Researchers at Tufts University and Harvard University’s Wyss Institute have created tiny biological robots that they call Anthrobots from human tracheal cells that can move across a surface and have been found to encourage the growth of neurons across a region of damage in a lab dish.

The multicellular robots, ranging in size from the width of a human hair to the point of a sharpened pencil, were made to self-assemble and shown to have a remarkable healing effect on other cells. The discovery is a starting point for the researchers’ vision to use patient-derived biobots as new therapeutic tools for regeneration, healing, and treatment of disease.

The work follows from earlier research in the laboratories of Michael Levin, Vannevar Bush Professor of Biology at Tufts University School of Arts & Sciences, and Josh Bongard at the University of Vermont in which they created multicellular biological robots from frog embryo cells called Xenobots, capable of navigating passageways, collecting material, recording information, healing themselves from injury, and even replicating for a few cycles on their own.

Additionally, GAI helps radiologists cross-reference comorbidities in a way that was not possible before. For instance, people with certain types of autoimmune arthritis have an increased risk of cardiovascular disease (atherosclerosis, hypertension, and type 2 diabetes). These conditions might seem unrelated, but if a CT scan reveals calcifications in the coronary arteries, GAI can facilitate informing the radiologist and treating physician of this important biomarker. These types of added value are not just consumer conveniences. As potentiators of clinical research and effectuators of episodes of care, they can save the lives of patients.

Leaning into the whole.

It should be clear to most in the industry that AI is knocking at the door, and those who do not adopt new technology will be left behind. What seems less clear is how that design and implementation should move forward. Laying AI functions on top of already outdated systems or relying on separate solutions that do not play into the unified stack system–especially given the volume of data, delicate privacy issues and the need for constant updates–does not optimally contribute to advancement. Instead, we should embrace the vision as a whole and build for unification and GAI, rather than jury-rig a square peg in a round hole.