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.
Device is latest example that wearable healthcare devices are moving past simple biomarker monitoring and into the area of assisting in rehab
Companies unrelated to traditional clinical laboratory medicine continue to develop wearable devices that enable individuals to monitor their health while also alerting physicians and caregivers in real time when certain biomarkers are out of range.
One recent example is US biotechnology company STAT Health Informatics in Boston, which has developed a wearable device that monitors blood flow to the ear and face “to better understand symptoms such as dizziness, brain fog, headaches, fainting, and fatigue that occur upon standing,” according to a press release. The tiny device is worn in the ear and connects wirelessly to a smartphone app.
Johns Hopkins University clinically tested the STAT device, and according to Medical Device Network, “It can predict a person fainting minutes before it happens and can be worn with more than 90% of devices that go in or around the ear. It can also be left in while sleeping and showering, meaning less likelihood of removing the device and forgetting to replace it.”
Another notable aspect of this invention is that it’s an example of how the ongoing miniaturization of various technologies makes it possible to invent smaller devices but with greater capabilities. In the case of the STAT device, it combines tiny sensors, Bluetooth, and an equally tiny battery to produce a device that fits in the ear and can function for up to three days before needing a recharge.
It’s easy to imagine these technologies being used for other types of diagnostic testing devices that could be managed by clinical laboratories.
“It’s well understood that the ear is a biometric gold mine because of its close proximity to the brain and major arteries. This allows for new biometrics … to be possible,” said Daniel Lee (above), co-founder and CEO of STAT Health, in a press release. “In addition, the ear is largely isolated from data corruption caused by arm motion—a problem that plagues current wearables and prevents them from monitoring heart metrics during many daily tasks. The ear is really the ideal window into the brain and heart.” Clinical laboratory managers may want to watch how this technology is further developed to incorporate other biomarkers for diseases and health conditions. (Photo copyright: STAT Health.)
How STAT Works
Every time the wearer stands, the STAT device tracks the change in response of blood pressure, heart rate, and blood flow to the head. “The device distills all this information into an ‘Up Score’ to track time spent upright. Its ‘Flow Score’ helps users pace their recovery by watching for blood flow abnormalities,” MassDevice reported.
According to the company’s website, STAT is intended for use in individuals who have been diagnosed with conditions known to suffer from drops in blood flow to the head, such as:
As an individual continues to use the device, STAT “learns about each user’s unique body to provide personalized coaching for healthy lifestyle choices,” MassDevice reported.
Another key factor is the technology built into the device. An optical sensor was chosen over ultrasound because STAT Health felt it was both easy to use and provided precise measurements accessing the shallow ear artery, MassDevice reported.
“Despite its small scale, the device incorporates advanced optical sensors, an accelerometer, a pressure sensor, temperature sensors, artificial intelligence (AI)-edge computing, three-day battery life (or more), and a micro solar panel,” Medical Device Network noted.
STAT’s image above demonstrates how truly minute the company’s wearable device is, even though it monitors blood flow to the face and ear looking for signs that the wearer is about to suffer bouts of dizziness or lightheadedness due to a drop in blood flow. (Photo copyright: STAT Health Informatics Inc.)
STAT’s Impact on Users’ Health
STAT’s developers intend the device to help individuals stay on track with their health. “The target population can navigate their condition better. If they’re not standing when they can, they will become deconditioned. This product encourages standing and being upright where possible, as part of rehab,” Lee told Medical Device Network.
Lee has been developing wearable in-ear devices for many years.
“Nobody has realized the ear’s true potential due to the miniaturization and complex systems design needed to make a practical and user-friendly ear wearable,” he told MassDevice. “After multiple engineering breakthroughs, we’ve succeeded in unlocking the ear to combine the convenience and long-term nature of wearables with the high fidelity nature of obtrusive clinical monitors. No other device comes close along the axis of wearability and cardiac signal quality, which is why we believe STAT is truly the world’s most advanced wearable.”
For clinical laboratories, though STAT is not a diagnostic test, it is the latest example of how companies are developing wearable monitoring devices intended to allow individuals to monitor their health. It moves beyond the simple monitoring of Apple Watch and Fitbit. This device can aid individuals during rehab.
Wearable healthcare devices will continue to be introduced that are smaller, allow more precise measurements of target biomarkers, and alert wearers in real time when those markers are out of range. Keeping in tune with the newest developments will help clinical laboratories and pathologists find new ways to support healthcare providers who recommend these devices for monitoring their patients conditions.
Spectroscopic technique was 91% accurate in identifying the notoriously difficult-to-diagnose disease suggesting a clinical diagnostic test for CFS may be possible
Most clinical pathologists know that, despite their best efforts, scientists have failed to come up with a reliable clinical laboratory blood test for diagnosing myalgic encephalomyelitis (ME), the condition commonly known as chronic fatigue syndrome (CFS)—at least not one that’s ready for clinical use.
But now an international team of researchers at the University of Oxford has developed an experimental non-invasive test for CFS using a simple blood draw, artificial intelligence (AI), and a spectroscopic technique known as Raman spectroscopy.
The approach uses a laser to identify unique cellular “fingerprints” associated with the disease, according to an Oxford news release.
“When Raman was added to a panel of potentially diagnostic outputs, we improved the ability of the model to identify the ME/CFS patients and controls,” Karl Morten, PhD, Director of Graduate Studies and Principal Investigator at Oxford University, told Advanced Science News. Morton led the research team along with Wei Huang, PhD, Professor of Biological Engineering at Oxford.
The researchers claim the test is 91% accurate in differentiating between healthy people, disease controls, and ME/CFS patients, and 84% accurate in differentiating between mild, moderate, and severe cases, the new release states.
“This could be a game changer as we are unsure what causes [ME/CFS] and diagnosis occurs perhaps 10 to 20 years after the condition has started to develop,” said Karl Morten, PhD, Director of Graduate Studies and Principal Investigator at Oxford University. “An early diagnosis might allow us to identify what is going wrong with the potential to fix it before the more long-term degenerative changes are observed.” Though this research may not lead to a simple clinical laboratory blood test for CFS, any non-invasive diagnostic test would enable doctors to help many people. (Photo copyright: Oxford University.)
Need for an ME/CFS Test
The federal Centers for Disease Control and Prevention (CDC) describes ME/CFS as “a serious, long-term illness that affects many body systems,” with symptoms that include severe fatigue and sleep difficulties. Citing an Institute of Medicine (IoM) report, the agency estimates that 836,000 to 2.5 million Americans suffer from the condition but notes that most cases have not been diagnosed.
“One of the difficulties is the complexity of the disease,” said Jonas Bergquist, MD, PhD, Director of the ME/CFS Research Center of Uppsala University in Sweden, told Advanced Science News. “Because it’s a multi-organ disorder, you get symptoms from many different regions of the body with different onsets, though it’s common with post viral syndrome to have different overlapping [symptoms] that disguise the diagnosis.” Bergquist was not involved with the Oxford study.
One key to the Oxford researchers’ technique is the use of multiple artificial intelligence models to analyze the spectral profiles. “These signatures are complex and by eye there are not necessarily clear features that separate ME/CFS patients from other groups,” Morten told Advanced Science News.
“The AI looks at this data and attempts to find features which can separate the groups,” he continued. “Different AI methods find different features in the data. Individually, each method is not that successful at assigning an unknown sample to the correct group. However, when we combine the different methods, we produce a model which can assign the subjects to the different groups very accurately.”
Without a reliable test, “diagnosis of the condition is difficult, with most patients relying on self-report, questionnaires, and subjective measures to receive a diagnosis,” the Oxford press release noted.
But developing such a test has been challenging, Advanced Science News noted.
How Oxford’s Raman Technique Works
Raman spectroscopy uses a laser to determine the “vibrational modes of molecules,” according to the Oxford press release.
“When a laser beam is directed at a cell, some of the scattered photons undergo frequency shifts due to energy exchanges with the cell’s molecular components,” the press release stated. “Raman micro-spectroscopy detects these shifted photons, providing a non-invasive method for single cell analysis. The resulting single cell Raman spectra serve as a unique fingerprint, revealing the intrinsic and biochemical properties and indicating the physiological and metabolic state of the cell.”
The researchers employed the technique on blood samples from 98 subjects, including 61 ME/CFS patients, 16 healthy controls, and 21 controls with multiple sclerosis (MS), Advanced Science reported.
The Oxford scientists focused their attention on peripheral blood mononuclear cells (PBMCs), as previous studies found that these cells showed “reduced energetic function” in ME/CFS patients. “With this evidence, the team proposed that single-cell analysis of PBMCs might reveal differences in the structure and morphology in ME/CFS patients compared to healthy controls and other disease groups such as multiple sclerosis,” the press release states.
Clinical Laboratory Blood Processing and the Oxford Raman Technique
Oxford’s Raman spectroscopic technique “only requires a small blood sample which could be developed as a point-of-care test perhaps from one drop of blood,” the researchers wrote. However, Advanced Science News pointed out that required laser microscopy equipment costs more than $250,000.
In their Advanced Science paper, the researchers note that the test could be made more widely available by transferring blood samples collected by local clinical laboratories to diagnostic centers that have the needed hardware.
“Alternatively, a compact system containing portable Raman instruments could be developed, which would be much cheaper than a standard Raman microscope, and [which] incorporated with microfluidic systems to stream cells through a Raman laser for detection, eliminating the need for lengthy blood sample processing,” the researchers wrote.
They noted that the technique could be adapted to test for other chronic conditions as well, such as MS, fibromyalgia, Lyme disease, and long COVID.
“Our paper is very much a starting point for future research,” Morten told Advanced Science News. “Larger cohorts need to be studied, and if Raman proves useful, we need to think carefully about how a test might be developed.”
Bergquist agreed, stating it’s “not necessarily something you would see in a doctor’s office. It requires a lot of advanced data analysis to use—I still see it as a research methodology. But in the long run, it could be developed into a tool that could be used in a more simplistic way.”
Though a useable diagnostic test may be far off, clinical laboratories should consider how they can aid in ME/CFS research.
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.
Studying gut bacteria continues to intrigue investors, but can the results produce viable diagnostic data for healthcare providers?
Even as microbiologists and clinical pathologists closely watch research into the human microbiome and anticipate study findings that could lead to new medical laboratory tests based on microbiome testing, there are entrepreneurs ready to tout the benefits of microbiome testing to consumers. That’s the impetus behind an announced deal between a microbiome testing company and a national pharmacy chain.
That deal involves health startup Viome Life Sciences, which recently closed a $86.5 million Series C funding round to support research and development of its consumer health at-home test kits, and CVS, which will sell Viome’s Gut Intelligence Test at 200 of the pharmacy company’s retail locations nationwide, according to an August press release.
“Founded seven years ago by serial entrepreneur Naveen Jain, Viome sells at-home kits that analyze the microbial composition of stool samples and provide food recommendations, as well as supplements and probiotics. Viome says it is the first company to sell gut tests at CVS, both online and in-store. The tests will sell for $179,” GeekWire reported.
Investors appear to be intrigued by these types of opportunities. To date, Viome has raised a total of $175 million.
“In a world where healthcare has often been reactive, treating symptoms and targeting diseases only after they manifest, Viome is pioneering a transformative shift by harnessing the innate power of food and nutrition,” stated Naveen Jain (above), Founder and CEO of Viome, in a press release. “Our mission is not just to prolong life but to enrich it, enabling everyone to thrive in health and vitality.” But some microbiologists and clinical laboratory scientists would consider that the current state of knowledge about the human microbiome is not well-developed enough to justify offering direct-to-consumer microbiology tests that encourage consumers to purchase nutritional products. (Photo copyright: Viome Life Sciences.)
Empowering People to Make Informed Decisions about Their Health
Established in 2016, Bellevue, Washington-based Viome produces and sells, among other tests, its Gut Intelligence at-home test kit, which analyzes the microbial composition of stool samples. This kit relies on RNA sequencing to detect bacteria and other elements present in the gut, such as yeasts and viruses.
The genetic data is then entered into an artificial intelligence (AI) algorithm to provide individuals with information regarding their personal gut health. Viome partnered with Los Alamos National Laboratory to create their AI platform. The company has collected more than 600,000 test samples to date.
“We are the only company that looks at the gene expression and what these microbes are doing,” said Naveen Jain, Founder and CEO of Viome in the press release.
Viome uses technology combined with science to determine the optimal outcomes for each individual consumer based on his or her unique human and microbial gene expression. The data derived from the microbiome is also utilized to offer nutritional recommendations and supplement advice to test takers.
“At Viome, we’re empowering our customers with an individualized nutrition strategy, cutting through the noise of temporary trends and one-size-fits-all advice,” Jain added. “We’re on a journey to redefine aging itself, and we’re invigorated by the support of our investors and customers. Together, we’re building pathways to wellness that hold the potential to enhance the lives of billions of fellow humans across the globe.”
Manipulating Microbiome through Diet
Some scientists, however, are not sold on the idea of microbiome test kits and the data they offer to healthcare providers for treating illnesses.
“The best thing anybody can do for their microbiome is to eat a healthy diet. That’s the best way of manipulating your microbiome,” David Suskind, MD, a gastroenterologist at Seattle Children’s Hospital and Professor of Pediatrics at the University of Washington, told GeekWire.
Verdu, GeekWire reported, added that “there needs to be standardization of protocols and better understanding of microbiome function in health and disease.”
“Recommendations for such commercial kits would have to be based on evidence-based guidelines, which currently do not exist,” she told GeekWire.
Nevertheless, Jain remains positive about the value of microbiome testing. “The future of medicine will be delivered at home, not at the hospital. And the medicines of the future are going to come from a farm, not a pharmacy,” he told GeekWire.
Other Viome At-home Tests
According to a paper published in the journal Therapeutic Advances in Gastroenterology titled, “Role of the Gut Microbiota in Health and Chronic Gastrointestinal Disease: Understanding a Hidden Metabolic Organ,” the human gut contains trillions of microbes, and no two people share the exact same microbiome composition. This complex community of microbial cells influences human physiology, metabolism, nutrition and immune function, and performs a critical role in overall health.
CVS currently sells Viome’s “Gut Intelligence Health Insights Plus Personalized Nutrition Plan” on its website for $149.99. Prices may vary from online to in-store. The test is intended for individuals who want to monitor and address gut imbalances or health symptoms, such as:
Constipation
Diarrhea
Stomach pain
Bloating
Heartburn
Itchy skin
Trouble maintaining a healthy weight
Viome sells the Gut Intelligence Test for $179 on its own website, as well as the following health tests:
Viome also sell precision probiotics and prebiotics, as well as supplements and oral health lozenges.
Gut microbiome testing kits, such as the one from Viome, typically require the collection of a stool sample. Healthcare consumers have in the past been reluctant to perform such testing, but as more information regarding gut health is published, that reluctance may diminish.
Clinical laboratories also have a stake in the game. Dynamic direct to consumer at-home testing has the potential to generate revenue for clinical laboratories, while helping consumers who want to monitor different aspects of their health. But this would be an adjunct to the primary mission of medical laboratories to provide testing services to local physicians and their patients.
