A.I.
Balancing AI and physics: toward a learnable climate model
INSTITUTE OF ATMOSPHERIC PHYSICS, CHINESE ACADEMY OF SCIENCES
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A SCHEMATIC OF PHYSICS-AI BALANCED CLIMATE MODEL
view moreCREDIT: YA WANG
Artificial intelligence (AI) is bringing notable changes to atmospheric science, particularly with the introduction of large AI weather models like Pangu Weather and GraphCast. However, alongside these advancements, questions have arisen about the alignment of these models with fundamental physics principles.
Previous studies have demonstrated that Pangu-Weather can accurately replicate certain climate patterns like tropical Gill responses and extra-tropical teleconnections through qualitative analysis. However, quantitative investigations have revealed significant differences in wind components, such as divergent winds and ageostrophic winds, within current AI weather models. Despite these findings, there are still concerns that the importance of physics in climate science is sometimes overlooked.
"The qualitative assessment finds AI models could understand and learn spatial patterns in weather and climate data. On the other hand, the quantitative approach highlights a limitation: current AI models struggle to learn certain wind patterns and instead rely solely on total wind speed," Explains Professor Gang Huang from the Institute of Atmospheric Physics (IAP) at the Chinese Academy of Sciences. "This underscores the need for comprehensive dynamic diagnostics of AI models. Only through a holistic analysis can we augment our understanding and impose necessary physical constraints."
Researchers, including collaborators from the IAP, Seoul National University, and Tongji University, advocate for a collaborative approach between AI and physics in climate modeling, moving beyond the notion of an 'either-or' scenario.
Professor Gang Huang emphasizes, "While AI excels in capturing spatial relationships within weather and climate data, it struggles with nuanced physical components like divergent winds and ageostrophic winds. This underscores the necessity for rigorous dynamic diagnostics to enforce physical constraints."
Published in Advances in Atmospheric Sciences, their perspectives paper illustrates methods to impose both soft and hard physical constraints on AI models, ensuring consistency with known atmospheric dynamics.
Moreover, the team advocates for a transition from offline to online parameterization schemes to achieve global optimality in model weights, thereby fostering fully coupled physics-AI balanced climate models. Dr. Ya Wang envisions, "This integration enables iterative optimization, transforming our models into truly learnable systems."
Recognizing the importance of community collaboration, the researchers promote a culture of openness, comparability, and reproducibility (OCR). By embracing principles akin to those in the AI and computer science communities, they believe in cultivating a culture conducive to the development of a truly learnable climate model.
In summary, by synthesizing AI's spatial prowess with physics' foundational principles and fostering a collaborative community, researchers aim to realize a climate model that seamlessly blends AI and physics, representing a significant step forward in climate science.
JOURNAL
Advances in Atmospheric Sciences
ARTICLE TITLE
Toward a Learnable Climate Model in the Artificial Intelligence Era
Study unveils balance of AI and preserving humanity in health care
Cross country healthcare and FAU launch future of nursing survey
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THE SURVEY WAS CONDUCTED WITH 1,127 NURSING PROFESSIONALS AND STUDENTS AT HEALTH CARE AND HOSPITAL FACILITIES.
view moreCREDIT: ALEX DOLCE, FLORIDA ATLANTIC UNIVERSITY
Cross Country Healthcare, Inc. (NASDAQ: CCRN), a pioneering force in tech-driven workforce solutions and advisory services, in collaboration with Florida Atlantic University's Christine E. Lynn College of Nursing, released its latest research findings in the fourth annual installment of the Future of Nursing Survey: “Embracing Technology While Preserving Humanity.” Drawing insights from more than 1,100 nursing professionals and students, the study illuminates the intricate interplay between cutting-edge health care technologies and the enduring essence of compassionate care.
Survey results reveal a nuanced perspective among nurses toward the integration of Artificial Intelligence, with more than half expressing reservations and 38% questioning its potential benefits for the nursing field. While a minority recognize AI's capacity to enhance efficiency, documentation, research, skill development and patient monitoring, concerns linger regarding its perceived lack of empathy, job displacement risks, data security, regulatory complexities, and the learning curve associated with new technology.
“As we navigate the future of nursing, our compass must be set on a dual course: embracing technology to propel us forward while steadfastly preserving the humanity at the core of our profession,” said John A. Martins, president and CEO of Cross Country. “This delicate balance is charting the course for the future of the health care industry.”
In addition to shedding light on nurses' mental well-being, with notable concerns about staffing shortages and burnout, the study uncovered several key insights:
- Despite the potential of telehealth services, 74% of nurses have never utilized them, citing doubts about their efficacy in delivering comprehensive patient care.
- A significant portion of both employed (29%) and student nurses (41%) contemplate retirement or transitioning out of the profession in the near future.
- An overwhelming 96% of nurses advocate for increased pay rates and incentives to attract and retain nursing talent.
“We are at the forefront of training future nurses to embrace the many opportunities that technology offers to improve patient outcomes and streamline time consuming day-to-day administrative tasks,” said Safiya George, Ph.D., the Holli Rockwell Trubinsky Eminent Dean and Professor, FAU Christine E. Lynn College of Nursing. “As AI rapidly evolves in the delivery of health care, nothing will replace the human touch, empathy and compassion that is at the core of the nursing profession. Ultimately, current and future nurses will find a synergistic balance between technology, innovation, patient trust and the human connection.”
Cross Country recommends four strategies for health care organizations to empower nurses in adopting AI:
1. Transparency: Ensure nurses understand AI's impact and benefits to their roles through transparent communication from leadership, building trust and reducing apprehension as well as case studies to show how the technology works to move the profession forward.
2. Training: Implement comprehensive training programs customized for nurses, demystifying AI and enhancing proficiency in AI-powered tools to foster confidence.
3. Communication: Customize communication strategies to resonate with different nurse personas, addressing their unique concerns and preferences to promote AI acceptance.
4. Feedback: Solicit and integrate nurses’ feedback into AI solutions, tailoring them to address specific challenges and improve the nursing experience.
“Ultimately, AI will not replace wisdom – intuition, empathy and experience. Nothing can replace the human experience,” said Martins. “However, AI has the potential to free time from routine tasks to help nursing practitioners focus more on their patients and health care outcomes.”
In 2021, FAU’s College of Nursing and College of Engineering and Computer Science launched two new combined programs in nursing and artificial intelligence and biomedical engineering. The innovative combined degree programs provide FAU bachelor’s in nursing (BSN) graduates with a leading edge in AI, which includes algorithms, pattern matching, deep learning and cognitive computing to learn how to understand complex data.
AI can be applied to almost every field of health care, including drug development, treatment decisions and patient care. Graduates of this FAU program can tackle complex problems that would otherwise be difficult or very time-intensive to address without AI. FAU BSN graduates who continue on to the master’s in science (MS) in the biomedical engineering program will use engineering principles to define and solve problems in biology, medicine, health care and other fields.
“The future of nursing, augmented by AI, holds immense promise for driving positive change, elevating patient experiences, and broadening access to health care services,” said Martins. “While AI technology can offer efficiency gains to supplement staffing levels and reduce stressful working conditions, it is essential to the future success of health care that we acknowledge that skilled talent will remain indispensable to effective health care delivery and outcomes.”
Read the full results here: The Future of Nursing Whitepaper (crosscountry.com).
About the Survey: This national Cross Country Healthcare survey was conducted with 1,127 nursing professionals and students at health care and hospital facilities. The online survey was conducted between Jan. 18 and March 11, in partnership with FAU’s Christine E. Lynn College of Nursing.
The survey was conducted with 1,127 nursing professionals and students at health care and hospital facilities.
CREDIT
Alex Dolce, Florida Atlantic University
- FAU -
About Florida Atlantic University's Christine E. Lynn College of Nursing
FAU’s Christine E. Lynn College of Nursing is nationally and internationally known for its excellence and philosophy of caring science. In 2024, the College was ranked No. 4 for the Family Nurse Practitioner Master’s concentration nationwide by U.S. News and World Report, No. 17 for “Best Online Master’s in Nursing Administration and Financial Leadership Programs” and No. 32 for the “Best Online Master’s in Nursing Programs.” In 2023, FAU graduates on the Boca Raton campus earned an 81% pass rate on the National Council Licensure Examination for Registered Nurses (NCLEX-RN®) and a 100% AGNP Certification Pass Rate. The baccalaureate, master’s and DNP programs at Florida Atlantic University’s Christine E. Lynn College of Nursing are accredited by the Commission on Collegiate Nursing Education. The College is the only one in the U.S. to have all degree programs endorsed by the American Holistic Nursing Credentialing Corporation.
About Florida Atlantic University:
Florida Atlantic University, established in 1961, officially opened its doors in 1964 as the fifth public university in Florida. Today, the University serves more than 30,000 undergraduate and graduate students across six campuses located along the southeast Florida coast. In recent years, the University has doubled its research expenditures and outpaced its peers in student achievement rates. Through the coexistence of access and excellence, FAU embodies an innovative model where traditional achievement gaps vanish. FAU is designated a Hispanic-serving institution, ranked as a top public university by U.S. News & World Report and a High Research Activity institution by the Carnegie Foundation for the Advancement of Teaching. For more information, visit www.fau.edu.
About Cross Country Healthcare, Inc.
Cross Country Healthcare, Inc. is a market-leading, tech-enabled workforce solutions and advisory firm with 38 years of industry experience and insight. We help clients tackle complex labor-related challenges and achieve high-quality outcomes while reducing complexity and improving visibility through data-driven insights. Diversity, equality, and inclusion are at the heart of the organization’s overall corporate social responsibility program. It is closely aligned with our core values to create a better future for its people, communities, and stockholders.
METHOD OF RESEARCH
Survey
SUBJECT OF RESEARCH
People
ARTICLE TITLE
Future of Nursing Survey: “Embracing Technology While Preserving Humanity.
ARTICLE PUBLICATION DATE
25-Apr-2024
Using AI to improve diagnosis of rare genetic disorders
BAYLOR COLLEGE OF MEDICINE
HOUSTON – (April 25, 2024) – Diagnosing rare Mendelian disorders is a labor-intensive task, even for experienced geneticists. Investigators at Baylor College of Medicine are trying to make the process more efficient using artificial intelligence. The team developed a machine learning system called AI-MARRVEL (AIM) to help prioritize potentially causative variants for Mendelian disorders. The study is published today in NEJM AI.
Researchers from the Baylor Genetics clinical diagnostic laboratory noted that AIM's module can contribute to predictions independent of clinical knowledge of the gene of interest, helping to advance the discovery of novel disease mechanisms. “The diagnostic rate for rare genetic disorders is only about 30%, and on average, it is six years from the time of symptom onset to diagnosis. There is an urgent need for new approaches to enhance the speed and accuracy of diagnosis,” said co-corresponding author Dr. Pengfei Liu, associate professor of molecular and human genetics and associate clinical director at Baylor Genetics.
AIM is trained using a public database of known variants and genetic analysis called Model organism Aggregated Resources for Rare Variant ExpLoration (MARRVEL) previously developed by the Baylor team. The MARRVEL database includes more than 3.5 million variants from thousands of diagnosed cases. Researchers provide AIM with patients’ exome sequence data and symptoms, and AIM provides a ranking of the most likely gene candidates causing the rare disease.
Researchers compared AIM’s results to other algorithms used in recent benchmark papers. They tested the models using three data cohorts with established diagnoses from Baylor Genetics, the National Institutes of Health-funded Undiagnosed Diseases Network (UDN) and the Deciphering Developmental Disorders (DDD) project. AIM consistently ranked diagnosed genes as the No. 1 candidate in twice as many cases than all other benchmark methods using these real-world data sets.
“We trained AIM to mimic the way humans make decisions, and the machine can do it much faster, more efficiently and at a lower cost. This method has effectively doubled the rate of accurate diagnosis,” said co-corresponding author Dr. Zhandong Liu, associate professor of pediatrics – neurology at Baylor and investigator at the Jan and Dan Duncan Neurological Research Institute (NRI) at Texas Children’s Hospital.
AIM also offers new hope for rare disease cases that have remained unsolved for years. Hundreds of novel disease-causing variants that may be key to solving these cold cases are reported every year; however, determining which cases warrant reanalysis is challenging because of the high volume of cases. The researchers tested AIM’s clinical exome reanalysis on a dataset of UDN and DDD cases and found that it was able to correctly identify 57% of diagnosable cases.
“We can make the reanalysis process much more efficient by using AIM to identify a high-confidence set of potentially solvable cases and pushing those cases for manual review,” Zhandong Liu said. “We anticipate that this tool can recover an unprecedented number of cases that were not previously thought to be diagnosable.”
Researchers also tested AIM’s potential for discovery of novel gene candidates that have not been linked to a disease. AIM correctly predicted two newly reported disease genes as top candidates in two UDN cases.
“AIM is a major step forward in using AI to diagnose rare diseases. It narrows the differential genetic diagnoses down to a few genes and has the potential to guide the discovery of previously unknown disorders,” said co-corresponding author Dr. Hugo Bellen, Distinguished Service Professor in molecular and human genetics at Baylor and chair in neurogenetics at the Duncan NRI.
“When combined with the deep expertise of our certified clinical lab directors, highly curated datasets and scalable automated technology, we are seeing the impact of augmented intelligence to provide comprehensive genetic insights at scale, even for the most vulnerable patient populations and complex conditions,” said senior author Dr. Fan Xia, associate professor of molecular and human genetics at Baylor and vice president of clinical genomics at Baylor Genetics. “By applying real-world training data from a Baylor Genetics cohort without any inclusion criteria, AIM has shown superior accuracy. Baylor Genetics is aiming to develop the next generation of diagnostic intelligence and bring this to clinical practice.”
Other authors of this work include Dongxue Mao, Chaozhong Liu, Linhua Wang, Rami AI-Ouran, Cole Deisseroth, Sasidhar Pasupuleti, Seon Young Kim, Lucian Li, Jill A.Rosenfeld, Linyan Meng, Lindsay C. Burrage, Michael Wangler, Shinya Yamamoto, Michael Santana, Victor Perez, Priyank Shukla, Christine Eng, Brendan Lee and Bo Yuan. They are affiliated with one or more of the following institutions: Baylor College of Medicine, Jan and Dan Duncan Neurological Research Institute at Texas Children’s Hospital, Al Hussein Technical University, Baylor Genetics and the Human Genome Sequencing Center at Baylor.
This work was supported by the Chang Zuckerberg Initiative and the National Institute of Neurological Disorders and Stroke (3U2CNS132415).
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JOURNAL
NEJM AI
ARTICLE TITLE
AI-MARRVEL: A Knowledge-Driven Artificial Intelligence for Molecular Diagnostics of Mendelian Disorders
ARTICLE PUBLICATION DATE
25-Apr-2024
Artificial intelligence helps scientists engineer plants to fight climate change
A unique collaboration at Salk uses deep learning software called SLEAP to analyze plant features, accelerating design of climate-saving plants
NEWS RELEASE
SALK INSTITUTE
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FROM LEFT: TALMO PEREIRA, ELIZABETH BERRIGAN, AND WOLFGANG BUSCH.
view moreCREDIT: SALK INSTITUTE
LA JOLLA (April 24, 2024)—The Intergovernmental Panel on Climate Change (IPCC) declared that removing carbon from the atmosphere is now essential to fighting climate change and limiting global temperature rise. To support these efforts, Salk scientists are harnessing plants’ natural ability to draw carbon dioxide out of the air by optimizing their root systems to store more carbon for a longer period of time.
To design these climate-saving plants, scientists in Salk’s Harnessing Plants Initiative are using a sophisticated new research tool called SLEAP—an easy-to-use artificial intelligence (AI) software that tracks multiple features of root growth. Created by Salk Fellow Talmo Pereira, SLEAP was initially designed to track animal movement in the lab. Now, Pereira has teamed up with plant scientist and Salk colleague Professor Wolfgang Busch to apply SLEAP to plants.
In a study published in Plant Phenomics on April 12, 2024, Busch and Pereira debut a new protocol for using SLEAP to analyze plant root phenotypes—how deep and wide they grow, how massive their root systems become, and other physical qualities that, prior to SLEAP, were tedious to measure. The application of SLEAP to plants has already enabled researchers to establish the most extensive catalog of plant root system phenotypes to date.
What’s more, tracking these physical root system characteristics helps scientists find genes affiliated with those characteristics, as well as whether multiple root characteristics are determined by the same genes or independently. This allows the Salk team to determine what genes are most beneficial to their plant designs.
“This collaboration is truly a testament to what makes Salk science so special and impactful,” says Pereira. “We’re not just ‘borrowing’ from different disciplines—we’re really putting them on equal footing in order to create something greater than the sum of its parts.”
Prior to using SLEAP, tracking the physical characteristics of both plants and animals required a lot of labor that slowed the scientific process. If researchers wanted to analyze an image of a plant, they would need to manually flag the parts of the image that were and weren’t plant—frame-by-frame, part-by-part, pixel-by-pixel. Only then could older AI models be applied to process the image and gather data about the plant’s structure.
What sets SLEAP apart is its unique use of both computer vision (the ability for computers to understand images) and deep learning (an AI approach for training a computer to learn and work like the human brain). This combination allows researchers to process images without moving pixel-by-pixel, instead skipping this intermediate labor-intensive step to jump straight from image input to defined plant features.
“We created a robust protocol validated in multiple plant types that cuts down on analysis time and human error, while emphasizing accessibility and ease-of-use—and it required no changes to the actual SLEAP software,” says first author Elizabeth Berrigan, a bioinformatics analyst in Busch’s lab.
Without modifying the baseline technology of SLEAP, the researchers developed a downloadable toolkit for SLEAP called sleap-roots (available as open-source software here). With sleap-roots, SLEAP can process biological traits of root systems like depth, mass, and angle of growth.
The Salk team tested the sleap-roots package in a variety of plants, including crop plants like soybeans, rice, and canola, as well as the model plant species Arabidopsis thaliana—a flowering weed in the mustard family. Across the variety of plants trialed, they found the novel SLEAP-based method outperformed existing practices by annotating 1.5 times faster, training the AI model 10 times faster, and predicting plant structure on new data 10 times faster, all with the same or better accuracy than before.
Together with massive genome sequencing efforts for elucidating the genotype data in large numbers of crop varieties, these phenotypic data, such as a plant’s root system growing especially deep in soil, can be extrapolated to understand the genes responsible for creating that especially deep root system.
This step—connecting phenotype and genotype—is crucial in Salk’s mission to create plants that hold on to more carbon and for longer, as those plants will need root systems designed to be deeper and more robust. Implementing this accurate and efficient software will allow the Harnessing Plants Initiative to connect desirable phenotypes to targetable genes with groundbreaking ease and speed.
“We have already been able to create the most extensive catalogue of plant root system phenotypes to date, which is really accelerating our research to create carbon-capturing plants that fight climate change,” says Busch, the Hess Chair in Plant Science at Salk. “SLEAP has been so easy to apply and use, thanks to Talmo’s professional software design, and it’s going to be an indispensable tool in my lab moving forward.”
Accessibility and reproducibility were at the forefront of Pereira’s mind when creating both SLEAP and sleap-roots. Because the software and sleap-roots toolkit are free to use, the researchers are excited to see how sleap-roots will be used around the world. Already, they have begun discussions with NASA scientists hoping to utilize the tool not only to help guide carbon-sequestering plants on Earth, but also to study plants in space.
At Salk, the collaborative team is not yet ready to disband—they are already embarking on a new challenge of analyzing 3D data with SLEAP. Efforts to refine, expand, and share SLEAP and sleap-roots will continue for years to come, but its use in Salk’s Harnessing Plants Initiative is already accelerating plant designs and helping the Institute make an impact on climate change.
Other authors include Lin Wang, Hannah Carrillo, Kimberly Echegoyen, Mikayla Kappes, Jorge Torres, Angel Ai-Perreira, Erica McCoy, Emily Shane, Charles Copeland, Lauren Ragel, Charidimos Georgousakis, Sanghwa Lee, Dawn Reynolds, Avery Talgo, Juan Gonzalez, Ling Zhang, Ashish Rajurkar, Michel Ruiz, Erin Daniels, Liezl Maree, and Shree Pariyar of Salk.
The work was supported by the Bezos Earth Fund, the Hess Corporation, the TED Audacious Project, and the National Institutes of Health (RF1MH132653).
A green leafy plant with its roots exposed on a background of abstract computer imagery representing SLEAP.
CREDIT
Salk Institute
Landmark plant root gif [VIDEO] |
SLEAP and sleap-roots automatically detect landmarks across the entire root system architecture.
About the Salk Institute for Biological Studies:
Unlocking the secrets of life itself is the driving force behind the Salk Institute. Our team of world-class, award-winning scientists pushes the boundaries of knowledge in areas such as neuroscience, cancer research, aging, immunobiology, plant biology, computational biology, and more. Founded by Jonas Salk, developer of the first safe and effective polio vaccine, the Institute is an independent, nonprofit research organization and architectural landmark: small by choice, intimate by nature, and fearless in the face of any challenge. Learn more at www.salk.edu.
JOURNAL
Plant Phenomics
ARTICLE TITLE
Fast and efficient root phenotyping via pose estimation
ARTICLE PUBLICATION DATE
22-Apr-2024
Sweet potato quality analysis is enhanced with hyperspectral imaging and AI
UNIVERSITY OF ILLINOIS COLLEGE OF AGRICULTURAL, CONSUMER AND ENVIRONMENTAL SCIENCES
IMAGE:
MD TOUKIR AHMED, A DOCTORAL STUDENT IN AGRICULTURAL AND BIOLOGICAL ENGINEERING AT THE UNIVERSITY OF ILLINOIS URBANA-CHAMPAIGN, TAKES IMAGES OF A SWEET POTATO WITH A HYPERSPECTRAL IMAGING CAMERA.
view moreCREDIT: COLLEGE OF ACES
URBANA, Ill. – Sweet potatoes are a popular food choice for consumers worldwide because of their delicious taste and nutritious quality. The red, tuberous root vegetable can be processed into chips and fries, and it has a range of industrial applications, including textiles, biodegradable polymers, and biofuels.
Sweet potato quality assessment is crucial for producers and processors because features influence texture and taste, consumer preferences, and viability for different purposes. A new study from the University of Illinois Urbana-Champaign explores the use of hyperspectral imaging and explainable artificial intelligence (AI) to assess sweet potato attributes.
“Traditionally, quality assessment is done using laboratory analytical methods. You need different instruments to measure different attributes in the lab, and you need to wait for the results. With hyperspectral imaging, you can measure several parameters simultaneously. You can assess every potato in a batch, not just a few samples. Spectral imaging is non-invasive, fast, accurate, and cost-effective,” said Mohammed Kamruzzaman, assistant professor in the Department of Agricultural and Biological Engineering (ABE), part of the College of Agricultural, Consumer and Environmental Sciences (ACES) and The Grainger College of Engineering at Illinois.
The study is part of a multi-state collaboration funded by the U.S. Department of Agriculture that includes researchers from Mississippi, North Carolina, Michigan, Louisiana, and Illinois. Each university addresses different aspects of the project; Kamruzzaman’s team focuses on the assessment of three chemical attributes — dry matter, firmness, and soluble sugar content (degree brix) — which affect the market price and whether a potato is suitable for the consumer or for processing.
The researchers use a visible near-infrared hyperspectral imaging camera to take images of sweet potatoes from two different angles. Analyzing the images produces spectral data, which are used to identify key wavelengths and develop color maps that display the distribution of desired attributes.
Hyperspectral imaging has become an important tool in agricultural and food processing research. However, it generates a vast amount of data that is processed with machine learning. It’s complex and typically acts like a black box, where users don’t know what is happening.
“We combine hyperspectral imaging with explainable AI, allowing us to understand the processes behind the results. It is a way to visualize how the machine learning algorithms work, how input data are processed, and how features are connected to predict the output,” said Md Toukir Ahmed, a doctoral student in ABE and lead author of the paper.
“We believe this is a novel application of this method for sweet potato assessment. This pioneering work has the potential to pave the way for usage in a wide range of other agricultural and biological research fields as well.”
The results can help industry professionals and researchers understand the significance of different features in predicting quality attributes, which leads to more informed decision-making and ensures supplies of higher-quality products to consumers.
Kamruzzaman said one goal of the multi-university project is to develop a tool that processors can use to quickly and easily scan batches of sweet potatoes to determine features and attributes. Eventually, researchers could create a mobile app consumers can use in the grocery store to scan the quality of sweet potatoes at the point of purchase.
The paper, “Advancing sweetpotato quality assessment with hyperspectral imaging and explainable artificial intelligence,” is published in Computers and Electronics in Agriculture [doi.org/10.1016/j.compag.2024.108855].
This work was funded by the U.S. Department of Agriculture Agricultural Marketing Service through the Specialty Crop Multistate Program grant AM21SCMPMS1010. The contents are solely the responsibility of the authors and do not necessarily represent the official views of the USDA.
JOURNAL
Computers and Electronics in Agriculture
METHOD OF RESEARCH
Data/statistical analysis
ARTICLE TITLE
Advancing sweetpotato quality assessment with hyperspectral imaging and explainable artificial intelligence
New study reveals how AI can enhance flexibility, efficiency for customer service centers
BINGHAMTON UNIVERSITY
IMAGE:
RESEARCHERS AT BINGHAMTON UNIVERSITY, STATE UNIVERSITY OF NEW YORK DETERMINED THAT WHILE IT’S BENEFICIAL FOR CUSTOMER SERVICE COMPANIES TO BE OPEN TO HARNESSING THE BENEFITS AND NAVIGATING ANY CHALLENGES OF AI SYSTEMS AS A GUIDE TO THEIR BUSINESS STRATEGIES, THEY SHOULD NOT DO SO AT THE EXPENSE OF SUPPORTING QUALITY PROFESSIONAL DEVELOPMENT AND ONGOING LEARNING OPPORTUNITIES FOR THEIR STAFF.
view moreCREDIT: BINGHAMTON UNIVERSITY, STATE UNIVERSITY OF NEW YORK
BINGHAMTON, N.Y. -- Whenever you call a customer service contact center, the team on the other end of the line typically has three goals: to reduce their response time, solve your problem and do it within the shortest service time possible.
However, resolving your problem might entail a significant time investment, potentially clashing with an overarching business objective to keep service duration to a minimum. These conflicting priorities can be commonplace for customer service contact centers, which often rely on the latest technology to meet customers’ needs.
To pursue those conflicting demands, these organizations practice what’s referred to as ambidexterity, and there are three different modes to achieve it: structural separation, behavioral integration and sequential alternation. So, what role might artificial intelligence (AI) systems play in improving how these organizations move from one ambidexterity mode to another to accomplish their tasks?
New research involving the School of Management at Binghamton University, State University of New York explored that question. Using data from different contact center sites, researchers examined the impact of AI systems on a customer service organization’s ability to shift across ambidexterity modes.
The key takeaway: it’s a delicate balancing act; AI is a valuable asset, so long as it’s used properly, though these organizations shouldn’t rely on it exclusively to guide their strategies.
Associate Professor Sumantra Sarkar, who helped conduct the research, said the study’s goal was to understand better how organizations today might use AI to guide their transition from one ambidexterity mode to another because certain structures or approaches might be more beneficial from one month to the next.
“Customer service organizations often balance exploiting the latest technology to boost efficiency and, therefore, save money,” Sarkar said. “This dichotomy is what ambidexterity is all about, exploring new technology to gain new insights and exploiting it to gain efficiency.”
As part of the three-year study, researchers examined the practices of five contact center sites: two global banks, one national bank in a developing country, a telecommunication Fortune 500 company in South Asia and a global infrastructure vendor in telecommunications hardware.
While many customer service organizations have spent recent years investing in AI, assuming that not doing so could lead to customer dissatisfaction, the researchers found these organizations haven’t used AI to its full potential. They have primarily used it for self-service applications.
Some of the AI-assisted tasks researchers tracked at those sites included:
- using AI systems to automatically open applications, send emails and transfer information from one system to another
- approving or disapproving loan applications
- providing personalized service based on customer’s data and contact history
Researchers determined that while it’s beneficial for customer service companies to be open to harnessing the benefits and navigating any challenges of AI systems as a guide to their business strategies, they should not do so at the expense of supporting quality professional development and ongoing learning opportunities for their staff.
Sarkar said that to fully utilize AI’s benefits, those leading customer service organizations need to examine every customer touchpoint and identify opportunities to enhance the customer experience while boosting the operation’s efficiency.
As a result, Sarkar said newcomers in this technology-savvy industry should learn how companies with 20 or 30 years of experience have already adapted to changes in technology, especially AI, during that time before forming their own business strategies.
“Any business is a balancing game because what you decide to do at the start of the year based on a forecast has to be revised over and over again,” Sarkar said. Since there’s that added tension within customer service organizations of whether they want to be more efficient or explore new areas, they have to work even harder at striking that balance. Using AI in the right way effectively helps them accomplish that.”
JOURNAL
International Journal of Information Management
SUBJECT OF RESEARCH
People
Cleveland Clinic study finds artificial intelligence can develop treatments to prevent “superbugs”
Researchers used reinforcement learning to design antibiotic regimens to prevent treatment resistance
CLEVELAND CLINIC
Cleveland Clinic researchers developed an artficial intelligence (AI) model that can determine the best combination and timeline to use when prescribing drugs to treat a bacterial infection, based solely on how quickly the bacteria grow given certain perturbations. A team led by Jacob Scott, MD, PhD, and his lab in the Theory Division of Translational Hematology and Oncology, recently published their findings in PNAS.
Antibiotics are credited with increasing the average US lifespan by almost ten years. Treatment lowered fatality rates for health issues we now consider minor – like some cuts and injuries. But antibiotics aren't working as well as they used to, in part because of widespread use.
"Health agencies worldwide agree that we're entering a post-antibiotic era," explains Dr. Scott. "If we don't change how we go after bacteria, more people will die from antibiotic-resistant infections than from cancer by 2050."
Bacteria replicate quickly, producing mutant offspring. Overusing antibiotics gives bacteria a chance to practice making mutations that resist treatment. Over time, the antibiotics kill all the susceptible bacteria, leaving behind only the stronger mutants that the antibiotics can't kill.
One strategy physicians are using to modernize the way we treat bacterial infections is antibiotic cycling. Healthcare providers rotate between different antibiotics over specific time periods. Changing between different drugs gives bacteria less time to evolve resistance to any one class of antibiotic. Cycling can even make bacteria more susceptible to other antibiotics.
"Drug cycling shows a lot of promise in effectively treating diseases," says study first author and medical student Davis Weaver, PhD. "The problem is that we don't know the best way to do it. Nothing's standardized between hospitals for which antibiotic to give, for how long and in what order."
Study co-author Jeff Maltas, PhD, a postdoctoral fellow at Cleveland Clinic, uses computer models to predict how a bacterium's resistance to one antibiotic will make it weaker to another. He teamed up with Dr. Weaver to see if data-driven models could predict drug cycling regimens that minimize antibiotic resistance and maximize antibiotic susceptibility, despite the random nature of how bacteria evolve.
Dr. Weaver led the charge to apply reinforcement learning to the drug cycling model, which teaches a computer to learn from its mistakes and successes to determine the best strategy to complete a task. This study is among the first to apply reinforcement learning to antibiotic cycling regiments, Drs. Weaver and Maltas say.
"Reinforcement learning is an ideal approach because you just need to know how quickly the bacteria are growing, which is relatively easy to determine," explains Dr. Weaver. "There's also room for human variations and errors. You don't need to measure the growth rates perfectly down to the exact millisecond every time."
The research team's AI was able to figure out the most efficient antibiotic cycling plans to treat multiple strains of E. coli and prevent drug resistance. The study shows that AI can support complex decision-making like calculating antibiotic treatment schedules, Dr. Maltas says.
Dr. Weaver explains that in addition to managing an individual patient's infection, the team's AI model can inform how hospitals treat infections across the board. He and his research team are also working to expand their work beyond bacterial infections into other deadly diseases.
"This idea isn't limited to bacteria, it can be applied to anything that can evolve treatment resistance," he says. "In the future we believe these types of AI can be used to to manage drug-resistant cancers, too."
JOURNAL
Proceedings of the National Academy of Sciences
ARTICLE TITLE
Reinforcement learning informs optimal treatment strategies to limit antibiotic resistance
ARTICLE PUBLICATION DATE
22-Apr-2024
AI tool recognizes serious ocular disease in horses
LUDWIG-MAXIMILIANS-UNIVERSITÄT MÜNCHEN
Researchers at the LMU Equine Clinic have developed a deep learning tool that is capable of reliably diagnosing moon blindness in horses based on photos.
Colloquially known as moon blindness, equine recurrent uveitis (ERU) is an inflammatory ocular disease in horses, which can lead to blindness or loss of the affected eye. It is one of the most common eye diseases in horses and has a major economic impact. Correct and swift diagnosis is very important to minimize lasting damage. A team led by Professor Anna May from the LMU Equine Clinic has developed and trained a deep learning tool that reliably recognizes the disease and can support veterinary doctors in the making of diagnoses, as the researchers report in a current study.
In an online survey, the researchers asked some 150 veterinarians to evaluate 40 photos. The pictures showed a mixture of healthy eyes, eyes with ERU, and eyes with other diseases. Working on the basis of image analyses, the deep learning tool was given the task of evaluating the same photos. Subsequently, May compared the results of the veterinarians against those of the AI. She discovered that veterinary doctors specialized in horses interpreted the pictures correctly 76 percent of the time, while the remaining vets from small animal or mixed practices were right 67 percent of the time. “With the deep learning tool, the probability of getting a correct answer was 93 percent,” says May. “Although the differences were not statistically significant, they nonetheless show that the AI reliably recognizes an ERU and has great potential as a tool for supporting veterinary doctors.”
The tool is web-app-based and simple to use. All you need is a smartphone. “It’s not meant to replace veterinarians, but can help them reach the correct diagnosis. It is particularly valuable for less experienced professionals or for horse owners in regions where vets are few and far between,” emphasizes May. Through the early detection of ERU, affected horses can receive appropriate treatment more quickly, which can be decisive in slowing down the progress of the disease and saving the afflicted eyes.
JOURNAL
Equine Veterinary Journal
ARTICLE TITLE
Comparison of veterinarians and a deep learning tool in the diagnosis of equine ophthalmic diseases
