Research results call into question the safety and dependability of using artificial intelligence in medical diagnosis, a development that should be watched by clinical laboratory scientists
ChatGPT, an artificial intelligence (AI) chatbot that returns answers to written prompts, has been tested and found wanting by researchers at the University of Florida College of Medicine (UF Health) who looked into how well it could answer typical patient questions on urology. Not good enough according to the researchers who conducted the study.
AI is quickly becoming a powerful new tool in diagnosis and medical research. Some digital pathologists and radiologists use it for data analysis and to speed up diagnostic modality readings. It’s even been said that AI will improve how physicians treat disease. But with all new discoveries there comes controversy, and that’s certainly the case with AI in healthcare.
Many voices in opposition to AI’s use in clinical medicine claim the technology is too new and cannot be trusted with patients’ health. Now, UF Health’s study seems to have confirmed that belief—at least with ChatGPT.
The study revealed that answers ChatGPT provided “fell short of the standard expected of physicians,” according to a UF Health new release, which called ChatGPT’s answers “flawed.”
The questions posed were considered to be common medical questions that patients would ask during a visit to a urologist.
The researchers believes their study is the first of its kind to focus on AI and the urology specialty and which “highlights the risk of asking AI engines for medical information even as they grow in accuracy and conversational ability,” UF Health noted in the news release.
“I am not discouraging people from using chatbots,” said Russell S. Terry, MD (above), an assistant professor in the UF College of Medicine’s department of urology and the study’s senior author, in a UF Health news release. “But don’t treat what you see as the final answer. Chatbots are not a substitute for a doctor.” Pathologists and clinical laboratory managers will want to monitor how developers improve the performance of chatbots and other applications using artificial intelligence. (Photo copyright: University of Florida.)
UF Health ChatGPT Study Details
UF Health’s study featured 13 of the most queried topics from patients to their urologists during office visits. The researchers asked ChatGPT each question three times “since ChatGPT can formulate different answers to identical queries,” they noted in the news release.
The urological conditions the questions covered included:
The researchers then “evaluated the answers based on guidelines produced by the three leading professional groups for urologists in the United States, Canada, and Europe, including the American Urological Association (URA). Five UF Health urologists independently assessed the appropriateness of the chatbot’s answers using standardized methods,” UF Health noted.
Notable was that many of the results were inaccurate. According to UF Health, only 60% of responses were deemed appropriate from the 39 evaluated responses. Outside of those results, the researchers noted in their Urology paper, “[ChatGPT] misinterprets clinical care guidelines, dismisses important contextual information, conceals its sources, and provides inappropriate references.”
When asked, for the most part ChatGPT was not able to accurately provide the sources it referenced for its answers. Apparently, the chatbot was not programmed to provide such sources, the UF Health news release stated.
“It provided sources that were either completely made up or completely irrelevant,” Terry noted in the new release. “Transparency is important so patients can assess what they’re being told.”
Further, “Only 7 (54%) of 13 topics and 21 (54%) of 39 responses met the BD [Brief DISCERN] cut-off score of ≥16 to denote good-quality content,” the researchers wrote in their paper. BD is a validated healthcare information assessment questionnaire that “provides users with a valid and reliable way of assessing the quality of written information on treatment choices for a health problem,” according to the DISCERN website.
ChatGPT often “omitted key details or incorrectly processed their meaning, as it did by not recognizing the importance of pain from scar tissue in Peyronie’s disease. As a result … the AI provided an improper treatment recommendation,” the UF Health study paper noted.
Is Using ChatGPT for Medical Advice Dangerous to Patients?
Terry noted that the chatbot performed better in some areas over others, such as infertility, overactive bladder, and hypogonadism. However, frequently recurring UTIs in women was one topic of questions for which ChatGPT consistently gave incorrect results.
“One of the more dangerous characteristics of chatbots is that they can answer a patient’s inquiry with all the confidence of a veteran physician, even when completely wrong,” UF Health reported.
“In only one of the evaluated responses did the AI note it ‘cannot give medical advice’ … The chatbot recommended consulting with a doctor or medical adviser in only 62% of its responses,” UF Health noted.
For their part, ChatGPT’s developers “tell users the chatbot can provide bad information and warn users after logging in that ChatGPT ‘is not intended to give advice,’” UF Health added.
Future of Chatbots in Healthcare
In UF Health’s Urology paper, the researchers state, “Chatbot models hold great promise, but users should be cautious when interpreting healthcare-related advice from existing AI models. Additional training and modifications are needed before these AI models will be ready for reliable use by patients and providers.”
UF Health conducted its study in February 2023. Thus, the news release points out, results could be different now due to ChatGPT updates. Nevertheless, Terry urges users to get second opinions from their doctors.
“It’s always a good thing when patients take ownership of their healthcare and do research to get information on their own,” he said in the news release. “But just as when you use Google, don’t accept anything at face value without checking with your healthcare provider.”
That’s always good advice. Still, UF Health notes that “While this and other chatbots warn users that the programs are a work in progress, physicians believe some people will undoubtedly still rely on them.” Time will tell whether trusting AI for medical advice turns out well for those patients.
The study reported above is a useful warning to clinical laboratory managers and pathologists that current technologies used in ChatGPT, and similar AI-powered solutions, have not yet achieved the accuracy and reliability of trained medical diagnosticians when answering common questions about different health conditions asked by patients.
In addition to viruses, wastewater analysis can also be used to detect the presence of chemical substances such as opioids
Wastewater surveillance and analysis continues to be a useful tool for detecting the prevalence of viruses such as SARS-CoV-2, influenza, and respiratory syncytial virus (RSV) in a community. Perhaps more importantly, wastewater surveillance can fill in gaps where clinical laboratory testing data may be days or weeks behind the true spread of viral infections.
One sign of the value of testing wastewater for infectious diseases is the fact that government officials are financing a continuing program of wastewater testing. In September, the federal Centers for Disease Control and Prevention (CDC) awarded a contract to conduct wastewater surveillance/analysis worth millions of dollars to Verily Life Sciences, a Google company, rather than renewing its contract with Biobot Analytics, which had been doing the work since 2020. One interesting twist in the award of this contract is how an ensuing dispute pulled the plug on a significant portion of the wastewater analysis in this country.
In their September Morbidity and Mortality Weekly Report (MMWR), the CDC highlighted a CDC study during which wastewater samples were taken from 40 wastewater treatment plants located in Wisconsin’s three largest cities. The samples were collected weekly and tested for influenza and RSV. The findings were then compared with data regarding emergency department (ED) visits for those diseases.
The CDC found that higher detections of flu and RSV were associated with higher rates of ED visits for both illnesses. The study also suggests that wastewater might detect the spread of these viruses earlier than ED visit data alone.
“During the COVID-19 pandemic, wastewater surveillance for SARS-CoV-2 provided valuable insight into community incidence of COVID-19,” said Peter DeJonge, PhD (above), a CDC Career Epidemiology Field Officer, in an interview with Infectious Disease Special Edition. “[The CDC’s] report supports the idea that wastewater surveillance also has the potential to serve as a useful method with which to track community spread of influenza and RSV.” Local clinical laboratories are also involved in the CDC’s wastewater surveillance programs. (Photo copyright: CDC.)
Keeping Communities Informed about Spread of Viral Infections
The CDC’s study was conducted from August 2022 to March 2023. The wastewater samples from all three cities tested positive for the viruses in advance of increases in ED visits. After the ED visits for those viruses had subsided, the viral material remained in sewersheds for up to three months.
“Both influenza and RSV can cause substantial amounts of illness, hospitalization, and even death during annual epidemics, which often occur during winter months in the US,” Peter DeJonge, PhD, a CDC Career Epidemiology Field Officer assigned to the Chicago Department of Public Health, told Infectious Disease Special Edition (IDSE). “Clinical providers and public health officials benefit from surveillance data to understand when and where these diseases are spreading in a community each year. This type of data can help prepare clinics [and clinical laboratories] for anticipated cases, tailor public health messaging, and encourage timely vaccination.”
“The collective burden from these respiratory viruses is staggering. With these viruses circulating simultaneously and potentially shifting in seasonality and severity, communities must be able to understand the full impact of each of these illnesses to inform awareness and public health responses that can prevent infections, hospitalizations, and even deaths,” said Mariana Matus, PhD, CEO and cofounder of Biobot Analytics, in an August press release announcing the launch of a “Respiratory Illnesses Panel” that will monitor wastewater for Influenzas A and B (seasonal flu), Respiratory Syncytial Virus (RSV), and SARS-CoV-2 (COVID-19).
“Traditional testing methods for these illnesses do not provide a comprehensive picture of the number of people infected due to inaccurate reporting, as well as asymptomatic or misdiagnosed cases,” Matus continued. “By monitoring wastewater concurrently for influenza, RSV, and SARS-CoV-2, we can fill in these gaps and provide important information to communities.”
CDC Moves to Change Wastewater Surveillance Contractor Mid-stream
As new variants of SARS-CoV-2 emerge, a recent contract dispute may be the cause of a time delay in efforts to perform wastewater surveillance for the disease, as well as for other viral infections, according to Politico.
The CDC’s move to replace Biobot Analytics with Verily Life Sciences to do wastewater surveillance has led to Biobot filing a protest with the Government Accountability Office (GAO).
According to World Socialist Web Site (WSWS), “The scope of the [Biobot] contract [to provide extended data for the public health agency’s National Wastewater Surveillance System (NWSS)] included data from more than 400 locations from over 250 counties across the entire United States, covering 60 million people. On top of this, Biobot also conducted genomic sequencing to identify the latest variants in circulation.”
About one quarter of the wastewater testing sites in the country have been shut down due to Biobot’s contract being suspended in September. The remaining 1,200 sites that are not covered under the original contract will continue wastewater testing, Politico reported.
The GAO hopes to have a decision on the contract dispute in January. Verily says it is ready to proceed with testing in all locations and already has its infrastructure in place.
“We are committed to working with the CDC to advance the goals of the … testing program, initiate testing on the samples already delivered when allowed to resume work, and make wastewater data available as quickly as possible,” Bradley White, PhD, Principal Scientist/Director at Verily, told Politico.
Under the terms of Verily’s contract, the company will collect samples from wastewater treatment centers cross the county and analyze the samples for COVID-19 and the mpox (monkey pox) virus.
This contract marks the first agreement between the CDC and Verily.
The CDC has not disclosed why it decided to change contractors, but it is probable that cost may have been played a role in the decision. Verily’s contract is for $38 million over the course of five years and Biobot’s most recent contract was for around $31 million for a period of less than 18 months, Politico reported.
In a LinkedIn post, Matus reported that Biobot had already laid off 35% of its staff due to the contract decision by the CDC.
Competition in Wastewater Surveillance Market
When seeking viruses in wastewater, scientists use gene-based detection methods to locate and amplify genetic signs of pathogens. But public health officials are just beginning to tap into the potential opportunities that may exist in the analysis of data present in wastewater.
Wastewater surveillance is also being looked at as a way to combat America’s opioid epidemic.
“Wastewater surveillance is becoming more mature and more mainstream month after month, year over year,” Matus told Time.
Thus, regardless of which companies end up working with the CDC, it appears that wastewater surveillance and analysis, which requires a great deal of clinical laboratory testing, will continue to help fight the spread of deadly viral infections, as well as possibly the nation’s drug epidemic.
Genetic engineers at the lab used the new tool to generate a catalog of 71 million possible missense variants, classifying 89% as either benign or pathogenic
Genetic engineers continue to use artificial intelligence (AI) and deep learning to develop research tools that have implications for clinical laboratories. The latest development involves Google’s DeepMind artificial intelligence lab which has created an AI tool that, they say, can predict whether a single-letter substitution in DNA—known as a missense variant (aka, missense mutation)—is likely to cause disease.
The Google engineers used their new model—dubbed AlphaMissense—to generate a catalog of 71 million possible missense variants. They were able to classify 89% as likely to be either benign or pathogenic mutations. That compares with just 0.1% that have been classified using conventional methods, according to the DeepMind engineers.
This is yet another example of how Google is investing to develop solutions for healthcare and medical care. In this case, DeepMind might find genetic sequences that are associated with disease or health conditions. In turn, these genetic sequences could eventually become biomarkers that clinical laboratories could use to help physicians make earlier, more accurate diagnoses and allow faster interventions that improve patient care.
“AI tools that can accurately predict the effect of variants have the power to accelerate research across fields from molecular biology to clinical and statistical genetics,” wrote Google DeepMind engineers Jun Cheng, PhD (left), and Žiga Avsec, PhD (right), in a blog post describing the new tool. Clinical laboratories benefit from the diagnostic biomarkers generated by this type of research. (Photo copyrights: LinkedIn.)
AI’s Effect on Genetic Research
Genetic experiments to identify which mutations cause disease are both costly and time-consuming, Google DeepMind engineers Jun Cheng, PhD, and Žiga Avsec, PhD, wrote in a blog post. However, artificial intelligence sped up that process considerably.
“By using AI predictions, researchers can get a preview of results for thousands of proteins at a time, which can help to prioritize resources and accelerate more complex studies,” they noted.
Of all possible 71 million variants, approximately 6%, or four million, have already been seen in humans, they wrote, noting that the average person carries more than 9,000. Most are benign, “but others are pathogenic and can severely disrupt protein function,” causing diseases such as cystic fibrosis, sickle-cell anemia, and cancer.
“A missense variant is a single letter substitution in DNA that results in a different amino acid within a protein,” Cheng and Avsec wrote in the blog post. “If you think of DNA as a language, switching one letter can change a word and alter the meaning of a sentence altogether. In this case, a substitution changes which amino acid is translated, which can affect the function of a protein.”
In the Google DeepMind study, AlphaMissense predicted that 57% of the 71 million variants are “likely benign,” 32% are “likely pathogenic,” and 11% are “uncertain.”
The AlphaMissense model is adapted from an earlier model called AlphaFold which uses amino acid genetic sequences to predict the structure of proteins.
“AlphaMissense was fed data on DNA from humans and closely related primates to learn which missense mutations are common, and therefore probably benign, and which are rare and potentially harmful,” The Guardian reported. “At the same time, the program familiarized itself with the ‘language’ of proteins by studying millions of protein sequences and learning what a ‘healthy’ protein looks like.”
The model assigned each variant a score between 0 and 1 to rate the likelihood of pathogenicity [the potential for a pathogen to cause disease]. “The continuous score allows users to choose a threshold for classifying variants as pathogenic or benign that matches their accuracy requirements,” Avsec and Cheng wrote in their blog post.
However, they also acknowledged that it doesn’t indicate exactly how the variation causes disease.
The engineers cautioned that the predictions in the catalog are not intended for clinical use. Instead, they “should be interpreted with other sources of evidence.” However, “this work has the potential to improve the diagnosis of rare genetic disorders, and help discover new disease-causing genes,” they noted.
Genomics England Sees a Helpful Tool
BBC noted that AlphaMissense has been tested by Genomics England, which works with the UK’s National Health Service. “The new tool is really bringing a new perspective to the data,” Ellen Thomas, PhD, Genomics England’s Deputy Chief Medical Officer, told the BBC. “It will help clinical scientists make sense of genetic data so that it is useful for patients and for their clinical teams.”
AlphaMissense is “a big step forward,” Ewan Birney, PhD, Deputy Director General of the European Molecular Biology Laboratory (EMBL) told the BBC. “It will help clinical researchers prioritize where to look to find areas that could cause disease.”
Other experts, however, who spoke with MIT Technology Review were less enthusiastic.
Heidi Rehm, PhD, co-director of the Program in Medical and Population Genetics at the Broad Institute, suggested that the DeepMind engineers overstated the certainty of the model’s predictions. She told the publication that she was “disappointed” that they labeled the variants as benign or pathogenic.
“The models are improving, but none are perfect, and they still don’t get you to pathogenic or not,” she said.
“Typically, experts don’t declare a mutation pathogenic until they have real-world data from patients, evidence of inheritance patterns in families, and lab tests—information that’s shared through public websites of variants such as ClinVar,” the MIT article noted.
Is AlphaMissense a Biosecurity Risk?
Although DeepMind has released its catalog of variations, MIT Technology Review notes that the lab isn’t releasing the entire AI model due to what it describes as a “biosecurity risk.”
The concern is that “bad actors” could try using it on non-human species, DeepMind said. But one anonymous expert described the restrictions “as a transparent effort to stop others from quickly deploying the model for their own uses,” the MIT article noted.
And so, genetics research takes a huge step forward thanks to Google DeepMind, artificial intelligence, and deep learning. Clinical laboratories and pathologists may soon have useful new tools that help healthcare provider diagnose diseases. Time will tell. But the developments are certain worth watching.
Healthcare providers of all types will benefit from acknowledging Gen Z’s preference for digital interactions, self-testing, and over-the-counter medications
Each generation has its own unique connection to how it manages its health, and the latest studies into the healthcare habits of Generation Z (aka, Gen Z or Zoomers) are providing valuable insight that savvy clinical laboratory managers and pathologists—in fact all healthcare providers—can use to better serve their Gen Z patients.
According to McKinsey and Company, Gen Z’s “identity has been shaped by the digital age, climate anxiety, a shifting financial landscape, and COVID-19.” And Pew Research states that Zoomers “are also digital natives who have little or no memory of the world as it existed before smartphones.”
As the largest demographic, “Gen Z stands 2.6 billion members strong. … Globally, they hold purchasing power of more than $500 billion and mobile buying power of $143 billion,” wrote Stacy Rapacon, Managing Editor at Senior Executive Media, in an article she penned for HP’s The Garage.
Meeting Gen Zers’ healthcare needs on their terms would seem to be a judicious choice.
“Gen-Z’s buying power may exceed $3 trillion,” wrote Bernhard Schroeder (above), a clinical lecturer on integrated/online marketing at San Diego State University, in Forbes. “Their spending ability exceeds the gross domestic product of all but about 25 of the world’s countries.” Thus, it behooves healthcare leaders, including clinical laboratory managers and pathologists, to consider how best to approach treating Gen Z patients. (Photo copyright: San Diego State University.)
Gen Z Leads in Digital Healthcare Use, Self-testing, OTC Drugs
“Gen Z engages in every type of digital healthcare activity more than other generations,” a recent study by PYMNTS noted. A total of 2,735 consumers were surveyed, and though all reported using digital healthcare to some degree, Gen Z stood out.
Patient portal access was the highest digital method accessed by Zoomers (62%), followed by telemedicine appointment usage (55%), the PYMNTS report found.
Knowing the direction Gen Z is trending may lead clinical laboratory leaders to expect self-testing to be on the rise, and that hunch would be correct. “There are two converging trends; the rise of women’s health technology and increased use of at-home sample collection for diagnosis tests,” Clinical Lab Products reported.
“Ongoing innovation in these areas could significantly improve the accessibility of women’s health testing. It will also have repercussions for labs, potentially changing the way samples are received and processed, and the way results are distributed. The quantity and quality of samples may be impacted, too. It’s important for labs to be aware of likely developments so they can prepare, and potentially collaborate with the health technology companies driving change,” CLP noted.
Another area feeling the impact of Gen Z’s healthcare spending is the over-the-counter (OTC) drug market.
“Since the pandemic began, more Americans are paying closer attention to their symptoms and looking for easily accessible information about over-the-counter medications, especially for allergies, coughs, and headaches,” said Kim Castro, Editor and Chief Content Officer for US News and World Report, in a press release.
Zoomers Want Healthcare on Their Own Terms
Gen Z grew up with the internet, Amazon, Netflix, Google, and social media since birth.
“The ‘norm’ they experienced as children was a world that operated at speed, scale, and scope. They developed an early facility with powerful digital tools that allowed them to be self-reliant as well as collaborative,” anthropologist Roberta Katz, PhD, a senior research scholar at Stanford’s Center for Advanced Study in the Behavioral Sciences (CASBS) told Stanford News.
As digital natives, Gen Z can be more science and data driven and yet still expect to find health advice on YouTube or TikTok. According to an article published by Harvard Pilgrim Healthcare, “Gen Z is the first generation to grow up surrounded by digital devices, and they expect their health benefits to be digital, too. From choosing a benefits package to finding a provider, Gen Z wants to take care of their health on their own terms. And that may just include video chatting with a doctor from the back of an Uber.”
In its 2022 US Digital Health Survey, research firm Insider Intelligence found that “Half of Gen Z adults turn to social media platforms for health-related purposes, either all the time or often.”
“Gen-Z will make up 31% of the world’s population by 2021 and they have deeply formed perceptions and beliefs … This has led to an amazing change in the way Gen-Z is disrupting several industries simultaneously,” wrote Bernhard Schroeder (above), a clinical lecturer on integrated/online marketing at San Diego State University, in Forbes.
What Can Clinical Laboratories Learn from These Findings
Gen Z seeks accuracy and trustworthy information. “Gen-Zers’ natural penchant for skepticism and frugality—coupled with low levels of confidence in the US healthcare system—makes them less likely to trust providers, more likely to research prices before seeking care, and more apt to worry that their health insurance won’t cover their treatment,” Insider Intelligence noted.
According to Contract Pharma, “Gen Z is concerned with holistic health and self-care, rather than a one size fits all pharmaceutical approach. They share a hesitancy for traditional healthcare models but with very interesting differences. By understanding these differences, the consumer healthcare industry can focus on agile and distinctive brands to harness Gen Z’s tremendous purchasing power.”
Savvy clinical laboratory leaders can better serve their Gen-Z client physicians and patients by better understanding why Zoomers are more inclined to order their own lab tests (without a physician), collect their own specimens to send into labs, and/or collect their own specimens to do home testing (think COVID-19 self-test kits). Zoomers may need an entirely new business model from their healthcare providers, including clinical laboratories.
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.
