The informed consent process in biomedical research is biased towards people who can meet with clinical study staff during the working day. For those who have the availability to have a consent conversation, the time burden can be off-putting. Professor Eric Vilain, from the Department of Paediatrics, University of California, Irvine, USA, will tell the European Society of Human Genetics annual conference today (Tuesday 13 June) how results from his team's study of the use of a chatbot (GIA -- 'Genetics Information Assistant' developed by Invitae Corporation) in the consent process show that it encourages inclusivity, and leads to faster completion and high levels of understanding. Since such consent is the cornerstone of all research studies, finding ways of cutting the time spent on it while continuing to make sure that participants' understanding is not lessened is something clinicians have aimed for some time.

Working with their institutional review board (IRB), Prof Vilain's team from across University of California Irvine, Children's National Hospital, and Invitae Corporation designed a script for the GIA chatbot to transform the trial consent form and protocol into a logic flow and script. Unlike conventional methods of obtaining consent, the bot was able to quiz participants to assess the knowledge they had attained. It could also be accessed at any time, allowing individuals with less free time to use it outside normal business hours. "We saw that more than half of our participants interacted with the bot at these times, and this shows its utility in decreasing the barriers to entry to research. Currently, most people who participate in biomedical research have time to do so as well as the knowledge that studies exist," says Prof Vilain

The researchers involved 72 families in the consent process during a six-month time period as part of the US national GREGoR consortium, a National Institutes of Health initiative to advance rare disease research. A total of 37 families completed consent using the traditional process, while 35 used the chatbot. The researchers found that the median length of the consent conversation was shorter for those using the bot, at 44 rather than 76 minutes, and the time from referral to the study to consent completion was also faster, at five as opposed to 16 days. The level of understanding of those who had used the bot was assessed with a 10-question quiz that 96% of participants passed, and a request for feedback showed that 86% thought that they had had a positive experience.

"I was surprised and pleased that a significant number of people would feel comfortable communicating with a chatbot," says Prof Vilain. "But we worked hard with our IRB to ensure that it didn't 'hallucinate' (make mistakes) and to ensure that knowledge was conveyed correctly. When the bot was unable to answer a question, it encouraged the participant to speak with a member of the study team."

While it is not possible to give an accurate account of cost saving, the time savings of staff were substantial, the researchers say. Because people can pause the chatbot consent process at any time, it can be completed much more quickly -- for example, four participants completed in 24 hours. Of the consent conversations that were quick (less than an hour), 83% of them were with the chatbot. The consent conversations that were longer (between one and two hours), were with a study staff member (66%).

"But it's far from being just about speed," says Prof Vilain. "The traditional method of consenting does not have a mechanism to verify understanding objectively. It is based on the conviction of the study staff member hosting the conversation that the consent has been informed properly and the individual understands what they are consenting to. The chat-based method can test comprehension more objectively. It does not allow users who do not show understanding to give consent, and puts them in touch with a genetic counsellor to figure out why knowledge transmission did not occur.

"We believe that our work has made an important contribution to the obtention of properly-informed consent, and would now like to see it used in different languages to reach global populations," he concludes.

Professor Alexandre Reymond, chair of the conference, said: "The keystone to informed consent should be that it is by definition 'informed', and we should explore all possibilities to ensure this in the future."

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The recent progress in the field of robotics and artificial intelligence (AI) promises a future where these technologies would play a more prominent role in society. Current developments, such as the introduction of autonomous vehicles, the ability to generate original artwork, and the creation of chatbots capable of engaging in human-like conversations, highlight the immense possibilities held by these technologies. While these advancements offer numerous benefits, they also pose some fundamental questions. The characteristics such as creativity, communication, critical thinking, and learning -- once considered to be unique to humans -- are now being replicated by AI. So, can intelligent machines be considered 'human'?

In a step toward answering this question, Associate Professor Tomohide Ibuki from Tokyo University of Science, in collaboration with medical ethics researcher Dr. Eisuke Nakazawa from The University of Tokyo and nursing researcher Dr. Ai Ibuki from Kyoritsu Women's University, recently explored whether robots and AI can be entrusted with nursing, a highly humane practice. Their work was made available online on 12 June 2023 and published in the journal Nursing Ethics on 12 June 2023.

"This study in applied ethics examines whether robotics, human engineering, and human intelligence technologies can and should replace humans in nursing tasks," says Dr. Ibuki.

Nurses demonstrate empathy and establish meaningful connections with their patients. This human touch is essential in fostering a sense of understanding, trust, and emotional support. The researchers examined whether the current advancements in robotics and AI can implement these human qualities by replicating the ethical concepts attributed to human nurses, including advocacy, accountability, cooperation, and caring.

Advocacy in nursing involves speaking on behalf of patients to ensure that they receive the best possible medical care. This encompasses safeguarding patients from medical errors, providing treatment information, acknowledging the preferences of a patient, and acting as mediators between the hospital and the patient. In this regard, the researchers noted that while AI can inform patients about medical errors and present treatment options, they questioned its ability to truly understand and empathize with patients' values and to effectively navigate human relationships as mediators.

The researchers also expressed concerns about holding robots accountable for their actions. They suggested the development of explainable AI, which would provide insights into the decision-making process of AI systems, improving accountability.

The study further highlights that nurses are required to collaborate effectively with their colleagues and other healthcare professionals to ensure the best possible care for patients. As humans rely on visual cues to build trust and establish relationships, unfamiliarity with robots might lead to suboptimal interactions. Recognizing this issue, the researchers emphasized the importance of conducting further investigations to determine the appropriate appearance of robots for facilitating efficient cooperation with human medical staff.

Lastly, while robots and AI have the potential to understand a patient's emotions and provide appropriate care, the patient must also be willing to accept robots as care providers.

Having considered the above four ethical concepts in nursing, the researchers acknowledge that while robots may not fully replace human nurses anytime soon, they do not dismiss the possibility. While robots and AI can potentially reduce the shortage of nurses and improve treatment outcomes for patients, their deployment requires careful weighing of the ethical implications and impact on nursing practice.

"While the present analysis does not preclude the possibility of implementing the ethical concepts of nursing in robots and AI in the future, it points out that there are several ethical questions. Further research could not only help solve them but also lead to new discoveries in ethics," concludes Dr. Ibuki.

Here's hoping for such novel applications of robotics and AI to emerge soon!

I think robots do to much, it’s seems like revolution 😅

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The hottest drink of the summer may be the SEAS-colada. Here's what you need to make it: gin, pineapple juice, coconut milk and a dielectric elastomer actuator-based soft peristaltic pump. Unfortunately, the last component can only be found in the lab of Robert Wood, the Harry Lewis and Marlyn McGrath Professor of Engineering and Applied Sciences at the Harvard John A. Paulson School of Engineering and Applied Sciences.

At least, for now.

Wood and his team designed the pump to solve a major challenge in soft robotics -- how to replace traditionally bulky and rigid power components with soft alternatives.

Over the past several years, Wood's Microrobotics Lab at SEAS has been developing soft analogues of traditionally rigid robotic components, including valves and sensors. In fluid-driven robotic systems, pumps control the pressure or flow of the liquid that powers the robot's movement. Most pumps available today for soft robotics are either too large and rigid to fit onboard, not powerful enough for actuation or only work with specific fluids.

Wood's team developed a compact, soft pump with adjustable pressure flow versatile enough to pump a variety of fluids with varying viscosity, including gin, juice, and coconut milk, and powerful enough to power soft haptic devices and a soft robotic finger.

The pump's size, power and versatility opens up a range of possibilities for soft robots in a variety of applications, including food handling, manufacturing, and biomedical therapeutics.

The research was published recently in Science Robotics.

Peristaltic pumps are widely used in industry. These simple machines use motors to compress a flexible tube, creating a pressure differential that forces liquid through the tube. These types of pumps are especially useful in biomedical applications because the fluid doesn't touch any component of the pump itself.

"Peristaltic pumps can deliver liquids with a wide range of viscosities, particle-liquid suspensions, or fluids such as blood, which are challenging for other types of pumps," said first author Siyi Xu, a former graduate student at SEAS and current postdoctoral fellow in Wood's lab.

Building off previous research, Xu and the team designed electrically powered dielectric elastomer actuators (DEAs) to act as the pump's motor and rollers. These soft actuators have ultra-high power density, are lightweight, and can run for hundreds of thousands of cycles.

The team designed an array of DEAs that coordinate with each other, compressing a millimeter-sized channel in a programmed sequence to produce pressure waves.

The result is a centimeter-sized pump small enough to fit on board a small soft robot and powerful enough to actuate movement, with controllable pressure, flow rate, and flow direction.

"We also demonstrated that we could actively tune the output from continuous flow to droplets by varying the input voltages and the outlet resistance, in our case the diameter of the blunt needle," said Xu. "This capability may allow the pump to be useful not only for robotics but also for microfluidic applications."

"The majority of soft robots contain rigid components somewhere along their drivetrain," said Wood. "This topic started as an effort to swap out one of those key pieces, the pump, with a soft alternative. But along the way we realized that compact soft pumps may have far greater utility, for example in biomedical settings for drug delivery or implantable therapeutic devices."

This incredible news?

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The new technology can fit 10 to 100 times more information on one device and process it in one place. The new memory processes data in a similar way as synapses in the human brain. A feature of the memory is resistance switching, which is capable of a continuous range of states, unlike traditional memory, which has only two states: one or zero.

Prototype device based on hafnium oxide, a material already used in the semiconductor industry. The technology was patented by the Cambridge Business Enterprise.

One potential solution to the problem of inefficient computer memory is a new type of technology known as resistive switching memory. Conventional memory devices are capable of two states: one or zero. A functioning resistive switching memory device however, would be capable of a continuous range of states – computer memory devices based on this principle would be capable of far greater density and speed.

“A typical USB stick based on continuous range would be able to hold between ten and 100 times more information, for example,” said Hellenbrand.

Hellenbrand and his colleagues developed a prototype device based on hafnium oxide, an insulating material that is already used in the semiconductor industry. The issue with using this material for resistive switching memory applications is known as the uniformity problem. At the atomic level, hafnium oxide has no structure, with the hafnium and oxygen atoms randomly mixed, making it challenging to use for memory applications.

However, the researchers found that by adding barium to thin films of hafnium oxide, some unusual structures started to form, perpendicular to the hafnium oxide plane, in the composite material.

These vertical barium-rich ‘bridges’ are highly structured, and allow electrons to pass through, while the surrounding hafnium oxide remains unstructured. At the point where these bridges meet the device contacts, an energy barrier was created, which electrons can cross. The researchers were able to control the height of this barrier, which in turn changes the electrical resistance of the composite material.

“This allows multiple states to exist in the material, unlike conventional memory which has only two states,” said Hellenbrand.

Unlike other composite materials, which require expensive high-temperature manufacturing methods, these hafnium oxide composites self-assemble at low temperatures. The composite material showed high levels of performance and uniformity, making them highly promising for next-generation memory applications.

A patent on the technology has been filed by Cambridge Enterprise, the University’s commercialisation arm.

“What’s really exciting about these materials is they can work like a synapse in the brain: they can store and process information in the same place, like our brains can, making them highly promising for the rapidly growing AI and machine learning fields,” said Hellenbrand.

The researchers are now working with industry to carry out larger feasibility studies on the materials, in order to understand more clearly how the high-performance structures form. Since hafnium oxide is a material already used in the semiconductor industry, the researchers say it would not be difficult to integrate into existing manufacturing processes.

The research was supported in part by the U.S. National Science Foundation and the Engineering and Physical Sciences Research Council (EPSRC), part of UK Research and Innovation (UKRI).

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Poems, essays and even books -- is there anything the open AI platform ChatGPT can't handle? These new AI developments have inspired researchers at TU Delft and the Swiss technical university EPFL to dig a little deeper: For instance, can ChatGPT also design a robot? And is this a good thing for the design process, or are there risks? The researchers published their findings in Nature Machine Intelligence.

What are the greatest future challenges for humanity? This was the first question that Cosimo Della Santina, assistant professor, and PhD student Francesco Stella, both from TU Delft, and Josie Hughes from EPFL, asked ChatGPT. "We wanted ChatGPT to design not just a robot, but one that is actually useful," says Della Santina. In the end, they chose food supply as their challenge, and as they chatted with ChatGPT, they came up with the idea of creating a tomato-harvesting robot.

Helpful suggestions

The researchers followed all of ChatGPT's design decisions. The input proved particularly valuable in the conceptual phase, according to Stella. "ChatGPT extends the designer's knowledge to other areas of expertise. For example, the chat robot taught us which crop would be most economically valuable to automate." But ChatGPT also came up with useful suggestions during the implementation phase: "Make the gripper out of silicone or rubber to avoid crushing tomatoes" and "a Dynamixel motor is the best way to drive the robot." The result of this partnership between humans and AI is a robotic arm that can harvest tomatoes.

ChatGPT as a researcher

The researchers found the collaborative design process to be positive and enriching. "However, we did find that our role as engineers shifted towards performing more technical tasks," says Stella. In Nature Machine Intelligence, the researchers explore the varying degrees of cooperation between humans and Large Language Models (LLM), of which ChatGPT is one. In the most extreme scenario, AI provides all the input to the robot design, and the human blindly follows it. In this case, the LLM acts as the researcher and engineer, while the human acts as the manager, in charge of specifying the design objectives.

Risk of misinformation

Such an extreme scenario is not yet possible with today's LLMs. And the question is whether it is desirable. "In fact, LLM output can be misleading if it is not verified or validated. AI bots are designed to generate the 'most probable' answer to a question, so there is a risk of misinformation and bias in the robotic field," Della Santina says. Working with LLMs also raises other important issues, such as plagiarism, traceability and intellectual property.

Della Santina, Stella and Hughes will continue to use the tomato-harvesting robot in their research on robotics. They are also continuing their study of LLMs to design new robots. Specifically, they are looking at the autonomy of AIs in designing their own bodies. "Ultimately an open question for the future of our field is how LLMs can be used to assist robot developers without limiting the creativity and innovation needed for robotics to rise to the challenges of the 21st century," Stella concludes.

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Researchers from the Department of Mechanical Science and Bioengineering at Osaka University have invented a new kind of walking robot that takes advantage of dynamic instability to navigate. By changing the flexibility of the couplings, the robot can be made to turn without the need for complex computational control systems. This work may assist the creation of rescue robots that are able to traverse uneven terrain.

Most animals on Earth have evolved a robust locomotion system using legs that provides them with a high degree of mobility over a wide range of environments. Somewhat disappointingly, engineers who have attempted to replicate this approach have often found that legged robots are surprisingly fragile. The breakdown of even one leg due to the repeated stress can severely limit the ability of these robots to function. In addition, controlling a large number of joints so the robot can transverse complex environments requires a lot of computer power. Improvements in this design would be extremely useful for building autonomous or semi-autonomous robots that could act as exploration or rescue vehicles and enter dangerous areas.

Now, investigators from Osaka University have developed a biomimetic "myriapod" robot that takes advantage of a natural instability that can convert straight walking into curved motion. In a study published recently in Soft Robotics, researchers from Osaka University describe their robot, which consists of six segments (with two legs connected to each segment) and flexible joints. Using an adjustable screw, the flexibility of the couplings can be modified with motors during the walking motion. The researchers showed that increasing the flexibility of the joints led to a situation called a "pitchfork bifurcation," in which straight walking becomes unstable. Instead, the robot transitions to walking in a curved pattern, either to the right or to the left. Normally, engineers would try to avoid creating instabilities. However, making controlled use of them can enable efficient maneuverability. "We were inspired by the ability of certain extremely agile insects that allows them to control the dynamic instability in their own motion to induce quick movement changes," says Shinya Aoi, an author of the study. Because this approach does not directly steer the movement of the body axis, but rather controls the flexibility, it can greatly reduce both the computational complexity as well as the energy requirements.

The team tested the robot's ability to reach specific locations and found that it could navigate by taking curved paths toward targets. "We can foresee applications in a wide variety of scenarios, such as search and rescue, working in hazardous environments or exploration on other planets," says Mau Adachi, another study author. Future versions may include additional segments and control mechanisms.

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Here, here are some fascinating facts about the human brain:

Your Brain Generates About 12-25 Watts of Electricity: This is enough power to light up a low-wattage LED light bulb.

The Human Brain is the Fattest Organ in the Body: Approximately 60% of it is made of fat.

Information Travels at Different Speeds Within Different Types of Neurons: It can be as slow as 0.5 meters/sec or as fast as 120 meters/sec.

The Adult Brain Weighs About 3 Pounds (1.4 kg): Despite making up just 2% of a person’s total body weight, the brain uses around 20% of the body’s energy.

There are around 86 Billion Neurons in a Human Brain: And each neuron can transmit about 1,000 nerve impulses per second and make as many as tens of thousands of synaptic contacts with other neurons.

Human Brains Have Amazing Plasticity: The brain has the ability to rewire itself and even create new neurons – a process known as neuroplasticity.

Your Brain Can’t Feel Pain: Even though the brain processes pain signals, the brain itself does not actually feel pain because it lacks pain receptors.

Brain Activity Creates Enough Heat to Boil Water: Your brain generates about as much heat as a small light bulb even when you’re sleeping.

Memory Capacity is Nearly Limitless:According to some estimations, our brains could potentially store around one petabyte (or a million gigabytes) worth of information in our lifetimes.

Although Smaller in Size, The Human Brain is More Efficient Than Most Powerful Computers: In terms of processing speed and energy consumption, our brains outperform the capabilities of modern supercomputers.

These discoveries emphasize how amazing and complex our brains are!

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