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10 top artificial intelligence (AI) applications in healthcare

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Artificial intelligence (AI)  is being applied across the healthcare spectrum — from administration to patient interaction and medical research, diagnosis and treatment. 

What is healthcare AI?

Healthcare AI is the application of artificial intelligence to medical services and the administration or delivery of medical services. Machine learning (ML), large and often unstructured datasets, advanced sensors, natural language processing (NLP) and robotics are all being used in a growing number of healthcare sectors. 

Along with great promise, the technology offers significant potential concerns — including the abuse that can come from the centralization and digitalization of patient data as well as  possible linkages with nanomedicine or universal biometric IDs. Equity and bias have both also been concerns in some early AI applications, but the technology may also be able to improve healthcare equity.

Although deployment of AI in the healthcare sector has truly just begun, it is becoming more commonly used. Gartner pegged 2021 global healthcare IT spending at $140 billion, with enterprises listing AI and robotic process automation (RPA) as their lead spending priorities.

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Healthcare costs approached a fifth (19.7%) of the total U.S. economy in 2020 (an estimated 19.7% or $4.1 trillion). Over half of that spending, for the first time, was racked up by the government, where fraud is especially high

Thus, the potential value of healthcare AI, from administration to medical AI is vast.  

10 top applications of artificial intelligence in healthcare in 2022

Here are 10 of the top areas where healthcare AI use cases are being developed and deployed today. 

1. Healthcare administration

Administrative expenses are estimated to comprise 15% to 25% of total healthcare costs. Tools to improve and streamline administration are valuable for insurers, payers and providers alike. 

Identifying and cutting down fraud, however, may provide the most immediate return as ealthcare fraud can happen on many levels and be committed by various parties. In some of the worst cases, fraud may cause insurers to get billed for services not rendered or result in surgeons performing unnecessary operations to get higher insurance payments. Insurers may also get billed for defective devices or test kits. 

AI can be a useful tool in stopping fraud before it happens. Just as banks commonly use algorithms to detect unusual transactions, and health insurers can do the same..

2. Public health

AI is already being applied across the public health sector. Including

  • ML algorithms are being applied to large public health datasets, and the CDC has compiled some of the many ways AI has been applied in analyzing public health for COVID-19 and beyond. 
  • NLP is being applied in public health contexts.
  • Increasingly, diagnostic imaging data is being harnessed for population-level analysis and predictions.
  • Lirio applies consumer data science and behavioral “nudging” techniques to creating “precision”, or personalized, nudges to prompt healthcare visits, medical compliance and the like.

3. Medical research

The applications for AI in medical research are also expansive. Examples range from new and repurposed drug discovery to clinical trials, including:

  • Finding new drugs to treat conditions can be incredibly complicated . In silicon computer-aided drug design (CADD) is its own complex field
  • In some cases, the goal is to repurpose existing drugs. One recent example came when AI analyzed cell images to see which drugs were most effective for patients with neurodegenerative diseases. Neurons change shape when positively responding to these treatments. However, conventional computers are too slow to spot these differences.
  • Pharma provider Bayer believes AI could enhance clinical trials by creating a virtual control group using medical database information. They’re exploring other AI clinical trial applications, too, that could make these investigations safer and more effective.

4. Medical training

AI may also alter how medical school students receive parts of their education. Including in cases like the following:

  • One example gave students feedback from an AI tutor as they learned to remove brain tumors. The system had a machine learning algorithm that taught students safe, effective techniques, then critiqued their performance. People learned skills 2.6 times faster and performed 36% better than those not taught with AI.
  • Organizations in the U.S. and the U.K. have also deployed AI-based virtual patients to facilitate virtual and remote training. That approach was particularly useful when the COVID-19 pandemic halted group gatherings. The AI supported practicing several skills, likecomforting distressed patients or delivering bad news.

5. Medical professional support

 AI is also deployed to support medical professionals in clinical settings, including the following: 

  • AI is applied to support intake professionals in medical facilities. One Stanford University pilot project uses algorithms to determine if patients are high-risk enough to need ICU care or to experience code-related events or those that require rapid response teams. They assess the likelihood of those events occurring within a six to 18-hour window, helping physicians make more confident decisions.
  • AI-based applications are being developed to support nurses, with decision support, sensors to notify them of patient needs and robotic assistance in challenging or dangerous situations among the areas addressed.

6. Patient engagement

AI is also deployed to support patients directly:

  • Hospitals use AI chatbots to check in with patients and help them get necessary information faster. When Northwell Health implemented patient chats, there was a 94% engagement rate among those utilizing oncology services. Clinicians who tried the tool agreed it extended the care they delivered. Chatbots are able to check on patients’ symptoms, recoveries and more. Many people are also used to chatting by text, which increases adoption. Chatbots also reduce challenges patients may encounter while seeking care. People can use them to find hospitals or clinics, book appointments and describe needs.
  • Estimates suggest that as many as half of all patients don’t take medications as prescribed. However, AI can increase the chances of patients taking their medications as they should. Some platforms use smart algorithms to suggest when health professionals should engage with patients about compliance and through which channels. Medication reminder chatbots exist, too. In a recent example, researchers collaborated and used AI to assist with finding the best medications for people with Type 2 diabetes. The algorithms helped choose the right options for more than 83% of patients, even in cases where the people needed more than one medication simultaneously.

7. Remote medicine

Telemedicine in the form of virtual doctor visits have become increasingly common since the COVID-19 lockdowns. In addition to those, AI is supporting other forms of remote medicine as well, including:

  • VirtuSense applies predictive AI to remotely monitor and alert providers about high-risk changes that may precipitate a fall. 
  • Some facilities currently using AI for monitoring rely on it for conditions ranging from heart disease to diabetes. Hospitals also used this technology to oversee COVID-19 patients, making it easier to decide which could receive home care and which needed hospital treatment.

8. Diagnostics

AI is also utilized for healthcare center diagnostics, including by:

  • One AI system used to spot breast cancer can detect current issues and a patient’s likelihood of developing the disease in the next several years.
  • Some applications of AI in healthcare detect mental ailments, too. Researchers have used trained algorithms to identify depressed people by listening to their voices or scanning their social media feeds, for example.

9. Surgery

AI does not eliminate surgical issues, but it can potentially reduce them while enhancing outcomes for patients and surgeons alike. This is illustrated in the following examples

  • A startup called Theator recently raised $39.5 million in a series A funding round. The company has an AI video solution built to help surgeons see what went wrong and right during procedures. They can then study the footage to make improvements for the future.
  • Artificial intelligence applications in healthcare include surgical robots that are increasingly common in operating rooms. Many are minimally invasive and often achieve outcomes superior to non-robotic interventions. These uses of AI won’t replace humans’ surgical expertise. Though, they can work as surgeons’ partners, improving the likelihood of procedures succeeding.

10. Hospital care

Along with the above-described diagnostic use cases, clinicians also must meet patients physical needs and, more prosaically, stock supplies and deliver goods. AI-powered collaborative robots are starting to ease the burden. Gartner expects 50% of U.S. providers to invest in robotics process automation (RPA) by 2023. Some examples of RPA in hospitals include:

  • One hospital recently deployed five robots named Moxie. These machines will proactively determine when nurses need supplies or assistance with lab test logistics. They’ll then respond before the provider’s workload gets too intensive.

Atheon provides robots that support not only medical functions, but tasks such as linen distribution and waste removal.

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Intel’s confidential computing solution for protecting cloud data is tested in healthcare

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Ensuring the integrity of software isn’t easy. At one level or another, you have to place trust that a third party implements the necessary security controls to protect your data. Or do you?

Today, at Intel Innovation, Intel announced that health provider, Leidos, and professional services company, Accenture, are beginning to implement Project Amber, the organization’s verification service for cloud-to-edge and on-premises trust assurance. 

Project Amber provides enterprises with a solution to independently verify the trustworthiness of computing assets throughout their environment.

Essentially, it provides enterprises with a solution they can use to help verify the integrity of the software supply chain to ensure that they aren’t using any computing assets or services that leave data exposed.

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Restoring faith in the software supply chain

The release of Project Amber comes as more and more organizations are struggling to place trust in the security of third-party software vendors. Currently, only 37% of IT professionals feel very confident in the security of the supply chain. 

While there are many reasons for this lack in confidence, a spate of supply chain attacks, starting with the SolarWinds breach in 2020, have highlighted that organizations can face serious exposure to risk if third-party vendors fail to secure their environments against threat actors. 

One of the key technologies that has the potential to address supply chain security is confidential computing. Confidential computing has the potential to mitigate supply chain risks by encrypting data-in-use so that it’s not accessible to unauthorized third parties processing or transmitting the data. 

“With the introduction of Project Amber at Intel Vision in May ’22, Intel is taking confidential computing to the next level in our commitment to a zero-trust approach to attestation and the verification of computing assets at the network, edge and in the cloud,” said Intel senior vice president, chief technology officer, and general manager of the software and advanced technology group (SATG), Greg Lavender. 

Intel essentially combines zero-trust attestation with confidential computing to help enterprises verify the security of third-party cloud services and software.

How Leidos and Accenture are using Project Amber 

At this stage, Leidos has a new Project Amber proof of concept that offers the potential to support its QTC Mobile Medical Clinics, where vans perform in-field medical exams and health information processing for U.S. veterans in rural areas.

In this instance, Intel’s solution provides additional security protections for internet of things (IoT) and medical internet of things (MIoT) devices that sit beyond the network’s edge. 

In another part of healthcare, Accenture is integrating Project Amber into an artificial intelligence (AI)-based framework for protecting data. As part of this proof of concept, healthcare institutions can share data securely to build a central AI model trained to detect and prevent diseases.

With the AI models needing to be trained on data taken from multiple hospitals and then aggregated in a single location, Project Amber enables Accenture to run machine learning (ML) workloads across multiple cloud service providers within a secure trusted execution environment (TEE).

This TEE prevents sensitive information from exposure to unauthorized third parties and verifies the trustworthiness of computing assets including TEEs, devices, policies and roots of trust. 

An overview of confidential computing approaches 

Confidential computing services are picking up momentum due to their ability to prevent unauthorized users from viewing or interacting with the underlying code at rest and in use. According to Everest Group, the confidential computing market has the potential to grow to $54 billion by 2026, as organization’s need for data privacy grows. 

Of course, Intel isn’t the only provider experimenting with confidential computing. 

Fortanix helped to pioneer this technology and offers a Confidential Computing Manager that can run applications in TEEs, while offering other security controls such as identity verification, data access control and code attestation. Fortanix also announced raising $90 million in series C funding earlier this year. 

Other providers like Google Cloud are also experimenting with confidential computing to encrypt data-in-use for confidential VMs and confidential GKE nodes to bolster the security of a wider cloud environment. Earlier this year, Google Cloud surpassed $6 billion in revenue during the second quarter of 2022. 

However, what makes Intel’s approach unique is that most TEE’s are self-attested by individual cloud service providers and software vendors. In effect, a provider verifies that their own infrastructure is secure. This means enterprises have to trust that a vendor accurately verifies the security of their own systems. Instead, Intel acts as an impartial third party who can testify that another vendor’s or cloud service provider’s workload or TEE is secure for an organization to use. 

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AI

6 AI companies disrupting healthcare in 2022

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Investments in AI-enabled healthcare have exploded over the past few years. But even with belt-tightening in 2022, digital health startups using artificial intelligence (AI) have received a whopping $3 billion in funding. That has left plenty of room for startup AI companies to make their mark in healthtech, biotech and medtech. 

It’s clear that even as health systems struggle to develop the right infrastructure to support AI’s need for vast data lakes, as well as to access quality or siloed data, the industry remains bullish on artificial intelligence. A December 2021 survey from health insurer Optum, for example, found that almost half of healthcare executives use AI, while around 85% say they have an AI strategy.

These are six startups that have had a banner year disrupting a variety of healthcare areas, from drug discovery and operational efficiency to disease detection and cell biology research.

Atomwise: AI for drug discovery

In August, the San Francisco-based Atomwise, which develops AI systems for drug discovery, signed a research collaboration with pharmaceutical leader Sanofi, potentially worth $1.2 billion. According to a press release, the deal “incorporates deep learning for structure-based drug design, enabling the rapid, AI-powered search of Atomwise’s proprietary library of more than 3 trillion synthesizable compounds.” 

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Drug discovery depends on a first step of “hit identification,” where the right molecules – hits – that bind to a target protein and modify its function are identified. According to an August 2020 VentureBeat article, Atomwise claims its AtomNet platform can screen 16 billion chemical compounds for potential hits in under two days, expediting a process that would normally take months or years. 

In 2022, Atomwise also strengthened its management team and bulked up its executive team. It will need that strength in a competitive space that includes Verge Genomics, Certara, Insilico Medicine, Recursion and Benevolent AI.

ClosedLoop AI: Digging into patient data

It’s been a big year for Austin, Texas-based ClosedLoop AI, since raising $34 million in August 2021. The company, which provides a data science platform that enables healthcare organizations to use AI to improve outcomes and reduce costs, was selected to participate in the AWS Healthcare Accelerator for Health Equity, and it won a 2022 Best in KLAS Award for healthcare artificial intelligence. 

Founded in 2017, the ClosedLoop platform provides off-the-shelf AI models and automation workflows for healthcare applications and manual processes involving data science tasks, examining patient data on an individual level and analyzing data points. Healthcare provider organizations have used ClosedLoop to make decisions on medical interventions and preventative measures for issues such as chronic kidney disease or heart failure. 

Top ClosedLoop AI competitors include heavyweights such as DataRobot and Dataiku, as well as Abacus Insights and Jvion. 

Digital Diagnostics: Identifying eye disease

In 2018, Iowa-based Digital Diagnostics made headlines when it became the first autonomous AI system authorized by the U.S. Food and Drug Administration. 

But 2022 has been kind to the company, whose AI-diagnostic system, the IDx-DR, can be used to identify diabetic retinopathy –- one of the leading causes of blindness in the U.S. and other developed countries –- as well as other serious eye diseases, including macular edema. In August, Digital Diagnostics announced that it had raised $75 million, one of the largest healthcare tech funding rounds this year. 

“There’s a strong mission and purpose for us to get our technology to patients that really need to be tested, and certainly to providers that may be burnt out or are getting burnt out,” Seth Rainford, cofounder, president and COO of Digital Diagnostics, recently told VentureBeat.

Cleerly: AI for cardiac imaging

New York-based Cleerly has been on a mission to transform cardiac care since its founding in 2017. It has enjoyed a huge 2022, raising a fresh round of $192 million in July for its AI-based approach to translate advanced imaging science into a new approach for identifying people at risk of heart attacks. 

According to a company press release, the research that evolved into Cleerly’s technologies was conducted in The Dalio Institute for Cardiovascular Imaging at the New York-Presbyterian Hospital and Weill Cornell Medicine, including large-scale clinical trials with more than 50,000 patients. It comprised the most extensive body of coronary imaging research to study how imaging can be used to better understand heart disease and project patient outcomes. 

A February 2022 study published in the Journal of American College of Cardiology found Cleerly’s AI platform is “as good or better than invasive angiography” – helping to catch heart disease early, before patients begin to show symptoms. 

Owkin: Finding the right drug for every patient

Talk about a big 2022: The medical-AI unicorn Owkin secured $80 million in June from pharmaceutical leader Bristol Myers Squibb as the two companies partner on drug trials.

The French-American, New York-based startup, founded in 2016, has developed a federated learning–based technology to speed up drug discovery and development, drawing on health data that is typically siloed, such as from U.S. and European hospitals.

Last week, Owkin announced two first-ever AI diagnostics approved for use in Europe. The first can predict whether a breast cancer patient will relapse after treatment, while the other can identify a biomarker that opens up potentially life-saving treatment for colorectal cancer patients.

Deepcell: AI for cell biology research

Founded in 2017, Menlo Park, CA-based Deepcell, which was spun out of Stanford University, raised fresh funds in March to use artificial intelligence to find new ways to understand biology. 

Back in 2020, Deepcell cofounder and CEO Maddison Masaeli told VentureBeat that Deepcell’s AI-powered approach “is able to differentiate among cell types with greater accuracy than traditional cell isolation techniques that rely on antibody staining or similar methods.” 

And in a February 2022 interview, Masaeli explained that the Deepcell platform relies on deep neural nets as the “ultimate cell classifier,” so that the model “learns continuously from the images that are collected.” Currently, that includes around 1.5 billion images. 

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AI

Why healthcare industry leaders need to prioritize digital equity

Presented by Optum


Barriers like broadband access, digital literacy, language, disabilities and more shut many out of the healthcare system. Watch now to learn why it’s urgent for industry leaders to close the gaps, and come away with a plan to identify and eliminate the digital challenges your customers face.

Watch free on demand here.


Healthcare is facing a new frontier, says Tushar Mehrotra, senior vice president, analytics at Optum. In just a few years, the industry has seen a boom in digital health tools and technologies on both the patient and provider side, along with an explosion of health data, which has been driving increasingly sophisticated predictive and prescriptive insights into individuals and populations.

Unfortunately, this frontier has proven to be hostile to marginalized communities. There is a growing digital divide, where healthcare technology has actually posed challenges, instead of benefits. The barriers to accessing newly digitized care are legion: it’s everything from language barriers to low income, lack of broadband or mobile access, disabilities and physical differences, low digital literacy, a fully understandable mistrust of the healthcare system and much more. The danger is that this divide will continue to grow, and even become insuperable.

“As we continue to advance healthcare technology and drive innovation in the space, we’ll see tremendous benefits — but it has to be done in a way where we’re thoughtful about implications and consumption across communities,” Mehrotra says. “The challenge is to reach all consumers without exacerbating the disparities that exist in our communities today.”

In other words, putting what he calls techquity front and center. Mehrotra describes techquity as using advancements in healthcare technology to drive health equity in underserved, vulnerable and at-risk populations, and close the access gaps.

Healthcare industry leaders are responsible for driving the techquity movement – it’s not only an ethical consideration, but also offers a number of advantages for consumers and organizations alike.

The real-world benefits of techquity

On the consumer side, techquity can change – or save — a person’s life. It unlocks new ways to drive health outcomes, safety and healthcare decisions, and enables the right care at the right point in time, in a way that wasn’t possible in the past. Access to healthcare technology and knowledge creates transparency into the system, enabling more choices for consumers navigating treatment.

But there are tremendous benefits for organizations as well. Techquity opens up innovation for organizations, promoting new ways of thinking, new avenues of exploration, and possibilities. It builds valuable trust between an organization and a customer, and opens up access to new potential customers that have previously been unreachable, or even invisible, in the past.

“As leaders we need to help consumers understand why it’s essential for their healthcare outcomes to stay on the digital landscape, and help them get comfortable it,” he says. “If you want to reap the potential of healthcare technology, fundamentally change the industry, and drive adoption, it’s going to be important to be a trusted partner for consumers navigating this new world.”

Why techquity rests in the hands of the C-suite

Techquity starts at the top, Mehrotra says.

“It’s important for a healthtech leadership team or an organization to really understand that you can build and design tools and technologies that are relevant for anyone in the population,” Mehrotra says. “We have influence, if we set up our product teams and tech teams in a way that we haven’t maybe thought about in the past. That’s why it’s important to treat this as a C-suite-level topic.”

For organizations, it’s about fundamentally changing their approach to building technology, doing the right research and market testing, and incorporating that equitable approach into designing, building and launching products. If this is not a top-team agenda item, then it isn’t going to be funneling down to the technology or product or design teams.

“If leadership isn’t there, you’ll run into challenges in terms of making sure it disseminates and is incorporated into your organizational approach,” he says.

But the biggest challenge is finding ways to address the fear or concern of the highest risk consumers who are at risk of being separated even further from access to healthcare. Leadership must take point on this effort too.

“There has to be a willingness, a persistence, a focus, and a commitment of resources in an organization, one, to understand that this is important, and two, to understand the implications of it,” he says. “There has to be proactive outreach to those communities. Unless you have that outreach — the partnerships in the local community to drive education, drive understanding — you’re not going to get the change in behavior.”

To learn more about the dangers of healthcare inequity, why industry leaders should care, how your organization can address your customer’s digital divide, and more, don’t miss this VB On-Demand event.


Watch free on demand here!


Agenda

  • How to build a data-driven map that identifies the health literacy, digital access and social determinants that impact digital engagement and outcomes
  • How to align your efforts with the cultural, social and economic environments experienced by the people you serve
  • Ideas for addressing the root causes that create barriers to health— and where simple digital solutions can close gaps
  • How to offer simple choices to ensure a consumer’s digital experience is consistent across the health journey

Presenters

  • Duncan Greenberg, VP of Product, Oscar Health
  • Michael Thompson, VP, Chief of Staff, Systems Improvement, Bassett Healthcare Network
  • Tushar Mehrotra, SVP, Data & Analytics, OptumInsight
  • John Li, Senior Director, Clinical Analytics and Product Solutions, Optum

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AI

First FDA-cleared autonomous AI makes new moves in healthcare diagnostics

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In 2018, Iowa-based Digital Diagnostics made headlines when it became the first autonomous AI (artificial intelligence) system authorized by the U.S. Food and Drug Administration. It received FDA approval to use AI to autonomously detect diabetic retinopathy in adults with diabetes, without the need for input from a doctor. 

Its AI-diagnostic system, the IDx-DR, can be used to identify diabetic retinopathy – one of the leading causes of blindness in the U.S. and other developed countries – as well as other serious eye diseases, including macular edema. 

“There’s a strong mission and purpose for us to get our technology to patients that really need to be tested, and certainly to providers that may be burnt out or are getting burnt out,” Seth Rainford, cofounder, president and COO of Digital Diagnostics, told VentureBeat.

In an effort to help get its technology to market and fulfill its mission of “paving the way for AI diagnosis to become a standard-of-care, democratizing healthcare and closing care gaps,” Digital Diagnostics today announced that it has raised $75 million in a series B funding round. 

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The need for AI automation in eye care

The original idea for Digital Diagnostics came from the company’s executive chairman and cofounder, Dr. Michael Abramoff, Rainford explained.

Dr. Abramoff, retina specialist, also has advanced degrees in machine learning. While working as a practicing physician, he often had patients coming in who had been waiting for months to get a routine exam to see if they were losing their eyesight. It was obvious to Dr. Abramoff that there was a gap that needed to be filled in the market.

By combining machine learning and his expertise in eye health, the mission of Digital Diagnostics is to provide an automated approach to detecting health risks, initially in the human eye. The basic idea is that with an automated system, more patients can potentially get quicker access to the diagnostics tests and results needed to avoid blindness.

Rainford sees the Digital Diagnostic approach as being both more scalable than requiring only human doctors and it can also be more cost effective as well.

“If you think about what’s required today to deliver the same thing that we’re delivering through this technology, you begin to think of a person going through med school, then more time at a residency or fellowship and further training,” Rainford said. 

Even after a human completes all the training to become qualified, the doctor could choose to work in a city center rather than a rural or underserved area. Rainford said that the goal with Digital Diagnostics is to allow for easier access to eye care diagnosis.

“We’re enabling or unlocking access to high quality-specialist level of care, at least from a diagnosis standpoint, and then we’re able to triage those patients that actually are losing their sight to the specialists that have spent all those years going to school,” he said.

How autonomous AI powers Digital Diagnostics

The Digital Diagnostics technology is not intended to just assist a clinician, it is actually intended to make a diagnosis, instead of a clinician.

Rainford noted the company’s FDA-approved system uses what is known as a fundus camera, which is an image-acquisition camera specialized for the human eye. The images from the camera are analyzed by Digital Diagnostics client software and its embedded AI.

Rainford emphasized that the AI is not a continuous learning model, but rather is based on an FDA-approved model.

“Our AI was approved by the FDA and now that algorithm is fixed until we go back to the FDA with something different,” Rainford said. “So there is not a continuous learning behind the scenes, we know what the algorithm is doing and everything is traceable.”

Looking forward, Digital Diagnostics has its sights set on disease detection beyond just the human eye. In 2020, Digital Diagnostics acquired privately-held 3Derm systems, which has expertise in dermatology.

“We’re working on the very same kind of technology for things like skin cancer and other skin diseases that, of course, are outside of the eye but the same thesis can hold true where we’re trying to democratize access to specialist-quality testing,” Rainford said.

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Is your doctor providing the right treatment? This healthcare AI tool can help

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How does a medical professional stay aware of the right procedures and treatments for patient ailments in the modern world? While many often rely on experience, there is another way that could have life-saving consequences. The trick is, it relies heavily on the power of artificial intelligence (AI).

New York-based medical startup H1 released a new update to its HCP Universe platform today to inject a dose of healthcare AI into medical intelligence. The HCP Universe platform is currently used by medical affairs teams at life sciences companies, which make sure doctors are aware of and use the latest science and medicine. 

With HCP Universe, medical affairs teams can target the right doctors and educate them about the latest and most important medical treatments and which patients should receive that treatment.

“Our mission for this product is to make sure that the latest medicine is used on the right patients, so that patients get the right treatment,” Ariel Katz, H1 cofounder and CEO, told VentureBeat.

Using healthcare AI to improve adoption of new treatments

For H1, the use of healthcare AI is all about providing the intelligence to help medical affairs people find the right doctors – proactively. 

“What we have done in the past is provide a platform for users to go and search and find doctors for a specific field or treatment, but that’s not the right thing to do,” Katz explained. 

Instead, identifying and reaching the right doctors in a given field of medicine is key. Katz said that the updated HCP Universe platform has AI-powered features to highlight and help drive evidence-based medicine usage around the world.

The hardest part of making the data actionable for medical affairs, Katz explained, was relating the data together and putting it in the right taxonomies. For example, if a user searches for “obesity,” there are any number of medical people that could be involved, including endocrinologists, dieticians or even psychiatrists. 

“If a patient searches for obesity, they don’t just want to find a doctor that specializes in diets, they want to find one that is relatable to that person’s needs,” Katz said. “So it’s relating the data together and then the machine learning libraries learn from user behavior to drive relevance.”

Why a graph database wasn’t enough

The idea of connecting relationships together is a common concept in graph databases. In fact, the HCP Universe platform is built with a graph database. But on its own, Katz said that his company has discovered that this is not accurate enough when it comes to important healthcare treatment decisions. 

“If a recommendation engine is 80% accurate for a restaurant where you just want a bagel, you’re probably happy,” Katz said. “If it is 80% accurate and you’re trying to find a doctor and you should have been diagnosed with cancer, that’s not okay.”

With the machine learning libraries, H1 is able to learn from the data and can correlate complex relationships that the graph database doesn’t identify on its own. H1 uses AWS machine learning tools, including Sagemaker, to help power its healthcare AI efforts, Katz said.

When H1 launched, Katz noted that the biggest problem it had to solve was aggregating and collecting sources of information on medical professionals.

“We started by solving a data problem first and making sure all the information is accurate, trustworthy and reliable,” Katz said. “The next generation, which is what we’re launching, is how do you actually make it smart and turn that data into insights?”

Looking forward, H1 will be training its AI to provide clinical quality scores for medical professionals. For example, if a user wants to identify the best medical professional for treating bladder cancer, the system will help identify the best doctor based on multiple factors, including patient surveys and hospital readmission rates, among other pertinent factors.

“This information, on who is a better doctor and what’s a better hospital, will change the experience for many people who are engaged with the healthcare ecosystem,” Katz said. 

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Tech News

WHO report on AI in healthcare is a mixed bag of horror and delight

The World Health Organization today issued its first-ever report on the use of artificial intelligence in healthcare.

The report is 165 pages cover-to-cover and it provides a summary assessment of the current state of AI in healthcare while also laying out several opportunities and challenges.

Most of what the report covers boils down to six “guiding principles for [AI’s] design and use.”

Per a WHO blog post, these include:

  • Protecting human autonomy
  • Promoting human well-being and safety and the public interest
  • Ensuring transparency, explainability and intelligibility
  • Fostering responsibility and accountability
  • Ensuring inclusiveness and equity
  • Promoting AI that is responsive and sustainable

These bullet points make up the framework for the report’s exploration of the current and potential benefits and dangers of using AI in healthcare.

The good news

The report focuses a lot of attention on cutting through hype to give analysis on the present capabilities of AI in the healthcare sector. And, according to the report, the most common use for AI in healthcare is as a diagnostic aid.

Per the report:

AI is being considered to support diagnosis in several ways, including in radiology and medical imaging. Such applications, while more widely used than other AI applications, are still relatively novel, and AI is not yet used routinely in clinical decision-making.

The WHO anticipates this will soon change.

Per the report, the WHO expects AI to improve nearly every aspect of healthcare from diagnostic accuracy to improved record-keeping. And there’s even hope it could lead to drastically improved outcomes for patients presenting with stroke, heart attack, or other illnesses where early diagnosis is crucial.

Furthermore, AI is a data-based technology. The WHO believes the onset of machine learning technologies in healthcare could help predict the spread of disease and possibly even prevent epidemics in the future.

It’s obvious from the report that the WHO is optimistic for the future of AI in healthcare. However, the report also details numerous challenges and risks associated with the wide-scale implementation of AI technologies into the healthcare system.

The bad news

The report recognizes efforts on behalf of numerous nations to codify the use of AI in healthcare, but it also notes that current policies and regulations aren’t enough to protect patients and the public at large.

Specifically, the report outlines several areas where AI could make things worse. These include modern day concerns such as handing care of the elderly over to inhuman automated systems. And they also include future concerns: what happens when a human doctor disagrees with a black box AI system? If we can’t explain why an AI made a decision, can we defend it if its diagnosis when it matters?

And the report also spends a significant portion of its pages discussing the privacy implications for the full implementation of AI into healthcare.

Per the report:

Collection of data without the informed consent of individuals for the intended uses (commercial or otherwise) undermines the agency, dignity and human rights of those individuals; however, even informed consent may be insufficient to compensate for the power dissymmetry between the collectors of data and the individuals who are the sources.

In other words: Even when everything is transparent, how can anyone be sure patients are giving informed consent when it comes to their medical information? When you consider the circumstances many patients are in when a doctor asks them to consent to a procedure, it’s hard to imagine a scenario where the intricacies of how artificial intelligence operates matters more than than what their doctor is recommending.

You can read the entire WHO report here.

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