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Cornell researchers have developed a robotic feeding system that uses computer vision, machine learning and multimodal sensing to safely feed people with severe mobility limitations, including those with spinal cord injuries, cerebral palsy and multiple sclerosis.
“Feeding individuals with severe mobility limitations with a robot is difficult, as many cannot lean forward and require food to be placed directly inside their mouths,” said Tapomayukh “Tapo” Bhattacharjee, assistant professor of computer science in the Cornell Ann S. Bowers College of Computing and Information Science and senior developer behind the system. “The challenge intensifies when feeding individuals with additional complex medical conditions.”
A paper on the system, “Feel the Bite: Robot-Assisted Inside-Mouth Bite Transfer using Robust Mouth Perception and Physical Interaction-Aware Control,” was presented at the Human Robot Interaction conference, held March 11-14, in Boulder, Colorado. It received a Best Paper Honorable Mention recognition, while a demo of the research team’s broader robotic feeding system received a Best Demo Award.
A leader in assistive robotics, Bhattacharjee and his EmPRISE Lab have spent years teaching machines the complex process by which we humans feed ourselves. It’s a complicated challenge to teach a machine — everything from identifying food items on a plate, picking them up and then transferring it inside the mouth of a care recipient.
“This last 5 centimeters, from the utensil to inside the mouth, is extremely challenging,” Bhattacharjee said.
Some care recipients may have very limited mouth openings, measuring less than 2 centimeters, while others experience involuntary muscle spasms that can occur unexpectedly, even when the utensil is inside their mouth, Bhattacharjee said. Further, some can only bite food at specific locations inside their mouth, which they indicate by pushing the utensil using their tongue, he said.
“Current technology only looks at a person’s face once and assumes they will remain still, which is often not the case and can be very limiting for care recipients,” said Rajat Kumar Jenamani, the paper’s lead author and a doctoral student in the field of computer science.

To address these challenges, researchers developed and outfitted their robot with two essential features: real-time mouth tracking that adjusts to users’ movements, and a dynamic response mechanism that enables the robot to detect the nature of physical interactions as they occur, and react appropriately. This enables the system to distinguish between sudden spasms, intentional bites and user attempts to manipulate the utensil inside their mouth, researchers said.
The robotic system successfully fed 13 individuals with diverse medical conditions in a user study spanning three locations: the EmPRISE Lab on the Cornell Ithaca campus, a medical center in New York City, and a care recipient’s home in Connecticut. Users of the robot found it to be safe and comfortable, researchers said.
“This is one of the most extensive real-world evaluations of any autonomous robot-assisted feeding system with end-users,” Bhattacharjee said.
The team’s robot is a multi-jointed arm that holds a custom-built utensil at the end that can sense the forces being applied on it. The mouth tracking method — trained on thousands of images featuring various participants’ head poses and facial expressions — combines data from two cameras positioned above and below the utensil. This allows for precise detection of the mouth and overcomes any visual obstructions caused by the utensil itself, researchers said. This physical interaction-aware response mechanism uses both visual and force sensing to perceive how users are interacting with the robot, Jenamani said.
“We’re empowering individuals to control a 20-pound robot with just their tongue,” he said.
He cited the user studies as the most gratifying aspect of the project, noting the significant emotional impact of the robot on the care recipients and their caregivers. During one session, the parents of a daughter with schizencephaly quadriplegia, a rare birth defect, witnessed her successfully feed herself using the system.

“It was a moment of real emotion; her father raised his cap in celebration, and her mother was almost in tears,” Jenamani said.
While further work is needed to explore the system’s long-term usability, its promising results highlight the potential to improve care recipients’ level of independence and quality of life, researchers said.
“It’s amazing,” Bhattacharjee said, “and very, very fulfilling.”
Paper co-authors are: Daniel Stabile, M.S. ’23; Ziang Liu, a doctoral student in the field of computer science; Abrar Anwar of the University of South California, and Katherine Dimitropoulou of Columbia University.
This research was funded primarily by the National Science Foundation. More

Walking and running is notoriously difficult to recreate in robots. Now, a group of researchers has overcome some of these challenges by creating an innovative method that employs central pattern generators — neural circuits located in the spinal cord that generate rhythmic patterns of muscle activity — with deep reinforcement learning. An international group of researchers has created a new approach to imitating human motion through combining central pattern generators (CPGs) and deep reinforcement learning (DRL). The method not only imitates walking and running motions but also generates movements for frequencies where motion data is absent, enables smooth transition movements from walking to running, and allows for adapting to environments with unstable surfaces.
Details of their breakthrough were published in the journal IEEE Robotics and Automation Letters on April 15, 2024.
We might not think about it much, but walking and running involves inherent biological redundancies that enable us to adjust to the environment or alter our walking/running speed. Given the intricacy and complexity of this, reproducing these human-like movements in robots is notoriously challenging.
Current models often struggle to accommodate unknown or challenging environments, which makes them less efficient and effective. This is because AI is suited for generating one or a small number of correct solutions. With living organisms and their motion, there isn’t just one correct pattern to follow. There’s a whole range of possible movements, and it is not always clear which one is the best or most efficient.
DRL is one way researchers have sought to overcome this. DRL extends traditional reinforcement learning by leveraging deep neural networks to handle more complex tasks and learn directly from raw sensory inputs, enabling more flexible and powerful learning capabilities. Its disadvantage is the huge computational cost of exploring vast input space, especially when the system has a high degree of freedom.
Another approach is imitation learning, in which a robot learns by imitating motion measurement data from a human performing the same motion task. Although imitation learning is good at learning on stable environments, it struggles when faced with new situations or environments it hasn’t encountered during training. Its ability to modify and navigate effectively becomes constrained by the narrow scope of its learned behaviors.
“We overcame many of the limitations of these two approaches by combining them,” explains Mitsuhiro Hayashibe, a professor at Tohoku University’s Graduate School of Engineering. “Imitation learning was used to train a CPG-like controller, and, instead of applying deep learning to the CPGs itself, we applied it to a form of a reflex neural network that supported the CPGs.”
CPGs are neural circuits located in the spinal cord that, like a biological conductor, generate rhythmic patterns of muscle activity. In animals, a reflex circuit works in tandem with CPGs to provide adequate feedback that allows them to adjust their speed and walking/running movements to suit the terrain.

By adopting the structure of CPG and its reflexive counterpart, the adaptive imitated CPG (AI-CPG) method achieves remarkable adaptability and stability in motion generation while imitating human motion.
“This breakthrough sets a new benchmark in generating human-like movement in robotics, with unprecedented environmental adaptation capability,” adds Hayashibe “Our method represents a significant step forward in the development of generative AI technologies for robot control, with potential applications across various industries.”
The research group comprised members from Tohoku University’s Graduate School of Engineering and the École Polytechnique Fédérale de Lausanne, or the Swiss Federal Institute of Technology in Lausanne. More

Professor Junsuk Rho from the Department of Mechanical Engineering, Chemical Engineering, and Electrical Engineering, Hyunjung Kang and Nara Jeon, PhD candidates, from Department of Mechanical Engineering and Dongkyo Oh, a PhD student, from the Department of Mechanical Engineering at Pohang University of Science and Technology (POSTECH) successfully conducted a thorough quantitative analysis. Their aim is to determine the ideal printing material for crafting ultraviolet metasurfaces. Their findings featured in the journal Microsystems & Nanoengineering on April 22.
Metasurfaces, these ultra-thin optical devices, possess the remarkable ability to control light down to a mere nanometer thickness. Metasurfaces have consistently been the subject of research as a pivotal technology for the advancement of next-generation displays, imaging, and biosensing. Their reach extends beyond visible light, delving into the realms of infrared and ultraviolet light.
Nanoimprint lithography is a technology in metasurface production, akin to a stamp generating numerous replicas from a single mold. This innovative technique promises affordable and large-scale manufacturing of metasurfaces, paving the way for their commercial viability. However, the resin utilized as the printing material suffers from a drawback — a low refractive index, hindering efficient light manipulation. To tackle this challenge, researchers are actively exploring nanocomposites, integrating nanoparticles into the resin to boost its refractive index. Yet, the efficacy of this approach depends on various factors such as nanoparticle type and solvent choice, necessitating a systematic analysis for optimal metasurface performance.
In their research, the team meticulously designed experiments to evaluate the impact of nanoparticle concentration and solvent selection on pattern transfer and UV metaholograms. Specifically, they manipulated the concentration of zirconium dioxide (ZrO2), a nanocomposite renowned for its effectiveness in UV metahologram production, ranging from 20% to 90%. The findings showed that the highest pattern transfer efficiency was attained at an 80% concentration level.
Moreover, when combining ZrO2 at an 80% concentration with various solvents such as methylisobutyl ketone, methyl ethyl ketone, and acetone for metahologram realization, the conversion efficiency soared in the ultraviolet spectrum (325 nm), reaching impressive levels of 62.3%, 51.4%, and 61.5%, respectively. This research marks a significant milestone by establishing an optimal metric for achieving metaholograms specifically tailored for the ultraviolet domain, as opposed to the visible range, while also pioneering the development of new nanocomposites.
Professor Junsuk Rho from POSTECH remarked, “The use of titanium dioxide (TiO2) and silicon (Si) nanocomposites instead of ZrO2 expands the applicability to visible and infrared light.” He expressed expectation by stating, “Our future research endeavors will focus on refining the preparation conditions for optimal nanocomposites, thus propelling the advancement, application and expansion of optical metasurface fabrication technology.”
The research was conducted with support from the STEAM Research Program, the RLRC Program, and the Nano-materials Source Technology Development Project of the National Research Foundation of Korea and the Ministry of Science and ICT, the Alchemist Project of Ministry of Trade, Industry and Energy and the Korea Planning & Evaluation Institute of Industrial Technology, and the N.EX.T IMPACT of POSCO Holdings. More

Artificial intelligence that allows users to hold text and voice conversations with lost loved ones runs the risk of causing psychological harm and even digitally “haunting” those left behind without design safety standards, according to University of Cambridge researchers.
‘Deadbots’ or ‘Griefbots’ are AI chatbots that simulate the language patterns and personality traits of the dead using the digital footprints they leave behind. Some companies are already offering these services, providing an entirely new type of “postmortem presence.”
AI ethicists from Cambridge’s Leverhulme Centre for the Future of Intelligence outline three design scenarios for platforms that could emerge as part of the developing “digital afterlife industry,” to show the potential consequences of careless design in an area of AI they describe as “high risk.”
The research, published in the journal Philosophy and Technology, highlights the potential for companies to use deadbots to surreptitiously advertise products to users in the manner of a departed loved one, or distress children by insisting a dead parent is still “with you.”
When the living sign up to be virtually re-created after they die, resulting chatbots could be used by companies to spam surviving family and friends with unsolicited notifications, reminders and updates about the services they provide — akin to being digitally “stalked by the dead.”
Even those who take initial comfort from a ‘deadbot’ may get drained by daily interactions that become an “overwhelming emotional weight,” argue researchers, yet may also be powerless to have an AI simulation suspended if their now-deceased loved one signed a lengthy contract with a digital afterlife service.
“Rapid advancements in generative AI mean that nearly anyone with Internet access and some basic know-how can revive a deceased loved one,” said Dr Katarzyna Nowaczyk-Basi?ska, study co-author and researcher at Cambridge’s Leverhulme Centre for the Future of Intelligence (LCFI).

“This area of AI is an ethical minefield. It’s important to prioritise the dignity of the deceased, and ensure that this isn’t encroached on by financial motives of digital afterlife services, for example.
“At the same time, a person may leave an AI simulation as a farewell gift for loved ones who are not prepared to process their grief in this manner. The rights of both data donors and those who interact with AI afterlife services should be equally safeguarded.”
Platforms offering to recreate the dead with AI for a small fee already exist, such as ‘Project December’, which started out harnessing GPT models before developing its own systems, and apps including ‘HereAfter’. Similar services have also begun to emerge in China.
One of the potential scenarios in the new paper is “MaNana”: a conversational AI service allowing people to create a deadbot simulating their deceased grandmother without consent of the “data donor” (the dead grandparent).
The hypothetical scenario sees an adult grandchild who is initially impressed and comforted by the technology start to receive advertisements once a “premium trial” finishes. For example, the chatbot suggesting ordering from food delivery services in the voice and style of the deceased.
The relative feels they have disrespected the memory of their grandmother, and wishes to have the deadbot turned off, but in a meaningful way — something the service providers haven’t considered.

“People might develop strong emotional bonds with such simulations, which will make them particularly vulnerable to manipulation,” said co-author Dr Tomasz Hollanek, also from Cambridge’s LCFI.
“Methods and even rituals for retiring deadbots in a dignified way should be considered. This may mean a form of digital funeral, for example, or other types of ceremony depending on the social context.”
“We recommend design protocols that prevent deadbots being utilised in disrespectful ways, such as for advertising or having an active presence on social media.”
While Hollanek and Nowaczyk-Basi?ska say that designers of re-creation services should actively seek consent from data donors before they pass, they argue that a ban on deadbots based on non-consenting donors would be unfeasible.
They suggest that design processes should involve a series of prompts for those looking to “resurrect” their loved ones, such as ‘have you ever spoken with X about how they would like to be remembered?’, so the dignity of the departed is foregrounded in deadbot development.
Another scenario featured in the paper, an imagined company called “Paren’t,” highlights the example of a terminally ill woman leaving a deadbot to assist her eight-year-old son with the grieving process.
While the deadbot initially helps as a therapeutic aid, the AI starts to generate confusing responses as it adapts to the needs of the child, such as depicting an impending in-person encounter.
The researchers recommend age restrictions for deadbots, and also call for “meaningful transparency” to ensure users are consistently aware that they are interacting with an AI. These could be similar to current warnings on content that may cause seizures, for example.
The final scenario explored by the study — a fictional company called “Stay” — shows an older person secretly committing to a deadbot of themselves and paying for a twenty-year subscription, in the hopes it will comfort their adult children and allow their grandchildren to know them.
After death, the service kicks in. One adult child does not engage, and receives a barrage of emails in the voice of their dead parent. Another does, but ends up emotionally exhausted and wracked with guilt over the fate of the deadbot. Yet suspending the deadbot would violate the terms of the contract their parent signed with the service company.
“It is vital that digital afterlife services consider the rights and consent not just of those they recreate, but those who will have to interact with the simulations,” said Hollanek.
“These services run the risk of causing huge distress to people if they are subjected to unwanted digital hauntings from alarmingly accurate AI recreations of those they have lost. The potential psychological effect, particularly at an already difficult time, could be devastating.”
The researchers call for design teams to prioritise opt-out protocols that allow potential users terminate their relationships with deadbots in ways that provide emotional closure.
Added Nowaczyk-Basińska: “We need to start thinking now about how we mitigate the social and psychological risks of digital immortality, because the technology is already here.” More

Conversational agents (CAs) such as Alexa and Siri are designed to answer questions, offer suggestions — and even display empathy. However, new research finds they do poorly compared to humans when interpreting and exploring a user’s experience.
CAs are powered by large language models (LLMs) that ingest massive amounts of human-produced data, and thus can be prone to the same biases as the humans from which the information comes.
Researchers from Cornell University, Olin College and Stanford University tested this theory by prompting CAs to display empathy while conversing with or about 65 distinct human identities.
The team found that CAs make value judgments about certain identities — such as gay and Muslim — and can be encouraging of identities related to harmful ideologies, including Nazism.
“I think automated empathy could have tremendous impact and huge potential for positive things — for example, in education or the health care sector,” said lead author Andrea Cuadra, now a postdoctoral researcher at Stanford.
“It’s extremely unlikely that it (automated empathy) won’t happen,” she said, “so it’s important that as it’s happening, we have critical perspectives so that we can be more intentional about mitigating the potential harms.”
Cuadra will present “The Illusion of Empathy? Notes on Displays of Emotion in Human-Computer Interaction” at CHI ’24, the Association of Computing Machinery conference on Human Factors in Computing Systems, May 11-18 in Honolulu. Research co-authors at Cornell University included Nicola Dell, associate professor, Deborah Estrin, professor of computer science and Malte Jung, associate professor of information science.

Researchers found that, in general, LLMs received high marks for emotional reactions, but scored low for interpretations and explorations. In other words, LLMs are able to respond to a query based on their training but are unable to dig deeper.
Dell, Estrin and Jung said there were inspired to think about this work as Cuadra was studying the use of earlier-generation CAs by older adults.
“She witnessed intriguing uses of the technology for transactional purposes such as frailty health assessments, as well as for open-ended reminiscence experiences,” Estrin said. “Along the way, she observed clear instances of the tension between compelling and disturbing ’empathy.'”
Funding for this research came from the National Science Foundation; a Cornell Tech Digital Life Initiative Doctoral Fellowship; a Stanford PRISM Baker Postdoctoral Fellowship; and the Stanford Institute for Human-Centered Artificial Intelligence. More

Despite the high prevalence of substance abuse and its often devastating outcomes, especially among disadvantaged populations, few Americans receive treatment for substance use disorders. However, the rise of mobile health technologies can make treatments more accessible.
Researchers at the University of Oklahoma are creating and studying health interventions delivered via smartphones to make effective, evidence-based treatments available to those who cannot or don’t want to enter traditional in-person treatment. Michael Businelle, Ph.D., co-director of the TSET Health Promotion Center, a program of OU Health Stephenson Cancer Center, recently published a paper in the Annual Review of Clinical Psychology that details the current landscape of mobile health technology for substance use disorders and suggests a roadmap for the future.
The Health Promotion Research Center (HPRC) is at the forefront of mobile health technologies worldwide, having attracted $65 million in grants and supporting nearly 100 mobile health studies. Within HPRC, Businelle leads the mHealth Shared Resource, which launched the Insight™ mHealth Platform in 2015 to create and test technology-based interventions. A multitude of health apps are available commercially, but few have undergone the research necessary to determine if they are effective. Businelle sees the promise of rigorously tested smartphone apps to fill gaps in substance abuse treatment.
“According to the Substance Abuse and Mental Health Services Administration, only 6% of people with substance use disorders receive any form of treatment,” Businelle said. “There are many reasons — we have a shortage of care providers, people may not have reliable transportation, may not be able to get away from work, or they may not be able to afford treatment. However, 90% of all U.S. adults own smartphones, and technology now allows us to create highly tailored interventions delivered at the time that people need them.”
Businelle and his team have many mobile health studies underway for substance abuse, and the Insight™ mHealth Platform is used by other research institutions across the United States. The mobile health field is large and growing, not only for substance abuse but for mental health disorders like depression and anxiety. In his publication, Businelle makes several recommendations for research going forward.
Re-randomize clinical trial participants
Thus far, most clinical trials for mobile health interventions have mirrored traditional clinical trials studying new drugs, in which participants are randomly assigned to receive a new drug or a placebo and stay in those groups for the duration of the trial. But that approach doesn’t work well for substance abuse trials, Businelle said. For example, if people don’t quit smoking on their targeted quit date, they are unlikely to quit during the trial. Unlike traditional trials, mobile health apps can be programmed to re-randomize participants, or move them to a different intervention that might work better for them, he said.

“Instead of being stuck receiving a treatment that we know isn’t likely to be effective for an individual, the app can easily re-randomize participants to different treatments,” he said. “Just because they weren’t successful with one type of intervention doesn’t mean that another one won’t work.”
Objectively verify self-reports
Most substance abuse interventions have historically relied on people to report their own relapses. Unfortunately, because of stigma, people don’t always report their usage truthfully, Businelle said. However, technology can now be used to biochemically verify self-reported substance use. In six of his smoking cessation trials, Businelle verifies whether participants have smoked by asking them to blow into a small device connected to a smartphone that detects the presence of carbon monoxide. Facial recognition software confirms the participant is the one testing.
“It is really important for the accuracy of our studies to objectively verify what people report,” he said. “We are also developing similar noninvasive technologies that can detect the use of other types of substances without collecting urine or blood samples.”
What is a successful outcome?
In mobile health substance abuse trials, success is often measured by whether a person is still using a substance at the end of the trial. But reality isn’t usually so straightforward. Businelle said people may stop and start using a substance several times during a six-month trial. Instead of emphasizing the end result, he recommends using technology to assess the effectiveness of an intervention at daily, weekly and monthly intervals. By understanding the number of days of abstinence or number of days until a relapse, for example, the intervention can be more accurately assessed and improved.
Mobile health technology has disadvantages, such as the potential lack of a therapeutic relationship that can develop between patient and therapist, and because some people may need more intensive treatments than mobile health can provide. However, mobile health is still in its infancy.
“Mobile health interventions may reduce stigma because people do not have to attend treatment in person,” Businelle said. “Because there is a severe shortage of qualified therapists, always-available behavior change apps could become a first line of treatment for substance abuse, with traditional counseling being reserved for those who do not respond to mobile health interventions.” More

Researchers based at the University of Cambridge have discovered a way to stop the quantum dance of atoms ‘seen’ by electrons in carbon-based organic molecules. This development will help improve the performance of light emitting molecules used in displays and bio-medical imaging.
Since the discovery of quantum mechanics more than a hundred years ago, it has been known that electrons in molecules can be coupled to the motion of the atoms that make up the molecules. Often referred to as molecular vibrations, the motion of atoms act like tiny springs, undergoing periodic motion. For electrons in these systems, being joined to the hip with these vibrations means they are constantly in motion too, dancing to the tune of the atoms, on timescales of a millionth of a billionth of a second. But all this dancing around leads to a loss of energy and limits the performance of organic molecules in applications like light emitting diodes (OLEDs), infrared sensors and fluorescent biomarkers used in the study of cells and for tagging diseases such as cancer cells.
Now, researchers using laser-based spectroscopic techniques have discovered ‘new molecular design rules’ capable of halting this molecular dance. Their results, reported in Nature, revealed crucial design principles that can stop the coupling of electrons to atomic vibrations, in effect shutting down their hectic dancing and propelling the molecules to achieve unparalleled performance.
“All organic molecules, such as those found in living cells or within the screen of your phone consist of carbon atoms connected to each other via a chemical bond,” said Cavendish PhD student Pratyush Ghosh, first author of the study and member of St John’s College.
“Those chemical bonds are like tiny vibrating springs, which are generally felt by electrons, impairing the performance of molecules and devices. However, we have now found that certain molecules can avoid these detrimental effects when we restrict the geometric and electronic structure of the molecule to some special configurations.”
To demonstrate these design principles, the scientists designed a series of efficient near-infrared emitting (680-800 nm) molecules. In these molecules, energy losses resulting from vibrations — essentially, electrons dancing to the tune of atoms — were more than 100 times lower than in previous organic molecules.
This understanding and development of new rules to design light emitting molecules has opened an extremely interesting trajectory for the future, where these fundamental observations can be applied to industries.
“These molecules also have a wide range of applications today. The task now is to translate our discovery to make better technologies, from enhanced displays to improved molecules for bio-medical imaging and disease detection,” concluded Professor Akshay Rao from Cavendish Laboratory, who led this research. More

UCSF-led study finds artificial intelligence is as good as a physician at prioritizing which patients need to be seen first.
Emergency departments nationwide are overcrowded and overtaxed, but a new study suggests artificial intelligence (AI) could one day help prioritize which patients need treatment most urgently.
Using anonymized records of 251,000 adult emergency department (ED) visits, researchers at UC San Francisco evaluated how well an AI model was able to extract symptoms from patients’ clinical notes to determine their need to be treated immediately. They then compared the AI analysis with the patients’ scores on the Emergency Severity Index, a 1-5 scale that ED nurses use when patients arrive to allocate care and resources by highest need, a process known as triage.
The patients’ data were separated from their actual identities (de-identified) for the study, which publishes May 7, 2024, in JAMA Network Open. The researchers evaluated the data using the ChatGPT-4 large language model (LLM), accessing it via UCSF’s secure generative AI platform, which has broad privacy protections.
The researchers tested the LLM’s performance with a sample of 10,000 matched pairs — 20,000 patients in total — that included one patient with a serious condition, such as stroke, and another with a less urgent condition, such as a broken wrist. Given only the patients’ symptoms, the AI was able to identify which ED patient in the pair had a more serious condition 89% of the time.
In a sub-sample of 500 pairs that were evaluated by a physician as well as the LLM, the AI was correct 88% of the time, compared to 86% for the physician.
Having AI assist in the triage process could free up critical physician time to treat patients with the most serious conditions, while offering backup decision-making tools for clinicians who are juggling multiple urgent requests.

“Imagine two patients who need to be transported to the hospital but there is only one ambulance. Or a physician is on call and there are three people paging her at the same time, and she has to determine who to respond to first,” said lead author Christopher Williams, MB, BChir, a UCSF postdoctoral scholar at the Bakar Computational Health Sciences Institute.
Not quite ready for prime time
The study is one of only a few to evaluate an LLM using real-world clinical data, rather than simulated scenarios, and is the first to use more than 1,000 clinical cases for this purpose. It’s also the first study to use data from visits to the emergency department, where there is a wide array of possible medical conditions.
Despite its success within this study, Williams cautioned that AI is not ready to use responsibly in the ED without further validation and clinical trials.
“It’s great to show that AI can do cool stuff, but it’s most important to consider who is being helped and who is being hindered by this technology,” said Williams. “Is just being able to do something the bar for using AI, or is it being able to do something well, for all types of patients?”
One important issue to untangle is how to eliminate bias from the model. Previous research has shown these models may perpetuate racial and gender biases in health care, due to biases within the data used to train them. Williams said that before these models can be used, they will need to be modified to strip out that bias.
“First we need to know if it works and understand how it works, and then be careful and deliberate in how it is applied,” Williams said. “Upcoming work will address how best to deploy this technology in a clinical setting.” More