"It is still possible that there are some other lineages that existed in East Asia during the time Denisovans are known to have existed," study co-author Qiaomei Fu, director of the Ancient DNA Laboratory at the Institute of Vertebrate Paleontology and Paleoanthropology, part of the Chinese Academy of Sciences, told Live Science in an email.

"We know from DNA that Denisovans were a very varied group genetically, which split early into different populations," Chris Stringer, a paleoanthropologist at the Natural History Museum in London who was not involved in the research, told Live Science in an email. "These could have developed a lot of morphological variation, which may well surprise us as the sample of fossils grows."

The researchers also produced a new reconstruction of what a Denisovan man might have looked like. A previous DNA study suggested that Denisovans likely had dark skin, brown hair and brown eyes. The new reconstruction shows the Denisovan looking directly at the viewer, with his low, wide braincase mostly obscured by dark hair flowing back from his massive brow ridge. There are clear similarities between the Denisovan man and Neanderthals as well as modern humans, but the Denisovan has a distinct visage all his own.

See how much you know about early humans with our human evolution quiz!

'> Our extinct cousins the Denisovans had a 'distinctive' appearance, new analysis reveals

An ultrasound revealed a calcified mass and an empty uterus. Calcification is a buildup of calcium that hardens tissue into a solid mass.

To get a better look at the mass, which occupied the woman's lower abdomen and pelvis, the doctors ordered an advanced CT scan to create a 3D model. The imaging revealed that the mass contained clearly identifiable fetal skeletal structures, including a skull, a spinal column, ribs, and arm and leg bones. The mass measured roughly 7 by 5 inches (18 by 13 centimeters).

The diagnosis: Doctors concluded that the woman had a lithopedion, or "stone baby." The term comes from the Greek words "lithos," meaning stone, and "paidion," meaning child.

In a typical pregnancy, an egg is fertilized in a fallopian tube — the structure between the ovaries and the womb — and then travels into the uterus, where it implants in the uterine lining. By contrast, lithopedions most often start as abdominal pregnancies,‬ a type of ectopic pregnancy in which the embryo instead implants on the lining of the abdominal cavity.

There, the embryo shares the space with organs like the stomach, kidneys and appendix. These misplaced pregnancies are almost never viable and are highly life-threatening for the mother. Because the placenta attaches directly to delicate abdominal organs and blood vessels, these carry a severe risk of catastrophic internal bleeding.

If the fetus dies at a developmental stage when it is too large for the mother's body to naturally break down and absorb, her immune system can sometimes treat the retained tissue as a foreign object. To protect the mother from inflammatory complications and infection, the body progressively deposits calcium over the fetal remains, creating a protective casing. In this case, the calcified fetus likely accounted for the woman's chronic abdominal pain and increasing sense of heaviness, her doctors concluded.

The treatment: The doctors performed abdominal surgery to completely remove the calcified fetal mass from the woman's pelvis.

The day after surgery, she developed a mild fever and nausea, but both resolved with treatment. She remained stable and improved during her hospital stay before being discharged in good condition.

Follow-up visits over the next six months showed she had healed well, with a complete resolution of her abdominal pain.

What makes this case unique: Only about 340 cases of lithopedion have been documented in the medical literature over the past 400 years, with the earliest documented in 1582.

This woman's case stands out because she was 42 years old, while many recent cases have been diagnosed in postmenopausal women. (Women typically experience menopause around 51 to 52 years old.) In these cases, the initial ectopic pregnancy occurred during the woman's childbearing years, but she unknowingly carried the calcified remains for decades before developing symptoms. For example, a 74-year old woman from India carried her "stone baby" for more than three decades. By contrast, the woman's recent case was caught after only three years.

The case also highlights the challenges of maternal healthcare in resource-limited settings such as Somaliland, where a lack of access to routine prenatal care and early pregnancy scans can lead to delayed diagnoses. That said, the successful use of advanced 3D imaging and complete surgical recovery demonstrate that excellent outcomes are possible even in these constrained environments, the doctors noted.

For more intriguing medical cases, check out our Diagnostic Dilemma archives.

This article is for informational purposes only and is not meant to offer medical advice.

Can you guess the diagnosis in these strange medical cases? Find out with our diagnostic dilemma quiz!

'> A woman carried a 'stone baby' in her abdomen for 3 years

In practice, the more likely outcome is darker: AI will not simply sweep away the bureaucratic machinery of universities; it will give that machinery a new regime of AI compliance — more policies, more forms, more trackers, more oversight roles and more work downloaded onto faculty who are already stretched thin.

The danger is not that AI will destroy universities but that universities will use AI to preserve the very processes that make them dysfunctional. If this happens, we might see faculty spending less time on research, less time on teaching, and more time on activities that do not directly contribute to the production of knowledge and education of students.

This is already evident in the existing assessment system in the U.K., but shades are creeping into systems elsewhere, providing a window into the future for U.S institutions. A single piece of student work may pass through a plagiarism-detection platform, a grading interface, a moderation spreadsheet for faculty to share feedback, a grade-transfer system, and an exam-board record. Each stage generates its own logins, deadlines, exception rules and audit trails. None of this is inherently malicious. Anonymous marking, moderation, appeals, accessibility and academic integrity are legitimate concerns. But when each concern is implemented as a separate system, the combined effect is administrative incoherence.

In essence, faculty become the human interface between incompatible platforms, spending their time reconciling grades and chasing missing evidence rather than teaching. While this example pertains to U.K. universities, the trend to expand the administrative staff extends to North America. AI simply introduces a new opportunity to increase administration.

AI could, in principle, solve this. It could integrate systems, automate routine reporting, draft feedback, identify inconsistencies and remove duplicate data entry. Yet the institutional incentive is rarely to remove work; it is to make work visible. A university that automates a broken workflow does not necessarily simplify it. It may simply produce faster reports about the broken workflow, more sophisticated dashboards and more elaborate evidence of compliance. The bureaucratic impulse survives by changing its vocabulary: risk management, assurance, transparency, governance, responsible innovation.

At least one possible outcome is that universities will create AI-use policies, require module-level AI statements, mandate disclosure forms, introduce approved-tool lists and establish review committees. Faculty will be asked to redesign assessments, monitor suspected misuse, document their decisions, complete training, and explain their approach to students. Much of this labor will be invisible. It will appear not as a new job title but as an additional expectation attached to teaching.

This creates a familiar pattern: responsibility without authority. Faculty are expected to make consequential judgments about academic integrity, fairness, privacy and assessment validity. But they do not control procurement, system design, institutional policy or disciplinary procedures. When something goes wrong, the academic is exposed; when the system works, the institution claims the credit.

That asymmetry is corrosive. It teaches committed staff that careful judgment is not valued as expertise but rather as a compliance risk to be managed, and the consequences will fall unevenly. Experienced faculty, after voicing their concern to no avail, may leave, retire early or disengage. Early-career academics, already precarious because they don't have tenure, will have the least power to resist. Students will receive less of the resource that matters most: sustained intellectual attention.

One possible outcome is that universities will encourage the development of faculty AI avatars and cut hours and employment. The institution will then justify hiring more ‪—‬ not fewer ‪—‬ compliance and "educational enhancement" staff to manage and maintain these AI avatars, negating the idea of efficiency and cost savings. In this scenario, bureaucracy will expand to manage the problems created by its own expansion while undercutting quality for the students who are paying high tuition fees. One might need to hire staff for oversight of these avatars, administrators and lawyers to manage licensing agreements, quality control staff in case things go wrong, and IT teams for integrating them into learning platforms and websites.

There is an alternative to the administrative tasks. AI should be governed by a simple institutional rule: no new compliance requirement without the removal of an equivalent existing burden. If a university requires assessment redesign to accommodate AI, or the introduction of new technology, it should provide protected time, technical support and a single integrated platform. If it expects faculty to manage AI use, it must give them clear institutional backing rather than leaving them to improvise. And critically, it should show how the technology makes work more efficient before rolling it out across the board.

But the deeper issue is not technology but institutional purpose. A university exists — or should exist — to cultivate understanding, judgment and intellectual independence. If its systems consume faculty time, fragment the student experience, and reward documentation over learning, then AI becomes another instrument of managerial self-preservation. The question is not whether universities can adopt AI. It is whether they can resist using AI to automate their own absurdities.

If they cannot, the outcome will be an educational system that looks progressively sophisticated while becoming increasingly hollow ‪—‬ full of monitoring, low on trust; rich in measuring, poor in teaching; obsessed with compliance, indifferent to the people who make education possible; overly procedural sans intellectual life. It will become an audited theater of learning not worth students' time.

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'> Universities should not be adopting AI to automate their own absurdities

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