safer as we all adjust to the new normal.

read the details at https://www.altoros.com/blog/whats-wrong-with-ai-tools-and-devices-preventing-covid-19/

After 3 years as head of IBM’s health division, Deborah DiSanzo is leaving her role.

A company spokesman said that DiSanzo will no longer lead IBM Watson Health, the Cambridge-based division that has pitched the company’s famed artificial intelligence capabilities as solutions for a myriad of health challenges, like treating cancer and analyzing medical images.

Even as it has heavily advertised the potential of Watson Health, IBM has not met lofty expectations in some areas. Its flagship cancer software, which used artificial intelligence to recommend courses of treatment, has been ridiculed by some doctors inside and outside of the company. 

And it has struggled to integrate different technologies from other businesses it has acquired, laying off employees in the process.

more at https://www.statnews.com/2018/10/19/head-of-ibm-watson-health-leaving-post/

Information technology has allowed much of our economy to automate processes. We have seen transformations of the airline, banking, brokerage, entertainment, lodging, music, printing, publishing, shipping and taxi industries through the availability of massive volumes of real-time price and service data. Across America, consumer-facing retail service continues to shift to a virtual environment.

Healthcare is the exception. Many health information technology (health IT) products initially focused on billing.

The misalignment between billing support and the sense that these tools do not materially automate clinician work to build in efficiencies or improve workflows adds to an overall frustration with the increasing amount of time providers spend at their screens.

Automation is hard because it tends to require interfaces of various types – both to other machines (Internet of Things) and to humans.

Often automation proposals involve solutions that focus on highly structured data. But, someone or something has to put energy (physician salary, for example) into organizing much of this information, assuming it is even knowable.

The underlying disease or patient behavior (e.g., smoking) is also often not knowable. And, automation relying on machine to machine interfaces regularly runs into a lack of application programming interfaces (APIs) supporting complex clinical data flows.

I posted this week old piece now and now when it was published as #NHITWeek is this week. A lot of posts this week deal with possibilities and problems with healthcare focussed automation. 

The original unedited piece can be read at https://www.himss.org/news/healthcare-automation-transforming-medicine

Deep mind will use data available to it via a new partnership with Jikei University Hospital in Japan to refine its artificially intelligent (AI) breast cancer detection algorithms.

Google AI subsidiary DeepMind has partnered with Jikei University Hospital in Japan to analyze mammagrophy scans from 30,000 women.

DeepMind is furthering its cancer research efforts with a newly announced partnership.

The London-based Google subsidiary said it has been given access to mammograms from roughly 30,000 women that were taken at Jikei University Hospital in Tokyo, Japan between 2007 and 2018.

Deep mind will use that data to refine its artificially intelligent (AI) breast cancer detection algorithms.

Over the course of the next five years, DeepMind researchers will review the 30,000 images, along with 3,500 images from magnetic resonance imaging (MRI) scans and historical mammograms provided by the U.K.’s Optimam (an image database of over 80,000 scans extracted from the NHS’ National Breast Screening System), to investigate whether its AI systems can accurately spot signs of cancerous tissue.