Augmented Intelligence Research Articles

AEFL: Anonymous and Efficient Federated Learning in Vehicle–Road Cooperation Systems With Augmented Intelligence of Things

AEFL: Anonymous and Efficient Federated Learning in Vehicle–Road Cooperation Systems With Augmented Intelligence of Things

INtelligent toolkit for reconnaissance, assessments and prehospital support in Perilous InciDents: a realistic experiment in prehospital environment

BackgroundFirst responders, when arriving at a disaster, need a rapid analysis of the environment in which they are going to operate, as they have to assess the conditions surrounding potential victims and neutralize any risks that may exist.The EU-funded INTREPID develops a new technology platform to assist first responders when arriving on the scene of a disaster. The project INTREPID aims to support safer operations in the form of more efficient, fast, and safe disaster site assessments. The objective of the study is to implement new technologies into rescue operations to facilitate and improve situational awareness and operation management capabilities to save lives.The focus of the study is relevant to the field of mass casualty incident management and disaster, as proper communication is extremely relevant in the management of catastrophes.MethodThe first phase of the project started with a qualitative methodology SCRUM, for catching the end user’s feedback and requirements to design the interface platform. It was developed a platform to support first responders in disasters areas improving the 3D scanning and analysis of disaster areas. This platform is based on the concepts of intelligence amplification and eXtended Reality, with hololens, drones and robots. The project continued with a β phase in which the platform with all tools integrated were tested in simulated mass casualty disasters.ResultsThese technologies are tested in different disaster scenarios: A flooded subway stop in Stockholm, an accident in the chemical industry in Marseille, and a man-made explosion in a hospital in Madrid. Through this platform, first responders can immediately initiate operations without exposing personnel to potential harmful risks without specialized equipment, with all important information shared and coordinated, among all responders, whether they are security, firefighters, or emergency health professionals.ConclusionsThe performance pilots and the questionnaire results validated the effectiveness and usability of the final version of the INTREPID platform and tools.

The Next Generation of Learning: AI Mentorship for a Transformative Educational Experience

Developers of this augmented reality-based artificial intelligence education program aim at augmenting student engagement in learning through the incorporation of virtual mentors and the overall experience being made engaging, efficient, and personalized. Therefore, the application provides contextual support, tailored learning pathways, and real-time feedback that significantly enhance users' understanding of complex AI concepts with customized AI avatars and augmented reality technologies. These artificial intelligence mentors and assistants act as learning facilitators for both engineering and non-engineering students while enhancing the educational experience. The software features natural language processing to enable smooth communication; it introduces gamification into the courseware, adds quizzes, and provides rewards for a better learning experience, among other features

The Holistic Intelligent Healthcare Theory (HIHT): Integrating AI for Ethical, Transparent, and Human-Centered Healthcare Innovation

The rise of artificial intelligence (AI) in healthcare has the potential to significantly increase the efficiency, personalization, and overall efficacy of medical care delivery. However, its integration needs to be improved regarding ethical norms, data quality, openness, and human-centered care. This study proposes the Holistic Intelligent Healthcare Theory (HIHT), a comprehensive theory for guiding AI's ethical and practical use in healthcare. It draws on insights from theories such as the Technology Acceptance Model (TAM), the Unified Theory of Acceptance and Use of Technology (UTAUT), Socio-Technical Systems Theory (STS), Ethics of Care, Data Governance and Information Quality Theory, and Augmented Intelligence Theory. The HIHT highlights the importance of centralized governance, strong data quality, transparent AI methods, human-AI collaboration, and a reimagined role for healthcare practitioners—the "Intelligent Doctor." The study presents the theory's structures and investigates their implications for AI integration in healthcare, offering a road map for responsible and practical AI applications.

Using augmented intelligence to improve long term outcomes.

For augmented intelligence (AI) tools to realize their potential, critical care clinicians must ensure they are designed to improve long-term outcomes. This overview is intended to align professionals with the state-of-the art of AI. Many AI tools are undergoing preliminary assessment of their ability to support the care of survivors and their caregivers at multiple time points after intensive care unit (ICU) discharge. The domains being studied include early identification of deterioration (physiological, mental), management of impaired physical functioning, pain, sleep and sexual dysfunction, improving nutrition and communication, and screening and treatment of cognitive impairment and mental health disorders.Several technologies are already being marketed and many more are in various stages of development. These technologies mostly still require clinical trials outcome testing. However, lacking a formal regulatory approval process, some are already in use. Plans for long-term management of ICU survivors must account for the development of a holistic follow-up system that incorporates AI across multiple platforms. A tiered post-ICU screening program may be established wherein AI tools managed by ICU follow-up clinics provide appropriate assistance without human intervention in cases with less pathology and refer severe cases to expert treatment.

Replacing "Artificial Intelligence" with "Augmented Intelligence" for a more Informed and Responsible Use of Technology in Medical Education.

Replacing "Artificial Intelligence" with "Augmented Intelligence" for a more Informed and Responsible Use of Technology in Medical Education.

ChatGPT: A canary in the coal mine or a parrot in the echo chamber? Detecting fraud with LLM: The case of FTX

Does the paradigm shift brought by Large Language Models (LLMs) hold the promise of revolutionizing financial analysis? Our article tackles this question by exploring fraud detection in cryptocurrency exchanges, with a focus on FTX. We study the abilities of generative artificial intelligence tools like ChatGPT to serve as early-warning systems of fraud and identify red flags in the particular and difficult case where no financial information is available. We recognize several challenges to provide insights beyond human knowledge. To achieve a higher degree of scrutiny, we highlight the role of sequential interactions between the AI Chatbot and the researcher as well as the inclusion of external contents, a technique known as Retrieval Augmented Generation (RAG). Therefore, this article serves as a cautionary tale on the necessary conditions to achieve augmented intelligence.

How Much Wearable Data is Enough for the Utility and Trust of Augmented Artificial Intelligence Systems? A Scenario-Based Interview with Medical Professionals

The paper explores the synergy between wearable data and augmented Artificial Intelligence (AI) through findings from two interconnected studies. The first study (study 1) focuses on medical professionals’ perceptions of wearable data and AI, and the second study (Study 2) extends it focuses on how differences in the level of granularity in the data presented affect the professionals’ understanding, interpretation, and trust in AI recommendations. This system allows medical professionals to view AI-generated recommendations for sleep and activity improvement and explanations of the underlying rationale. While each study has distinct research questions, Study 2 builds upon Study 1's foundation. Both studies employed scenario-based interviews. Thematic analysis of Study 1 identified trust as a crucial factor in the acceptance of wearable data and AI, influencing Study 2's exploration of factors affecting trust, such as explainability, data granularity, representativeness, and user interaction. Study 2 highlighted varying perspectives on information sufficiency and data sharing from the AI system linked to professionals’ roles and tasks. The work offers insights into data granularity’s impact on engagement with AI recommendations.

Human-Guided Metaverse Synthesis for Quantum Dots: Advancing Nanomaterial Research through Augmented Artificial Intelligence.

This study proposes an innovative paradigm for metaverse-based synthesis experiments, aiming to enhance experimental optimization efficiency through human-guided parameter tuning in the metaverse and augmented artificial intelligence (AI) with human expertise. By integration of the metaverse experimental system with automated synthesis techniques, our goal is to profoundly extend the efficiency and advancement of materials chemistry. Leveraging advanced software algorithms and simulation techniques within the metaverse, we dynamically adjust synthesis parameters in real time, thereby minimizing the conventional trial-and-error methods inherent in laboratory experiments. In comparison fully AI-driven adjustments, this human-intervened approach to metaverse parameter tuning achieves desired results more rapidly. Coupled with automated synthesis techniques, experiments in the metaverse system can be swiftly realized. We validate the high synthesis efficiency and precision of this system through NaYF4:Yb/Tm nanocrystal synthesis experiments, highlighting its immense potential in nanomaterial studies. This pioneering approach not only simplifies the process of nanocrystal preparation but also paves the way for novel methodologies, laying the foundation for future breakthroughs in materials science and nanotechnology.

Machine learning-assisted discovery of flow reactor designs

Additive manufacturing has enabled the fabrication of advanced reactor geometries, permitting larger, more complex design spaces. Identifying promising configurations within such spaces presents a significant challenge for current approaches. Furthermore, existing parameterizations of reactor geometries are low dimensional with expensive optimization, limiting more complex solutions. To address this challenge, we have established a machine learning-assisted approach for the design of new chemical reactors, combining the application of high-dimensional parameterizations, computational fluid dynamics and multi-fidelity Bayesian optimization. We associate the development of mixing-enhancing vortical flow structures in coiled reactors with performance and used our approach to identify the key characteristics of optimal designs. By appealing to the principles of fluid dynamics, we rationalized the selection of design features that lead to experimental plug flow performance improvements of ~60% compared with conventional designs. Our results demonstrate that coupling advanced manufacturing techniques with ‘augmented intelligence’ approaches can give rise to reactor designs with enhanced performance.

First clinical experience using augmented intelligence in robotic colorectal surgery with the Senhance robotic platform.

First clinical experience using augmented intelligence in robotic colorectal surgery with the Senhance robotic platform.

An integrated MCDM approach for enhancing efficiency in connected autonomous vehicles through augmented intelligence and IoT integration

The rapid advancement of Connected Autonomous Vehicles (CAVs) equipped with self-powered sensors is poised to revolutionize road safety, efficiency, and the quality of travel. However, the effective integration of these technologies within dynamic road environments poses significant challenges, highlighting the need for innovative multi-criteria decision-making (MCDM) approaches to optimize their deployment. This study tries to solve the integration problem by proposing an MCDM method that uses fuzzy sets to evaluate and rank different scenarios for better performance of augmented intelligence and the Internet of Things (IoT) in CAVs. This research uses two key techniques: the Fuzzy Logarithm of Incremental Weights (F-LMAW) method for criteria evaluation and the Fermatean Fuzzy Weighted Aggregated Sum Product Assessment (FF-WASPAS) method for scenario evaluation. To ensure the reliability and accuracy of the results, a sensitivity analysis was conducted. The analysis confirmed the effectiveness of the proposed approach. The study's results showed that the third scenario (the integration of augmented intelligence and IoT in urban areas via self-powered sensors) got the highest score, which shows how important it is compared to the other choices. The obtained results highlight the importance of integrating augmented intelligence and IoT technologies to enhance public transportation with autonomous vehicles equipped with self-powered sensors.

Evolutionary Decision-Making and Planning for Autonomous Driving: A Hybrid Augmented Intelligence Framework

Evolutionary Decision-Making and Planning for Autonomous Driving: A Hybrid Augmented Intelligence Framework

Accelerating Scientific Paper Skimming with Augmented Intelligence Through Customizable Faceted Highlights

Scholars need to keep up with an exponentially increasing flood of scientific papers. To aid this challenge, we introduce Scim, a novel intelligent interface that helps scholars skim papers to rapidly review and gain a cursory understanding of its contents. Scim supports the skimming process by highlighting salient content within a paper, directing a scholar’s attention. These automatically-extracted highlights are faceted by content type, evenly distributed across a paper, and have a density configurable by scholars. We evaluate Scim with an in-lab usability study and a longitudinal diary study, revealing how its highlights facilitate the more efficient construction of a conceptualization of a paper. Finally, we describe the process of scaling highlights from their conception within Scim, a research prototype, to production on over 521,000 papers within the Semantic Reader, a publicly-available augmented reading interface for scientific papers. We conclude by discussing design considerations and tensions for the design of future skimming tools with augmented intelligence.

Outlier analysis for accelerating clinical discovery: An augmented intelligence framework and a systematic review.

Clinical discoveries largely depend on dedicated clinicians and scientists to identify and pursue unique and unusual clinical encounters with patients and communicate these through case reports and case series. This process has remained essentially unchanged throughout the history of modern medicine. However, these traditional methods are inefficient, especially considering the modern-day availability of health-related data and the sophistication of computer processing. Outlier analysis has been used in various fields to uncover unique observations, including fraud detection in finance and quality control in manufacturing. We propose that clinical discovery can be formulated as an outlier problem within an augmented intelligence framework to be implemented on any health-related data. Such an augmented intelligence approach would accelerate the identification and pursuit of clinical discoveries, advancing our medical knowledge and uncovering new therapies and management approaches. We define clinical discoveries as contextual outliers measured through an information-based approach and with a novelty-based root cause. Our augmented intelligence framework has five steps: define a patient population with a desired clinical outcome, build a predictive model, identify outliers through appropriate measures, investigate outliers through domain content experts, and generate scientific hypotheses. Recognizing that the field of obstetrics can particularly benefit from this approach, as it is traditionally neglected in commercial research, we conducted a systematic review to explore how outlier analysis is implemented in obstetric research. We identified two obstetrics-related studies that assessed outliers at an aggregate level for purposes outside of clinical discovery. Our findings indicate that using outlier analysis in clinical research in obstetrics and clinical research, in general, requires further development.

AI Health Agents: Pathway2vec, ReflectE, Category Theory, and Longevity

Health Agents are introduced as the concept of a personalized AI health advisor overlay for continuous health monitoring (e.g. 1000x/minute) medical-grade smartwatches and wearables for “healthcare by app” instead of “sickcare by appointment.” Individuals can customize the level of detail in the information they view. Health Agents “speak” natural language to humans and formal language to the computational infrastructure, possibly outputting the mathematics of personalized homeostatic health as part of their reinforcement learning agent behavior. As an AI health interface, the agent facilitates the management of precision medicine as a service. Healthy longevity is a high-profile area characterized by the increasing acceptance of medical intervention, longevity biotech venture capital investment, and global priority as 2 billion people will be over 65 in 2050. Aging hallmarks, biomarkers, and clocks provide a quantitative measure for intervention. Some of the leading interventions include metformin, rapamycin, spermidine, NAD+/sirtuins, alpha-ketoglutarate, and taurine. AI-driven digital biology, longevity medicine, and Web3 personalized healthcare come together in the idea of Health Agents. This Web3 genAI tool for automated health management, specifically via digital-biological twins and pathway2vec approaches, demonstrates human-AI intelligence amplification and works towards healthy longevity for global well-being.

Overtaking Mechanisms Based on Augmented Intelligence for Autonomous Driving: Data Sets, Methods, and Challenges

Overtaking Mechanisms Based on Augmented Intelligence for Autonomous Driving: Data Sets, Methods, and Challenges

Engaging industry effectively and ethically in artificial intelligence from the Augmented Artificial Intelligence Committee Standards Workgroup

Engaging industry effectively and ethically in artificial intelligence from the Augmented Artificial Intelligence Committee Standards Workgroup

Flexible and Stretchable Thermoresponsive Display for Multifunctional Device Applications

Metal–organic frameworks (MOFs) have been recognized vastly as stimuli‐responsive materials owing to their extreme sensitivity toward the stimuli that make them flawless nominees for multifunctional device applications. However, MOFs are often oversensitive toward moisture, which significantly affects their stable responses and reproducibility. Herein, a MOF–polymer display (i.e., copper‐based MOF with polydimethylsiloxane) is developed that exhibits high sensitivity toward temperature changes without getting affected by moisture. In response to the thermal gradient, the display exhibits the change in color from sky blue to dark blue when the temperature varies from 30 to 100 ºC, which is attributed to the second‐order phase transition within the time interval 40 s and reversible in 60 s. To provide insights on the mechanism, ex situ UV–vis and temperature‐dependent operando X‐day diffraction are conducted, which unveils that the color change in the display is associated with reversible contraction/expansion of the unit cell parameters. In addition to that, the thermoresponse of the display remains unaffected under mechanical deformations, which indicates that the color display is mechanically and thermally stable. The as‐developed color display is mechanically robust and highly sensitive toward temperature change; it can be utilized as the color indicator in signaling temperature rise in smart mobiles or laptops during discharging to keep the user safe or applications in augmented intelligence.

Clinician perspectives on how situational context and augmented intelligence design features impact perceived usefulness of sepsis prediction scores embedded within a simulated electronic health record.

Obtain clinicians' perspectives on early warning scores (EWS) use within context of clinical cases. We developed cases mimicking sepsis situations. De-identified data, synthesized physician notes, and EWS representing deterioration risk were displayed in a simulated EHR for analysis. Twelve clinicians participated in semi-structured interviews to ascertain perspectives across four domains: (1) Familiarity with and understanding of artificial intelligence (AI), prediction models and risk scores; (2) Clinical reasoning processes; (3) Impression and response to EWS; and (4) Interface design. Transcripts were coded and analyzed using content and thematic analysis. Analysis revealed clinicians have experience but limited AI and prediction/risk modeling understanding. Case assessments were primarily based on clinical data. EWS went unmentioned during initial case analysis; although when prompted to comment on it, they discussed it in subsequent cases. Clinicians were unsure how to interpret or apply the EWS, and desired evidence on its derivation and validation. Design recommendations centered around EWS display in multi-patient lists for triage, and EWS trends within the patient record. Themes included a "Trust but Verify" approach to AI and early warning information, dichotomy that EWS is helpful for triage yet has disproportional signal-to-high noise ratio, and action driven by clinical judgment, not the EWS. Clinicians were unsure of how to apply EWS, acted on clinical data, desired score composition and validation information, and felt EWS was most useful when embedded in multi-patient views. Systems providing interactive visualization may facilitate EWS transparency and increase confidence in AI-generated information.