This AI platform has the potential to also reduce workload of radiologists, but also of anatomic pathologists and oncologists allowing them to be more productive
When the UK’s National Health Service (NHS) recently tested an artificial intelligence (AI) platform’s ability to analyze mammograms, the AI found early signs of breast cancer that “human doctors” had previously missed, the BBC reported. This level of ability by AI might soon be adapted to aid overworked anatomic pathologists and cancer doctors in the United Kingdom.
Out of 10,000 mammograms MIA analyzed, the AI platform found “tiny signs of breast cancer in 11 women” which had not been spotted during earlier examinations, the BBC noted, adding that the cancers “were practically invisible to the human eye.”
This is a significant development in AI’s role in healthcare. Anatomic pathologists and clinical laboratory leaders will note that ongoing advancements in AI are enabling technology developers to apply their solutions to assessing radiology images, as well as in whole slide imaging used in digital pathology. In the UK, use of AI, the BBC noted, may also help ease doctor’s workloads.
“This is just the beginning of our work with Kheiron,” said Ben Glocker, PhD (above), Professor in Machine Learning for Imaging at Imperial College London and Head of ML Research at Kheiron Medical, in a news release. “We are actively working on new methodologies for the safe deployment and continuous monitoring of MIA to support a US and UK rollout. We are working hard to make sure that as many women as possible will benefit from the use of this new technology within the next year.” AI tools such as MIA may soon take much of the load from anatomic pathologists and radiologists. (Photo copyright: Imperial College London.)
MIA Cloud-based AI Platform
Kheiron was founded in 2016 and MIA was named one of the seven biggest medical breakthroughs in 2023 by ABC News. A study conducted by Imperial College London in 2023 found that MIA “could significantly increase the early detection of breast cancers in a European healthcare setting by up to 13%,” according to an Imperial news release.
“The study was conducted over three phases (two pilot phases and a live roll-out). Overall across the three phases, the AI reader found 24 more cancers than the standard human reading—a 7% relative increase—and resulted in 70 more women recalled (0.28% relative increase),” the news release reported. “Of the additional recalls, six (initial pilot), 13 (extended pilot), and 11 (live use) additional cancers were found, increasing relative cancer detection rate by 13%, 10%, and 5% respectively. [The researchers] found that 83% of the additional cancers detected using MIA in real clinical practice were invasive, showing that MIA can detect cancers where early detection is particularly vital.”
Supported by Microsoft’s Azure Cloud, MIA came together over six years based on training encompassing millions of mammograms worldwide, Healthcare Digital reported.
“AI tools are generally pretty good at spotting symptoms of a specific disease if they are trained on enough data to enable them to be identified. This means feeding the program with as many different anonymized images of those symptoms as possible, from as diverse a range of people as possible,” Sarah Kerruish, Chief Strategy Officer, Kheiron, told Healthcare Digital.
MIA has been trained to “recognize subtle patterns and anomalies” that can point to “cancerous cells even in their earliest stages of development,” Dataconomy reported.
MIA Finds Early Cancer Signs
In the pilot study, MIA examined mammograms from 10,889 women. Each image had previously been reviewed by two radiologists, the BBC reported.
Findings include the following according to Healthcare Digital:
MIA “flagged” all people the physicians previously identified with symptoms.
The AI platform discovered 11 people with cancer the doctors did not identify.
The cancer MIA discovered—and the doctors did not—suggested cancer in early stages.
So, how did the doctors miss the cancer that MIA spotted? Gerald Lip, MD, Clinical Director for Breast Screening in North East Scotland who led the pilot study for the NHS, told Healthcare Digital, “part of the power of AI is it’s not prone to exhaustion or distraction.
“There is an element of fatigue,” he said. “You get disruptions, someone’s coming in, someone’s chatting in the background. There are lots of things that can probably throw you off your regular routine as well. And in those days when you have been distracted, you go, ‘how on earth did I miss that?’ It does happen.”
Lip is also the Chief Investigator in the Mammography Artificial Intelligence Project in the Industrial Center for Artificial Intelligence and Digital Diagnostics in Scotland.
“I see MIA as a friend and an augmentation to my practice,” he told Healthcare Digital. “MIA isn’t perfect. It had no access to patient history so [it] would flag cysts that had already been identified by previous scans and designated harmless.”
AI as a Safety Net
In the 2023 study, researchers from Imperial College London deployed MIA as an extra reader for mammograms of 25,065 women who visited screening sites in Hungary between April 2021 and January 2023, according to a news release.
“Our prospective real-world usage data in Hungary provides evidence for a significant, measurable increase of early breast cancer detection when MIA is used in clinical practice,” said Peter Kecskemethy, PhD, CEO and co-founder of Kheiron Medical, in the news release.
“Our study shows that AI can act as an effective safety net—a tool to prevent subtler signs of cancer from falling through the cracks,” said Ben Glocker, PhD, Professor in Machine Learning for Imaging at Imperial College London and Head of ML Research at Kheiron Medical, in the news release.
More studies are needed before MIA can be used in clinical settings. Nevertheless, use of AI in radiology—specifically mammograms—where the AI tool can identify very small cancers typically undetectable by radiologists, would be a boon to cancer doctors and the patients they treat.
So far, the research suggests that the AI-powered MIA has benefits to deployment in breast cancer screening. Eventually, it may also make impressive contributions to medical diagnosis and patient care, particularly if MIA eventually proves to be effective at analyzing the whole slide images used by anatomic pathologists.
Expanded genomic dataset includes a wider racial diversity which may lead to improved diagnostics and clinical laboratory tests
Human genomic research has taken another important step forward. The National Institutes of Health’s All of Us research program has reached a milestone of 250,000 collected whole genome sequences. This accomplishment could escalate research and development of new diagnostics and therapeutic biomarkers for clinical laboratory tests and prescription drugs.
The NIH’s All of Us program “has significantly expanded its data to now include nearly a quarter million whole genome sequences for broad research use. About 45% of the data was donated by people who self-identify with a racial or ethnic group that has been historically underrepresented in medical research,” the news release noted.
Detailed information on this and future data releases is available at the NIH’s All of us Data Roadmap.
“For years, the lack of diversity in genomic datasets has limited our understanding of human health,” said Andrea Ramirez, MD, Chief Data Officer, All of Us Research Program, in the news release. Clinical laboratories performing genetic testing may look forward to new biomarkers and diagnostics due to the NIH’s newly expanded gene sequencing data set. (Photo copyright: Vanderbilt University.)
Diverse Genomic Data is NIH’s Goal
NIH launched the All of Us genomic sequencing program in 2018. Its aim is to involve more than one million people from across the country and reflect national diversity in its database.
So far, the program has grown to include 413,450 individuals, with 45% of participants self-identifying “with a racial or ethnic group that has been historically under-represented in medical research,” NIH said.
“By engaging participants from diverse backgrounds and sharing a more complete picture of their lives—through genomic, lifestyle, clinical, and social environmental data—All of Us enables researchers to begin to better pinpoint the drivers of disease,” said Andrea Ramirez, MD, Chief Data Officer of the All of Us research program, in the news release.
More than 5,000 researchers are currently registered to use NIH’s All of Us genomic database. The vast resource contains the following data:
245,350 whole genome sequences, which includes “variation at more than one billion locations, about one-third of the entire human genome.”
1,000 long-read genome sequences to enable “a more complete understanding of the human genome.”
Analysis of drugs effectiveness in different patients.
Data Shared with Participants
Participants in the All of Us program, are also receiving personalized health data based on their genetic sequences, which Dark Daily previously covered.
“Through a partnership with participants, researchers, and diverse communities across the country, we are seeing incredible progress towards powering scientific discoveries that can lead to a healthier future for all of us,” said Josh Denny, MD, Chief Executive Officer, All of Us Research Program, in the news release.
“[Researchers] can get access to the tools and the data they need to conduct a project with our resources in as little as two hours once their institutional data use agreement is signed,” said Fornessa Randal, Executive Director, Center for Asian Health Equity, University of Chicago, in a YouTube video about Researcher Workbench.
For diagnostics professionals, the growth of available whole human genome sequences as well as access to participants in the All of Us program is noteworthy.
Also impressive is the better representation of diversity. Such information could result in medical laboratories having an expanded role in precision medicine.
Google designed the suite to ease radiologists’ workload and enable easy and secure sharing of critical medical imaging; technology may eventually be adapted to pathologists’ workflow
Clinical laboratory and pathology group leaders know that Google is doing extensive research and development in the field of cancer diagnostics. For several years, the Silicon Valley giant has been focused on digital imaging and the use of artificial intelligence (AI) algorithms and machine learning to detect cancer.
Now, Google Cloud has announced it is launching a new medical imaging suite for radiologists that is aimed at making healthcare data for the diagnosis and care of cancer patients more accessible. The new suite “promises to make medical imaging data more interoperable and useful by leveraging artificial intelligence,” according to MedCity News.
In a press release, medical technology company Hologic, and healthcare provider Hackensack Meridian Health in New Jersey, announced they were the first customers to use Google Cloud’s new suite of medical imaging products.
“Hackensack Meridian Health has begun using it to detect metastasis in prostate cancer patients earlier, and Hologic is using it to strengthen its diagnostic platform that screens women for cervical cancer,” MedCity News reported.
“Google pioneered the use of AI and computer vision in Google Photos, Google Image Search, and Google Lens, and now we’re making our imaging expertise, tools, and technologies available for healthcare and life sciences enterprises,” said Alissa Hsu Lynch (above), Global Lead of Google Cloud’s MedTech Strategy and Solutions, in a press release. “Our Medical Imaging Suite shows what’s possible when tech and healthcare companies come together.” Clinical laboratory companies may find Google’s Medical Imaging Suite worth investigating. (Photo copyright: Influencive.)
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Easing the Burden on Radiologists
Clinical laboratory leaders and pathologists know that laboratory data drives most healthcare decision-making. And medical images make up 90% of all healthcare data, noted an article in Proceedings of the IEEE (Institute of Electrical and Electronics Engineers).
More importantly, medical images are growing in size and complexity. So, radiologists and medical researchers need a way to quickly interpret them and keep up with the increased workload, Google Cloud noted.
“The size and complexity of these images is huge, and, often, images stay sitting in data siloes across an organization,” said Alissa Hsu Lynch, Global Lead, MedTech Strategy and Solutions at Google, told MedCity News. “In order to make imaging data useful for AI, we have to address interoperability and standardization. This suite is designed to help healthcare organizations accelerate the development of AI so that they can enable faster, more accurate diagnosis and ease the burden for radiologists,” she added.
According to the press release, Google Cloud’s Medical Imaging Suite features include:
Imaging Storage: Easy and secure data exchange using the international DICOM (digital imaging and communications in medicine) standard for imaging. A fully managed, highly scalable, enterprise-grade development environment that includes automated DICOM de-identification. Seamless cloud data management via a cloud-native enterprise imaging PACS (picture archiving and communication system) in clinical use by radiologists.
Imaging Lab: AI-assisted annotation tools that help automate the highly manual and repetitive task of labeling medical images, and Google Cloud native integration with any DICOMweb viewer.
Imaging Datasets and Dashboards: Ability to view and search petabytes of imaging data to perform advanced analytics and create training datasets with zero operational overhead.
Imaging AI Pipelines: Accelerated development of AI pipelines to build scalable machine learning models, with 80% fewer lines of code required for custom modeling.
Imaging Deployment: Flexible options for cloud, on-prem (on-premises software), or edge deployment to allow organizations to meet diverse sovereignty, data security, and privacy requirements—while providing centralized management and policy enforcement with Google Distributed Cloud.
First Customers Deploy Suite
Hackensack Meridian Health hopes Google’s imaging suite will, eventually, enable the healthcare provider to predict factors affecting variance in prostate cancer outcomes.
“We are working toward building AI capabilities that will support image-based clinical diagnosis across a range of imaging and be an integral part of our clinical workflow,” said Sameer Sethi, Senior Vice President and Chief Data and Analytics Officer at Hackensack, in a news release.
The New Jersey healthcare network said in a statement that its work with Google Cloud includes use of AI and machine learning to enable notification of newborn congenital disorders and to predict sepsis risk in real-time.
Hologic, a medical technology company focused on women’s health, said its collaboration integrates Google Cloud AI with the company’s Genius Digital Diagnostics System.
“By complementing our expertise in diagnostics and AI with Google Cloud’s expertise in AI, we’re evolving our market-leading technologies to improve laboratory performance, healthcare provider decision making, and patient care,” said Michael Quick, Vice President of Research and Development and Innovation at Hologic, in the press release.
Hologic says its Genius Digital Diagnostics System combines AI with volumetric medical imaging to find pre-cancerous lesions and cancer cells. From a Pap test digital image, the system narrows “tens of thousands of cells down to an AI-generated gallery of the most diagnostically relevant,” according to the company website.
Hologic plans to work with Google Cloud on storage and “to improve diagnostic accuracy for those cancer images,” Hsu Lynch told MedCity News.
Medical image storage and sharing technologies like Google Cloud’s Medical Imaging Suite provide an opportunity for radiologists, researchers, and others to share critical image studies with anatomic pathologists and physicians providing care to cancer patients.
One key observation is that the primary function of this service that Google has begun to deploy is to aid in radiology workflow and productivity, and to improve the accuracy of cancer diagnoses by radiologists. Meanwhile, Google continues to employ pathologists within its medical imaging research and development teams.
Assuming that the first radiologists find the Google suite of tools effective in support of patient care, it may not be too long before Google moves to introduce an imaging suite of tools designed to aid the workflow of surgical pathologists as well.
And in less than eight hours, they had diagnosed a child with a rare genetic disorder, results that would take clinical laboratory testing weeks to return, demonstrating the clinical value of the genomic process
In another major genetic sequencing advancement, scientists at Stanford University School of Medicine have developed a method for rapid sequencing of patients’ whole human genome in as little as five hours. And the researchers used their breakthrough to diagnose rare genetic diseases in under eight hours, according to a Stanford Medicine news release. Their new “ultra-rapid genome sequencing approach” could lead to significantly faster diagnostics and improved clinical laboratory treatments for cancer and other diseases.
“A few weeks is what most clinicians call ‘rapid’ when it comes to sequencing a patient’s genome and returning results,” said cardiovascular disease specialist Euan Ashley, MD, PhD (above), professor of medicine, genetics, and biomedical data science, at Stanford University in the news release. “The right people suddenly came together to achieve something amazing. We really felt like we were approaching a new frontier.” Their results could lead to faster diagnostics and clinical laboratory treatments. (Photo copyright: Stanford Medicine.)
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Need for Fast Genetic Diagnosis
In their NEJM paper, the Stanford scientists argue that rapid genetic diagnosis is key to clinical management, improved prognosis, and critical care cost savings.
“Although most critical care decisions must be made in hours, traditional testing requires weeks and rapid testing requires days. We have found that nanopore genome sequencing can accurately and rapidly provide genetic diagnoses,” the authors wrote.
To complete their study, the researchers sequenced the genomes of 12 patients from two hospitals in Stanford, Calif. They used nanopore genome sequencing, cloud computing-based bioinformatics, and a “custom variant prioritization.”
Their findings included:
Five people received a genetic diagnosis from the sequencing information in about eight hours.
Diagnostic rate of 42%, about 12% higher than the average rate for diagnosis of genetic disorders (the researchers noted that not all conditions are genetically based and appropriate for sequencing).
Five hours and two minutes to sequence a patient’s genome in one case.
Seven hours and 18 minutes to sequence and diagnose that case.
How the Nanopore Process Works
To advance sequencing speed, the researchers used equipment by Oxford Nanopore Technologies with 48 sequencing units called “flow cells”—enough to sequence a person’s whole genome at one time.
The Oxford Nanopore PromethION Flow Cell generates more than 100 gigabases of data per hour, AI Time Journal reported. The team used a cloud-based storage system to enable computational power for real-time analysis of the data. AI algorithms scanned the genetic code for errors and compared the patients’ gene variants to variants associated with diseases found in research data, Stanford explained.
According to an NVIDIA blog post, “The researchers accelerated both base calling and variant calling using NVIDIA GPUs on Google Cloud. Variant calling, the process of identifying the millions of variants in a genome, was also sped up with NVIDIA Clara Parabricks, a computational genomics application framework.”
Rapid Genetic Test Produces Clinical Benefits
“Together with our collaborators and some of the world’s leaders in genomics, we were able to develop a rapid sequencing analysis workflow that has already shown tangible clinical benefits,” said Mehrzad Samadi, PhD, NVIDIA Senior Engineering Manager and co-author of the NEJM paper, in the blog post. “These are the kinds of high-impact problems we live to solve.”
In their paper, the Stanford researchers described their use of the rapid genetic test to diagnose and treat an infant who was experiencing epileptic seizures on arrival to Stanford’s pediatric emergency department. In just eight hours, their diagnostic test found that the infant’s convulsions were attributed to a mutation in the gene CSNK2B, “a variant and gene known to cause a neurodevelopmental disorder with early-onset epilepsy,” the researchers wrote.
“By accelerating every step of this process—from collecting a blood sample to sequencing the whole genome to identifying variants linked to diseases—[the Stanford] research team took just hours to find a pathogenic variant and make a definitive diagnosis in a three-month-old infant with a rare seizure-causing genetic disorder. A traditional gene panel analysis ordered at the same time took two weeks to return results,” AI Time Journal reported.
New Benchmarks
The Stanford research team wants to cut the sequencing time in half. But for now, the five-hour rapid whole genome sequence can be considered by clinical laboratory leaders, pathologists, and research scientists a new benchmark in genetic sequencing for diagnostic purposes.
Stories like Stanford’s rapid diagnosis of the three-month old patient with epileptic seizures, point to the ultimate value of advances in genomic sequencing technologies.
Strategists agree that big tech is disrupting healthcare,
so how will clinical laboratories and anatomic pathology groups serve virtual
healthcare customers?
Visionary XPRIZE founder Peter Diamandis, MD, sees big tech as “the doctor of the future.” In an interview with Fast Company promoting his new book, “The Future Is Faster Than You Think,” Diamandis, who is the Executive Chairman of the XPRIZE Foundation, said that the healthcare industry is “phenomenally broken” and that Apple, Amazon, and Google could do “a thousandfold” better job.
Diamandis, who also founded Singularity University, a global learning and innovation community that uses exponential technologies to tackle worldwide challenges, according to its website, said, “We’re going to see Apple and Amazon and Google and all the data-driven companies that are in our homes right now become our healthcare providers.”
If this prediction becomes reality, it will bring significant changes in the traditional ways that consumers and patients have selected providers and access healthcare services. In turn, this will require all clinical laboratories and pathology groups to develop business strategies in response to these developments.
Amazon Arrives in Healthcare Markets
Several widely-publicized business initiatives by Amazon, Google, and Apple substantiate these predictions. According to an Amazon blog, healthcare insurers, providers, and pharmacy benefit managers are already operating HIPAA-eligible Amazon Alexa for:
Alexa also enables HIPAA-compliant blood glucose updates as part of the Livongo for Diabetes program. “Our members now have the ability to hear their last blood glucose check by simply asking Alexa,” said Jennifer Schneider, MD, President of Livongo, a digital health company, in a news release.
And Cigna’s “Answers By Cigna” Alexa “skill” gives members who install the option responses to 150 commonly asked health insurance questions, explained a Cigna news release.
“Google plans to disrupt healthcare and use data and artificial intelligence,” Toby Cosgrove, Executive Advisor to the Google Cloud team and former Cleveland Clinic President, told B2B information platform PYMNTs.com.
PYMNTs speculated that Google, which recently acquired Fitbit, could be aiming at connecting consumers’ Fitbit fitness watch data with their electronic health records (EHRs).
“Ultimately what’s best is human and AI collaboratively,” Peter Diamandis, MD, founder of XPRIZE Foundation and Singularity University told Fast Company. “But I think for reading x-rays, MRIs, CT scans, genome data, and so forth, that once we put human ego aside, machine learning is a much better way to do that.” (Photo copyright: SALT.)
Apple Works with Insurers, Integrating Health Data
The Apple Watch health app also enables people to access medical laboratory test results and vaccination records, and “sync up” information with some hospitals, Business Insider explained.
Virtual Care, a Payer Priority: Survey
Should healthcare providers feel threatened by the tech giants? Not necessarily. However, employers and payers surveyed by the National Business Group on Health (NBGH), an employer advocacy organization, said they want to see more virtual care solutions, a news release stated.
“One of the challenges employers face in managing their healthcare costs is that healthcare is delivered locally, and change is not scalable. It’s a market-by-market effort,” said Brian Marcotte, President and CEO of the NBGH, in the news release. “Employers are turning to market-specific solutions to drive meaningful changes in the healthcare delivery system.
“Virtual care solutions bring healthcare to the consumer
rather than the consumer to healthcare,” Marcotte continue. “They continue to
gain momentum as employers seek different ways to deliver cost effective,
quality healthcare while improving access and the consumer experience.”
“In AI, there are three trends to watch,” said health strategist Ted Schwab (above) while speaking at the 2019 Executive War College. “The first major AI trend will affect clinical laboratories and pathologists. It involves how diagnosis will be done on the Internet and via telehealth. The second AI trend is care delivery, such as what we’ve seen with Amazon’s Alexa—you should know that Amazon’s business strategy is to disrupt healthcare. And the third AI trend involves biological engineering,” he concluded. (Photo copyright: Dark Daily.)
“If you use Google in the United States to check symptoms,
you’ll get five-million to 11-million hits,” Schwab told The Dark Report.
“Clearly, there’s plenty of talk about symptom checkers, and if you go online
now, you’ll find 350 different electronic applications that will give you
medical advice—meaning you’ll get a diagnosis over the internet. These
applications are winding their way somewhere through the regulatory process.
“The FDA just released a report saying it plans to regulate
internet doctors, not telehealth doctors and not virtual doctors,” he
continued. “Instead, they’re going to regulate machines. This news is
significant because, today, within an hour of receiving emergency care, 45% of
Americans have googled their condition, so the cat is out of the bag as it
pertains to us going online for our medical care.”
Be Proactive, Not Reactive, Health Leaders Say
Healthcare leaders need to work on improving access to primary care, instead of becoming defensive or reactive to tech companies, several healthcare CEOs told Becker’s Hospital Review.
Clinical laboratory leaders are advised to keep an eye on
these virtual healthcare trends and be open to assisting doctors engaged in
telehealth services and online diagnostic activities.
