A rendering of emerging medical technology.
Ethics needs context. So does science – specifically, science that aims to create bioengineered models of early human embryo development in a dish (hereafter synthetic embryos). Even the term "synthetic embryos" begs for an explanation. What are these? And why would anyone want to create them?
"This knowledge may help scientists understand how certain birth defects are formed and why miscarriages often occur."
First the research context. Synthetic embryos are stem cell-derived simulations of human post-implantation embryos that are designed to mimic a stage of early development called gastrulation. That's the stage—around 14-15 days after fertilization – when embryos begin to form a very primitive body plan (basic dorsal-ventral and anterior-posterior axes, and distinct cell lineages). Researchers are starting to create synthetic embryos in the lab – albeit imperfect and incomplete versions – to learn how gastrulation might unfold in real human embryos embedded unseen in the womb. This knowledge may help scientists understand how certain birth defects are formed and why miscarriages often occur soon after implantation. As such, synthetic embryos are meant to be models of human embryo development, not themselves actually embryos. But will synthetic embryos ever get to the point where they are practically the same thing as "natural" human embryos? That is my concern and why I think researchers should avoid creating synthetic embryos capable of doing everything natural embryos can do.
It may not be too difficult to prevent this slide from synthetic to real. Synthetic embryos must be created using sophisticated 3D culture systems that mimic the complex architecture of human embryos. These complex culture systems also have to incorporate precise microinjection systems to chemically trigger the symmetry-breaking events involved in early body plan formation. In short, synthetic embryos need a heavy dose of engineering to get their biological processes going and to help keep them going. And like most engineered entities, designs can be built into the system early to serve well-considered goals – in our case, the goal of not wanting to create synthetic embryos that are too realistic.
"If one wants to study how car engines work, one can model an engine without also modeling the wheels, transmission, and every other car part together."
A good example of this point is found a report published in Nature Communications where scientists created a human stem cell-based 3D model that faithfully recapitulates the biological events around post-implantation amniotic sac development. Importantly, however, the embryo model they developed lacked several key structures and therefore – despite its partial resemblance to an early human embryo – did not have complete human form and potential. While fulfilling their model's aim of revealing a previously inaccessible early developmental event, the team intentionally did not recreate the entire post-implantation human embryo because they did not want to provoke any ethical concerns, as the lead author told me personally. Besides, creating a complete synthetic embryo was not necessary or scientifically justified for the research question they were pursuing. This example goes to show that researchers can create a synthetic embryo to model specific developmental events they want to study without modeling every aspect of a developing embryo. Likewise – to use a somewhat imprecise but instructive analogy – if one wants to study how car engines work, one can model an engine without also modeling the wheels, transmission, and every other car part together.
A representative "synthetic embryo," which in some ways resembles a post-implantation embryo around 14 days after fertilization.
(Courtesy of Yue Shao)
But why should researchers resist creating complete synthetic embryos? To answer this, we need some policy context. Currently there is an embryo research rule in place – a law in many nations, in others a culturally accepted agreement – that intact human embryos must not be grown for research in the lab for longer than 14 consecutive days after fertilization or the formation of the primitive streak (a faint embryonic band that signals the start of gastrulation). This is commonly referred to as the 14-day rule. It was established in the UK decades ago to carve out a space for meritorious human embryo research while simultaneously assuring the public that researchers won't go too far in cultivating embryos to later developmental stages before destroying them at the end of their studies. Many citizens accepting of pre-implantation stage human embryo research would not have tolerated post-implantation stage embryo use. The 14-day rule was a line in the sand, drawn to protect the advancement of embryo research, which otherwise might have been stifled without this clear stopping point. To date, the 14-day rule has not been revoked anywhere in the world, although new research in extended natural embryo cultivation is starting to put some pressure on it.
"Perhaps the day will come when scientists don't have to apply for research funding under such a dark cloud of anti-science sentiment."
Why does this policy context matter? The creation of complete synthetic embryos could raise serious questions (some of them legal) about whether the 14-day rule applies to these lab entities. Although they can be constructed in far fewer than 14 days, they would, at least in theory, be capable of recapitulating all of a natural embryo's developmental events at the gastrulation stage, thus possibly violating the spirit of the 14-day rule. Embryo research laws and policies worldwide are not ready yet to tackle this issue. Furthermore, professional guidelines issued by the International Society for Stem Cell Research prohibit the culture of any "organized embryo-like cellular structures with human organismal potential" to be cultured past the formation of the primitive streak. Thus, researchers should wait until there is greater clarity on this point, or until the 14-day rule is revised through proper policy-making channels to explicitly exclude complete synthetic embryos from its reach.
I should be clear that I am not basing my recommendations on any anti-embryo-research position per se, or on any metaphysical position regarding the positive moral status of synthetic embryos. Rather, I am concerned about the potential backlash that research on complete synthetic embryos might bring to embryo research in general. I began this essay by saying that ethics needs context. The ethics of synthetic embryo research needs to be considered within the context of today's fraught political environment. Perhaps the day will come when scientists don't have to apply for research funding under such a dark cloud of anti-science sentiment. Until then, however, it is my hope that scientists can fulfill their research aims by working on an array of different but each purposefully incomplete synthetic embryo models to generate, in the aggregate of their published work, a unified portrait of human development such that biologically complete synthetic embryo models will not be necessary.
Editor's Note: Read a different viewpoint here written by a leading New York fertility doctor/researcher.
A recent study in The Lancet Oncology showed that AI found 20 percent more cancers on mammogram screens than radiologists alone.
Published in The Lancet Oncology, the study analyzed the scans of 80,000 Swedish women with a moderate hereditary risk of breast cancer who had undergone a mammogram between April 2021 and July 2022. Half of these scans were read by AI and then a radiologist to double-check the findings. The second group of scans was read by two researchers without the help of AI. (Currently, the standard of care across Europe is to have two radiologists analyze a scan before diagnosing a patient with breast cancer.)
The study showed that the AI group detected cancer in 6 out of every 1,000 scans, while the radiologists detected cancer in 5 per 1,000 scans. In other words, AI found 20 percent more cancers than the highly-trained radiologists.
Scientists have been using MRI images (like the ones pictured here) to train artificial intelligence to detect cancers earlier and with more accuracy. Here, MIT's AI system, MIRAI, looks for patterns in a patient's mammograms to detect breast cancer earlier than ever before. news.mit.edu
But even though the AI was better able to pinpoint cancer on an image, it doesn’t mean radiologists will soon be out of a job. Dr. Laura Heacock, a breast radiologist at NYU, said in an interview with CNN that radiologists do much more than simply screening mammograms, and that even well-trained technology can make errors. “These tools work best when paired with highly-trained radiologists who make the final call on your mammogram. Think of it as a tool like a stethoscope for a cardiologist.”
AI is still an emerging technology, but more and more doctors are using them to detect different cancers. For example, researchers at MIT have developed a program called MIRAI, which looks at patterns in patient mammograms across a series of scans and uses an algorithm to model a patient's risk of developing breast cancer over time. The program was "trained" with more than 200,000 breast imaging scans from Massachusetts General Hospital and has been tested on over 100,000 women in different hospitals across the world. According to MIT, MIRAI "has been shown to be more accurate in predicting the risk for developing breast cancer in the short term (over a 3-year period) compared to traditional tools." It has also been able to detect breast cancer up to five years before a patient receives a diagnosis.
The challenges for cancer-detecting AI tools now is not just accuracy. AI tools are also being challenged to perform consistently well across different ages, races, and breast density profiles, particularly given the increased risks that different women face. For example, Black women are 42 percent more likely than white women to die from breast cancer, despite having nearly the same rates of breast cancer as white women. Recently, an FDA-approved AI device for screening breast cancer has come under fire for wrongly detecting cancer in Black patients significantly more often than white patients.
As AI technology improves, radiologists will be able to accurately scan a more diverse set of patients at a larger volume than ever before, potentially saving more lives than ever.