Clinical laboratory managers should note that this company’s new diagnostic offering involving screening embryos for specific genetic conditions is not without controversy
Is the world ready for whole genome sequencing (WGS) of preimplantation embryos to help couples undergoing in vitro fertilization (IVF) treatments know if their embryos have potential genetic health problems? Orchid Health, a clinical preimplantation genetic testing (PGT) laboratory that conducts genetic screening in San Francisco, believes the answer is yes! But the cost is high, and the process is not without controversy.
According to an article in Science, Orchid’s service—a sequencings of the whole human genome of preimplantation embryos at $2,500 per embryo tested—“will look not just for single-gene mutations that cause disorders such as cystic fibrosis, but also more extensively for medleys of common and rare gene variants known to predispose people to neurodevelopmental disorders, severe obesity, and certain psychiatric conditions such as schizophrenia.”
However, Science also noted that some genomics researchers “claim the company inappropriately uses their data to generate some of its risk estimates,” adding that the “Psychiatric Genomics Consortium (PGC), an international group of more than 800 researchers working to decode the genetic and molecular underpinnings of mental health conditions, says Orchid’s new test relies on data [PGC] produced over the past decade, and that the company has violated restrictions against the data’s use for embryo screening.”
There are some who assert that a whole genome sequence of an embryo—given today’s state of genetic technology and knowledge—could generate information that cannot be interpreted accurately in ways that help parents and doctors make informed prenatal testing decisions. At the same time, criticisms expressed by the PGC raise reasonable points.
Perhaps this is a sign of the times. Orchid Health is the latest genetic testing company that is looking to get ahead of genetic testing competitors with its diagnostics offerings. Meanwhile, knowledgeable and credible experts question the appropriateness of this testing, given the genetic knowledge that exists today.
“This is a major advance in the amount of information parents can have,” Orchid’s founder and CEO Noor Siddiqui (above) told CNBC. “The way that you can use that information is really up to you, but it gives a lot more control and confidence into a process that, for all of history, has just been totally left to chance.” Should Orchid Health’s analysis prove useful, pediatricians could order further clinical laboratory prenatal testing to confirm and diagnose potential genetic diseases for parents. (Photo copyright: General Assembly.)
Orchid Receives World-class Support
Regardless of the pushback from some genetic researchers, Orchid has attracted several world-class geneticists and genetics investors to its board of advisors. They include:
Jacques Cohen, PhD, embryologist, co-founder and former director for genetics laboratory Reprogenetics LLC (now CooperGenomics).
Anne Wojcicki, co-founder and CEO of direct-to-consumer genetic testing company 23andMe.
and others.
The WGS test, according to Orchid, detects genetic errors in embryos that are linked to severe illnesses before a pregnancy even begins. And by sequencing 99% of an embryo’s DNA, the test can spot potential health risks that could affect a future baby.
According to its website, the PGT lab company uses the WGS data to identify both monogenic (single-gene) and polygenic (multiple-gene) diseases, including:
Orchid is not without its critics. Knowledgeable, credible experts have questioned the appropriateness of this type of genetic testing. They fear it could become a modern-day form of eugenics.
Andrew McQuillin, PhD, Professor of Molecular Psychiatry at University College London, has concerns about Orchid’s preimplantation genetic testing. He maintains that it is difficult to control how such data is used, and that even the most accurate sequencing techniques do not predict disease risk very well.
“[Polygenic risk scores are] useful in the research context, but at the individual level, they’re not actually terribly useful to predict who’s going to develop schizophrenia or not,” McQuillin told Science. “We can come up with guidance on how these things should be used. The difficulty is that official guidance like that doesn’t feature anywhere in the marketing from these companies.”
McQuillin also stated that researchers must have an extensive discussion regarding the implications of this type of embryo screening.
“We need to take a look at whether this is really something we should be doing. It’s the type of thing that, if it becomes widespread, in 40 years’ time, we will ask, ‘What on Earth have we done?’” McQuillin emphasized.
Redefining Reproduction
It takes about three weeks for couples to receive their report back from Orchid after completing the whole genome sequence of a preimplantation embryo. A board-certified genetic counselor then consults with the parents to help them understand the results.
Founder and CEO Noor Siddiqui hopes Orchid will be able to scale up its operations and introduce more automation to the testing process to the cost per embryo.
“We want to make this something that’s accessible to everyone,” she told CNBC.
“I think this has the potential to totally redefine reproduction,” she added. “I just think that’s really exciting to be able to make people more confident about one of the most important decisions of their life, and to give them a little bit more control.”
Clinical laboratories have long been involved in prenatal screening to gain insight into risk levels associated with certain genetic disorders. Even some of that testing comes with controversy and ambiguous findings. Whether Orchid Health’s PGT process delivers accurate, reliable diagnostic insights regarding preimplantation embryos remains to be seen.
One goal of these new functions is to streamline physician workflows. However, these new EHRs may interface differently with clinical laboratory information systems
Artificial intelligence (AI) developers are making great contributions in clinical laboratory, pathology, radiology, and other areas of healthcare. Now, Electronic Health Record (EHR) developers are looking into ways to incorporate a new type of AI—called “Generative AI”—into their EHR products to assist physicians with time-consuming and repetitive administrative tasks and help them focus on patient-centered care.
Generative AI uses complex algorithms and statistical models to learn patterns from collected data. It then generates new content, including text, images, and audio/video information.
According to the federal Government Accountability Office (GAO), generative AI “has potential applications across a wide range of fields, including education, government, medicine, and law” and that “a research hospital is piloting a generative AI program to create responses to patient questions and reduce the administrative workload of healthcare providers.”
Reducing the workload on doctors and other medical personnel is a key goal of the EHR developers.
Generative AI uses deep learning neural networks modeled after the human brain comprised of layers of connected nodes that process data. It employs two neural networks: a generator [generative network] which creates new content, and a discriminator [discriminative network] which evaluates the quality of that content.
The collected information is entered into the network where each individual node processes the data and passes it on to the next layer. The last layer in the process produces the final output.
Many EHR companies are working toward adding generative AI into their platforms, including:
As our sister publication The Dark Report points out in its December 26 “Top 10 Biggest Lab Stories for 2023,” almost every product or service presented to a clinical laboratory or pathology group will soon include an AI-powered solution.
“We believe that generative AI has the potential of being a personal assistant for every doctor, and that’s what we’re working on,” Girish Navani (above), co-founder and CEO of eClinicalWorks, told EHRIntelligence. “It could save hours. You capture the essence of the entire conversation without touching a keyboard. It is transformational in how it works and how well it presents the information back to the provider.” Clinical laboratory information systems may also benefit from connecting with generative AI-based EHRs. (Photo copyright: eClinicalWorks.)
Generative AI Can Help with Physician Burnout
One of the beneficial features of generative AI is that it has the ability to “listen” to a doctor’s conversation with a patient while recording it and then produce clinical notes. The physician can then review, edit, and approve those notes to enter into the patient’s EHR record, thus streamlining administrative workflows.
“The clinician or support team essentially has to take all of the data points that they’ve got in their head and turn that into a narrative human response,” Phil Lindemann, Vice President of Data and Analytics at Epic, told EHRIntelligence. “Generative AI can draft a response that the clinician can then review, make changes as necessary, and then send to the patient.”
By streamlining and reducing workloads, EHRs that incorporate generative AI may help reduce physician burnout, which has been increasing since the COVID-19 pandemic.
“Language models have a huge potential in impacting almost every workflow,” Girish Navani, co-founder and CEO of eClinicalWorks, told EHRIntelligence. “Whether it’s reading information and summarizing it or creating the right type of contextual response, language models can help reduce cognitive load.”
Generative AI can also translate information into many different languages.
“Health systems spend a lot of time trying to make patient education and different things available in certain languages, but they’ll never have every language possible,” Lindemann said. “This technology can take human language, translate it at any reading level in any language, and have it understandable.”
MEDITECH is working on a generative AI project to simplify clinical documentation with an emphasis on hospital discharge summaries that can be very laborious and time-consuming for clinicians.
“Providers are asked to go in and review previous notes and results and try to bring that all together,” Helen Waters, Executive Vice President and COO of MEDITECH, told EHRIntelligence. “Generative AI can help auto-populate the discharge note by bringing in the discrete information that would be most relevant to substantiate that narrative and enable time savings for those clinicians.”
Many Applications for Generative AI in Healthcare
According to technology consulting and solutions firm XenonStack, generative AI has many potential applications in healthcare including:
The technology is currently in its early stages and does present challenges, such as lack of interpretability, the need for large datasets and more transparency, and ethical concerns, all of which will need to be addressed.
“We see it as a translation tool,” Lindemann told EHRIntelligence. “It’s not a panacea, but there’s going to be really valuable use cases, and the sooner the community can agree on that, the more useful the technology’s going to be.”
Since generative AI can be used to automate manual work processes, clinical laboratories and anatomic pathology groups should be alert to opportunities to interface their LISs with referring physicians’ EHRs. Such interfaces may enable the use of the generative AI functions to automate manual processes in both the doctors’ offices and the labs.
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.
