Progress in robotics for combating infectious diseases

Robotic versus manual disinfection of global priority pathogens at COVID-19-dedicated hospitals

BACKGROUND

Twelve bacterial families identified as global priority pathogens (GPPs) pose the greatest threat to human health due to declining antibiotic efficacy. Robotics, a swift and contactless tool for disinfecting hospital surfaces, was sought to compare with manual disinfection.

METHODS

The disinfection efficacy of a robot was compared with manual disinfection for multiple clinical surfaces and inanimate objects at two hospitals in Nepal using bleach (NaOCl). Surfaces were swabbed pre- and post-disinfection and total heterotrophic plate count evaluated, and bacterial pathogens identified using Gram's staining and biochemical characteristics. Disinfection outcomes were reported as log reduction (log10 CFU/inch2) of heterotrophic count and presence or absence of GPPs: Staphylococcus aureus, Escherichia coli, Acinetobacter spp., and Klebsiella pneumoniae, among others.

RESULTS

Both robotic and manual disinfection significantly reduced the microbial load (log 2.3 to log 5.8) on hospital surfaces. No pathogens were detected post-disinfection using the robot. Robotic disinfection was more effective, significantly reducing the bacterial load (log 5.8) compared to manual disinfection (log 3.95).

CONCLUSIONS

Our results showed better efficacy of robotic disinfection over manual disinfection of hospital surfaces, and thus contactless robotic disinfection is recommended for disinfecting surfaces in the hospital and clinical settings as it favors patient safety against GPPs.

A Flexible Dual-Mode Photodetector for Human-Machine Collaborative IR Imaging

Photothermoelectric (PTE) photodetectors with self-powered and uncooled advantages have attracted much interest due to the wide application prospects in the military and civilian fields. However, traditional PTE photodetectors lack of mechanical flexibility and cannot operate independently without the test instrument. Herein, we present a flexible PTE photodetector capable of dual-mode output, combining electrical and optical signal generation for enhanced functionality. Using solution processing, high-quality MXene thin films are assembled on asymmetric electrodes as the photosensitive layer. The geometrically asymmetric electrode design significantly enhances the responsivity, achieving 0.33 mA W-1 under infrared illumination, twice that of the symmetrical configuration. This improvement stems from optimized photothermal conversion and an expanded temperature gradient. The PTE device maintains stable performance after 300 bending cycles, demonstrating excellent flexibility. A new energy conversion pathway has been established by coupling the photothermal conversion of MXene with thermochromic composite materials, leading to a real-time visualization of invisible infrared radiation. Leveraging this functionality, we demonstrate the first human-machine collaborative infrared imaging system, wherein the dual-mode photodetector arrays synchronously generate human-readable pattern and machine-readable pattern. Our study not only provides a new solution for functional integration of flexible photodetectors, but also sets a new benchmark for human-machine collaborative optoelectronics.

A Critical Review of the Prospect of Integrating Artificial Intelligence in Infectious Disease Diagnosis and Prognosis

This paper explores the transformative potential of integrating artificial intelligence (AI) in the diagnosis and prognosis of infectious diseases. By analyzing diverse datasets, including clinical symptoms, laboratory results, and imaging data, AI algorithms can significantly enhance early detection and personalized treatment strategies. This paper reviews how AI-driven models improve diagnostic accuracy, predict patient outcomes, and contribute to effective disease management. It also addresses the challenges and ethical considerations associated with AI, including data privacy, algorithmic bias, and equitable access to healthcare. Highlighting case studies and recent advancements, the paper underscores AI's role in revolutionizing infectious disease management and its implications for future healthcare delivery.

Navigation benchmarking for autonomous mobile robots in hospital environment

The widespread adoption of robotic technologies in healthcare has opened up new perspectives for enhancing accuracy, effectiveness and quality of medical procedures and patients' care. Special attention has been given to the reliability of robots when operating in environments shared with humans and to the users' safety, especially in case of mobile platforms able to navigate autonomously. From the analysis of the literature, it emerges that navigation tests carried out in a hospital environment are preliminary and not standardized. This paper aims to overcome the limitations in the assessment of autonomous mobile robots navigating in hospital environments by proposing: (i) a structured benchmarking protocol composed of a set of standardized tests, taking into account conditions with increasing complexity, (ii) a set of quantitative performance metrics. The proposed approach has been used in a realistic setting to assess the performance of two robotic platforms, namely HOSBOT and TIAGo, with different technical features and developed for different applications in a clinical scenario.

BART, the new robotic assistant: big data, artificial intelligence, robotics, and telemedicine integration for an ICU 4.0

We are in the era of Health 4.0 when novel technologies are providing tools capable of improving the quality and safety of the services provided. Our project involves the integration of different technologies (AI, big data, robotics, and telemedicine) to create a unique system for patients admitted to intensive care units suffering from infectious diseases capable of both increasing the personalization of care and ensuring a safer environment for caregivers.

Roadmap for Clinical Translation of Mobile Microrobotics

Medical microrobotics is an emerging field to revolutionize clinical applications in diagnostics and therapeutics of various diseases. On the other hand, the mobile microrobotics field has important obstacles to pass before clinical translation. This article focuses on these challenges and provides a roadmap of medical microrobots to enable their clinical use. From the concept of a "magic bullet" to the physicochemical interactions of microrobots in complex biological environments in medical applications, there are several translational steps to consider. Clinical translation of mobile microrobots is only possible with a close collaboration between clinical experts and microrobotics researchers to address the technical challenges in microfabrication, safety, and imaging. The clinical application potential can be materialized by designing microrobots that can solve the current main challenges, such as actuation limitations, material stability, and imaging constraints. The strengths and weaknesses of the current progress in the microrobotics field are discussed and a roadmap for their clinical applications in the near future is outlined.

The evolution of robotics: research and application progress of dental implant robotic systems

The use of robots to augment human capabilities and assist in work has long been an aspiration. Robotics has been developing since the 1960s when the first industrial robot was introduced. As technology has advanced, robotic-assisted surgery has shown numerous advantages, including more precision, efficiency, minimal invasiveness, and safety than is possible with conventional techniques, which are research hotspots and cutting-edge trends. This article reviewed the history of medical robot development and seminal research papers about current research progress. Taking the autonomous dental implant robotic system as an example, the advantages and prospects of medical robotic systems would be discussed which would provide a reference for future research.

The role of artificial intelligence in generating original scientific research

Artificial intelligence (AI) is a revolutionary technology that is finding wide application across numerous sectors. Large language models (LLMs) are an emerging subset technology of AI and have been developed to communicate using human languages. At their core, LLMs are trained with vast amounts of information extracted from the internet, including text and images. Their ability to create human-like, expert text in almost any subject means they are increasingly being used as an aid to presentation, particularly in scientific writing. However, we wondered whether LLMs could go further, generating original scientific research and preparing the results for publication. We taskedGPT-4, an LLM, to write an original pharmaceutics manuscript, on a topic that is itself novel. It was able to conceive a research hypothesis, define an experimental protocol, produce photo-realistic images of 3D printed tablets, generate believable analytical data from a range of instruments and write a convincing publication-ready manuscript with evidence of critical interpretation. The model achieved all this is less than 1 h. Moreover, the generated data were multi-modal in nature, including thermal analyses, vibrational spectroscopy and dissolution testing, demonstrating multi-disciplinary expertise in the LLM. One area in which the model failed, however, was in referencing to the literature. Since the generated experimental results appeared believable though, we suggest that LLMs could certainly play a role in scientific research but with human input, interpretation and data validation. We discuss the potential benefits and current bottlenecks for realising this ambition here.

Soft Sensors and Actuators for Wearable Human-Machine Interfaces

Haptic human-machine interfaces (HHMIs) combine tactile sensation and haptic feedback to allow humans to interact closely with machines and robots, providing immersive experiences and convenient lifestyles. Significant progress has been made in developing wearable sensors that accurately detect physical and electrophysiological stimuli with improved softness, functionality, reliability, and selectivity. In addition, soft actuating systems have been developed to provide high-quality haptic feedback by precisely controlling force, displacement, frequency, and spatial resolution. In this Review, we discuss the latest technological advances of soft sensors and actuators for the demonstration of wearable HHMIs. We particularly focus on highlighting material and structural approaches that enable desired sensing and feedback properties necessary for effective wearable HHMIs. Furthermore, promising practical applications of current HHMI technology in various areas such as the metaverse, robotics, and user-interactive devices are discussed in detail. Finally, this Review further concludes by discussing the outlook for next-generation HHMI technology.

Battery-free, wireless, and electricity-driven soft swimmer for water quality and virus monitoring

Miniaturized mobile electronic system is an effective candidate for in situ exploration of confined spaces. However, realizing such system still faces challenges in powering issue, untethered mobility, wireless data acquisition, sensing versatility, and integration in small scales. Here, we report a battery-free, wireless, and miniaturized soft electromagnetic swimmer (SES) electronic system that achieves multiple monitoring capability in confined water environments. Through radio frequency powering, the battery-free SES system demonstrates untethered motions in confined spaces with considerable moving speed under resonance. This system adopts soft electronic technologies to integrate thin multifunctional bio/chemical sensors and wireless data acquisition module, and performs real-time water quality and virus contamination detection with demonstrated promising limits of detection and high sensitivity. All sensing data are transmitted synchronously and displayed on a smartphone graphical user interface via near-field communication. Overall, this wireless smart system demonstrates broad potential for confined space exploration, ranging from pathogen detection to pollution investigation.

Magnetic Continuum Robot with Intraoperative Magnetic Moment Programming

Magnetic continuum robots (MCRs), which are free of complicated structural designs for transmission, can be miniaturized and are therefore widely used in the medical field. However, the deformation shapes of different segments, including deflection directions and curvatures, are difficult to control simultaneously under an external programmable magnetic field. This is because the latest MCRs have designs with an invariable magnetic moment combination or profile of one or more actuating units. Therefore, the limited dexterity of the deformation shape causes the existing MCRs to collide readily with their surroundings or makes them unable to approach difficult-to-reach regions. These prolonged collisions are unnecessary or even hazardous, especially for catheters or similar medical devices. In this study, a novel magnetic moment intraoperatively programmable continuum robot (MMPCR) is introduced. By applying the proposed magnetic moment programming method, the MMPCR can deform under three modalities, that is, J, C, and S shapes. Additionally, the deflection directions and curvatures of different segments in the MMPCR can be modulated as desired. Furthermore, the magnetic moment programming and MMPCR kinematics are modeled, numerically simulated, and experimentally validated. The experimental results exhibit a mean deflection angle error of 3.3° and correspond well with simulation results. Comparisons between navigation capacities of the MMPCR and MCR demonstrate that the MMPCR has a higher capacity for dexterous deformation.

Ultraviolet disinfection (UV-D) robots: bridging the gaps in dentistry

Maintaining a microbe-free environment in healthcare facilities has become increasingly crucial for minimizing virus transmission, especially in the wake of recent epidemics like COVID-19. To meet the urgent need for ongoing sterilization, autonomous ultraviolet disinfection (UV-D) robots have emerged as vital tools. These robots are gaining popularity due to their automated nature, cost advantages, and ability to instantly disinfect rooms and workspaces without relying on human labor. Integrating disinfection robots into medical facilities reduces infection risk, lowers conventional cleaning costs, and instills greater confidence in patient safety. However, UV-D robots should complement rather than replace routine manual cleaning. To optimize the functionality of UV-D robots in medical settings, additional hospital and device design modifications are necessary to address visibility challenges. Achieving seamless integration requires more technical advancements and clinical investigations across various institutions. This mini-review presents an overview of advanced applications that demand disinfection, highlighting their limitations and challenges. Despite their potential, little comprehensive research has been conducted on the sterilizing impact of disinfection robots in the dental industry. By serving as a starting point for future research, this review aims to bridge the gaps in knowledge and identify unresolved issues. Our objective is to provide an extensive guide to UV-D robots, encompassing design requirements, technological breakthroughs, and in-depth use in healthcare and dentistry facilities. Understanding the capabilities and limitations of UV-D robots will aid in harnessing their potential to revolutionize infection control practices in the medical and dental fields.

Disrupting the infectious disease ecosystem in the digital precision health era innovations and converging emerging technologies

This commentary explores the convergence of precision health and evolving technologies, including the critical role of artificial intelligence (AI) and emerging technologies in infectious diseases (ID) and microbiology. We discuss their disruptive impact on the ID ecosystem and examine the transformative potential of frontier technologies in precision health, public health, and global health when deployed with robust ethical and data governance guardrails in place.

Comparison of the safety, effectiveness, and usability of swab robot vs. manual nasopharyngeal specimen collection

Background

Healthcare workers face a risk of infection during aerosol-generating procedures, such as nasal swabbing. Robot-assisted nasopharyngeal sampling aims to minimize this risk and reduce stress for healthcare providers. However, its effectiveness and safety require validation.

Methods

We conducted a controlled trial with 80 subjects at two teaching hospitals and compared robot-collected vs manually-collected nasopharyngeal swabs. The primary outcomes included specimen quality and success rate of nasopharyngeal swab collection. We also recorded the pain index, duration of the collection, and psychological stress using a post-collection questionnaire.

Results

During the study period, from September 23 to October 27, 2020, 40 subjects were enrolled in both the robotic and manual groups. The cycle threshold (Ct) value for nasopharyngeal specimens was statistically higher in the robotic group compared to the manual group (30.9 vs 28.0, p < 0.01). Both groups had Ct values under 35, indicating good quality specimens. In the robotic group, 3 out of 40 subjects required a second attempt at specimen collection, resulting in a success rate of 92.5 %. Further, although the pain levels were lower in the robotic group, the difference was not statistically significant (2.8 vs 3.6, p = 0.07). The manual group had a shorter sampling time, which was 29 s (201 vs 29, p < 0.05). However, when factoring in the time needed to put on personal protective equipment, the average time for the manual group increased to 251 s (201 vs 251, p < 0.05). Participants' questionnaire results show comparable psychological stress in both groups. Medical staff expressed that using a robot would reduce their psychological stress.

Conclusions

We propose a safe and effective robotic technology for collecting nasopharyngeal specimens without face-to-face contact, which may reduce the stress of physicians and nurses. This technology can also be optimized for efficiency, making it useful in situations where droplet-transmitted infectious diseases are a concern.

Robotic ultrasound imaging: State-of-the-art and future perspectives

Ultrasound (US) is one of the most widely used modalities for clinical intervention and diagnosis due to the merits of providing non-invasive, radiation-free, and real-time images. However, free-hand US examinations are highly operator-dependent. Robotic US System (RUSS) aims at overcoming this shortcoming by offering reproducibility, while also aiming at improving dexterity, and intelligent anatomy and disease-aware imaging. In addition to enhancing diagnostic outcomes, RUSS also holds the potential to provide medical interventions for populations suffering from the shortage of experienced sonographers. In this paper, we categorize RUSS as teleoperated or autonomous. Regarding teleoperated RUSS, we summarize their technical developments, and clinical evaluations, respectively. This survey then focuses on the review of recent work on autonomous robotic US imaging. We demonstrate that machine learning and artificial intelligence present the key techniques, which enable intelligent patient and process-specific, motion and deformation-aware robotic image acquisition. We also show that the research on artificial intelligence for autonomous RUSS has directed the research community toward understanding and modeling expert sonographers' semantic reasoning and action. Here, we call this process, the recovery of the "language of sonography". This side result of research on autonomous robotic US acquisitions could be considered as valuable and essential as the progress made in the robotic US examination itself. This article will provide both engineers and clinicians with a comprehensive understanding of RUSS by surveying underlying techniques. Additionally, we present the challenges that the scientific community needs to face in the coming years in order to achieve its ultimate goal of developing intelligent robotic sonographer colleagues. These colleagues are expected to be capable of collaborating with human sonographers in dynamic environments to enhance both diagnostic and intraoperative imaging.

Surgical robots in movies may not be science fiction

RoboDoc, a 2009 comedy, offers a surprisingly accurate assessment of robot surgery.

Cyberbiosecurity in high-containment laboratories

High-containment laboratories (HCLs) conduct critical research on infectious diseases, provide diagnostic services, and produce vaccines for the world's most dangerous pathogens, often called high-consequence pathogens (HCPs). The modernization of HCLs has led to an increasingly cyber-connected laboratory infrastructure. The unique cyberphysical elements of these laboratories and the critical data they generate pose cybersecurity concerns specific to these laboratories. Cyberbiosecurity, the discipline devoted to the study of cybersecurity risks in conjunction with biological risks, is a relatively new field for which few approaches have been developed to identify, assess, and mitigate cyber risks in biological research and diagnostic environments. This study provides a novel approach for cybersecurity risk assessment and identification of risk mitigation measures by applying an asset-impact analysis to the unique environment of HCLs. First, we identified the common cyber and cyberphysical systems in HCLs, summarizing the typical cyber-workflow. We then analyzed the potential adverse outcomes arising from a compromise of these cyber and cyberphysical systems, broadly categorizing potential consequences as relevant to scientific advancement, public health, worker safety, security, and the financial wellbeing of these laboratories. Finally, we discussed potential risk mitigation strategies, leaning heavily on the cybersecurity materials produced by the Center for Internet Security (CIS), including the CIS Controls®, that can serve as a guide for HCL operators to begin the process of implementing risk mitigation measures to reduce their cyberbiorisk and considering the integration of cyber risk management into existing biorisk management practices. This paper provides a discussion to raise awareness among laboratory decision-makers of these critical risks to safety and security within HCLs. Furthermore, this paper can serve as a guide for evaluating cyberbiorisks specific to a laboratory by identifying cyber-connected assets and the impacts associated with a compromise of those assets.

Roboethics of tourism and hospitality industry: A systematic review

This study aims to give a comprehensive analysis of customers' acceptance and use of AI gadgets and its relevant ethical issues in the tourism and hospitality business in the era of the Internet of Things. Adopting a PRISMA methodology for Systematic Reviews and Meta-Analyses, the present research reviews how tourism and hospitality scholars have conducted research on AI technology in the field of tourism and the hospitality industry. Most of the journal articles related to AI issues published in Web of Science, ScienceDirect.com and the journal websites were considered in this review. The results of this research offer a better understanding of AI implementation with roboethics to investigate AI-related issues in the tourism and hospitality industry. In addition, it provides decision-makers in the hotel industry with practical references on service innovation, participation in the design of AI devices and AI device applications, meeting customer needs, and optimising customer experience. The theoretical implications and practical interpretations are further identified.

HANDdata - first-person dataset including proximity and kinematics measurements from reach-to-grasp actions

HANDdata is a dataset designed to provide hand kinematics and proximity vision data during reach to grasp actions of non-virtual objects, specifically tailored for autonomous grasping of a robotic hand, and with particular attention to the reaching phase. Thus, we sought to capture target object characteristics from radar and time-of-flight proximity sensors, as well as details of the reach-to-grasp action by looking at wrist and fingers kinematics, and at hand-object interaction main events. We structured the data collection as a sequence of static and grasping tasks, organized by increasing levels of complexity. HANDdata is a first-person, reach-to-grasp dataset that includes almost 6000 human-object interactions from 29 healthy adults, with 10 standardized objects of 5 different shapes and 2 kinds of materials. We believe that such data collection can be of value for researchers interested in autonomous grasping robots for healthcare and industrial applications, as well as for those interested in radar-based computer vision and in basic aspects of sensorimotor control and manipulation.

Bioinspired claw-engaged and biolubricated swimming microrobots creating active retention in blood vessels

Swimming microrobots guided in the circulation system offer considerable promise in precision medicine but currently suffer from problems such as limited adhesion to blood vessels, intensive blood flow, and immune system clearance-all reducing the targeted interaction. A swimming microrobot design with clawed geometry, a red blood cell (RBC) membrane-camouflaged surface, and magnetically actuated retention is discussed, allowing better navigation and inspired by the tardigrade's mechanical claw engagement, coupled to an RBC membrane coating, to minimize blood flow impact. Using clinical intravascular optical coherence tomography in vivo, the microrobots' activity and dynamics in a rabbit jugular vein was monitored, illustrating very effective magnetic propulsion, even against a flow of ~2.1 cm/s, comparable with rabbit blood flow characteristics. The equivalent friction coefficient with magnetically actuated retention is elevated ~24-fold, compared to magnetic microspheres, achieving active retention at 3.2 cm/s, for >36 hours, showing considerable promise across biomedical applications.

Care Robotics: An Assessment of Professional Perception in the Face of the COVID-19 Pandemic

The COVID-19 crisis accelerated the adoption of technologies. Technological advancement is also expected in robotics applied to any sector, including in healthcare. The aim is to assess the professional perception of care robotics facing COVID-19. This study aimed to (1) select a tool for assessing different aspects of healthcare, (2) analyse the professional perception about the development, usefulness and helpfulness of technologies and robotics in the field of healthcare and (3) evaluate the correlation between the perceived helpfulness of care robotics and the selected tool. We implement five validated clinical tests which integrate 80 items about a person and their clinical situation. From the sample of 46 professionals, 95.65% affirmed that technology was moderately to completely useful for professional performance in the context of the pandemic, lowering to 67.39% when asked only about robotics; 93.48% stated that the inclusion of robotics in at least one health area affected by COVID-19 would have helped them. Finally, the variables extracted from clinical tests corresponded to the most relevant health areas as identified by the professionals. This research shows the potential of care robotics oriented towards healthcare from a care paradigm.

UV Disinfection Robots: A Review

The novel coronavirus (COVID-19) pandemic has completely changed our lives and how we interact with the world. The pandemic has brought about a pressing need to have effective disinfection practices that can be incorporated into daily life. They are needed to limit the spread of infections through surfaces and air, particularly in public settings. Most of the current methods utilize chemical disinfectants, which can be laborious and time-consuming. Ultraviolet (UV) irradiation is a proven and powerful means of disinfection. There has been a rising interest in the implementation of UV disinfection robots by various public institutions, such as hospitals, long-term care homes, airports, and shopping malls. The use of UV-based disinfection robots could make the disinfection process faster and more efficient. The objective of this review is to equip readers with the necessary background on UV disinfection and provide relevant discussion on various aspects of UV robots.

Continuously-deformable and stiffness-tunable soft manipulator achieving unmanned COVID-19 pandemic sampling

In recent years, COVID-19 has spread across the whole world, and manpowered collection of pharyngeal samples undoubtedly increases the possibility of cross-infections. In this article, based on our previous fabricated soft manipulator (Cell Reports Physical Science, 2021, 2, 100600), we performed the COVID-19 sampling on real human volunteers by exploiting a pre-programmed unmanned system. The unmanned sampling system mainly includes a soft manipulator and a rigid motion platform, which are adjusted by pneumatic control box and the motor control modules, respectively. Drawn on the lead-through teaching method, the unmanned sampling of COVID-19 is realized by recording the applied pressure in soft manipulator and the feed motion of rigid platform. This research provides a potential approach for unmanned COVID-19 sampling, solving the risk of cross-infection during manual collection.

Development of a Computer System for Automatically Generating a Laser Photocoagulation Plan to Improve the Retinal Coagulation Quality in the Treatment of Diabetic Retinopathy

In this article, the development of a computer system for high-tech medical uses in ophthalmology is proposed. An overview of the main methods and algorithms that formed the basis of the coagulation plan planning system is presented. The system provides the formation of a more effective plan for laser coagulation in comparison with the use of existing coagulation techniques. An analysis of monopulse- and pattern-based laser coagulation techniques in the treatment of diabetic retinopathy has shown that modern treatment methods do not provide the required efficacy of medical laser coagulation procedures, as the laser energy is nonuniformly distributed across the pigment epithelium and may exert an excessive effect on parts of the retina and anatomical elements. The analysis has shown that the efficacy of retinal laser coagulation for the treatment of diabetic retinopathy is determined by the relative position of coagulates and parameters of laser exposure. In the course of the development of the computer system proposed herein, main stages of processing diagnostic data were identified. They are as follows: the allocation of the laser exposure zone, the evaluation of laser pulse parameters that would be safe for the fundus, mapping a coagulation plan in the laser exposure zone, followed by the analysis of the generated plan for predicting the therapeutic effect. In the course of the study, it was found that the developed algorithms for placing coagulates in the area of laser exposure provide a more uniform distribution of laser energy across the pigment epithelium when compared to monopulse- and pattern-based laser coagulation techniques.

Large-scale investigation of zoonotic viruses in the era of high-throughput sequencing

Zoonotic diseases considerably impact public health and socioeconomics. RNA viruses reportedly caused approximately 94% of zoonotic diseases documented from 1990 to 2010, emphasizing the importance of investigating RNA viruses in animals. Furthermore, it has been estimated that hundreds of thousands of animal viruses capable of infecting humans are yet to be discovered, warning against the inadequacy of our understanding of viral diversity. High-throughput sequencing (HTS) has enabled the identification of viral infections with relatively little bias. Viral searches using both symptomatic and asymptomatic animal samples by HTS have revealed hidden viral infections. This review introduces the history of viral searches using HTS, current analytical limitations, and future potentials. We primarily summarize recent research on large-scale investigations on viral infections reusing HTS data from public databases. Furthermore, considering the accumulation of uncultivated viruses, we discuss current studies and challenges for connecting viral sequences to their phenotypes using various approaches: performing data analysis, developing predictive modeling, or implementing high-throughput platforms of virological experiments. We believe that this article provides a future direction in large-scale investigations of potential zoonotic viruses using the HTS technology.

On the Use of Assistive Technology during the COVID-19 Outbreak: Results and Lessons Learned from Pilot Studies

As a consequence of the COVID-19 emergency, frail citizens felt isolated because of social isolation, suspended and/or strongly reduced home assistance, and limited access to hospitals. In this sense, assistive technology could play a pivotal role in empowering frail older adults reducing their isolation, as well as in reinforcing the work of formal caregivers and professionals. In this context, the goal of this paper is to present four pilot studies-conducted from March 2020 to April 2021-to promptly react to COVID-19 by providing assistive technology solutions, aiming to (1) guarantee high-quality service to older adults in-home or in residential facility contexts, (2) promote social inclusion, and (3) reduce the virus transmission. In particular, four services, namely, telepresence service, remote monitoring service, virtual visit, and environmental disinfection, were designed, implemented, and tested in real environments involving 85 end-users to assess the user experience and/or preliminary assess the technical feasibility. The results underlined that all the proposed services were generally accepted by older adults and professionals. Additionally, the results remarked that the use of telepresence robots in private homes and residential facilities increased enjoyment reducing anxiety, whereas the monitoring service supported the clinicians in monitoring the discharged COVID-19 patients. It is also worth mentioning that two new services/products were developed to disinfect the environment and to allow virtual visits within the framework of a hospital information system. The virtual visits service offered the opportunity to expand the portfolio of hospital services. The main barriers were found in education, technology interoperability, and ethical/legal/privacy compliance. It is also worth mentioning the key role played by an appropriate design and customer needs analysis since not all assistive devices were designed for older persons.

All-printed soft human-machine interface for robotic physicochemical sensing

Ultrasensitive multimodal physicochemical sensing for autonomous robotic decision-making has numerous applications in agriculture, security, environmental protection, and public health. Previously reported robotic sensing technologies have primarily focused on monitoring physical parameters such as pressure and temperature. Integrating chemical sensors for autonomous dry-phase analyte detection on a robotic platform is rather extremely challenging and substantially underdeveloped. Here, we introduce an artificial intelligence-powered multimodal robotic sensing system (M-Bot) with an all-printed mass-producible soft electronic skin-based human-machine interface. A scalable inkjet printing technology with custom-developed nanomaterial inks was used to manufacture flexible physicochemical sensor arrays for electrophysiology recording, tactile perception, and robotic sensing of a wide range of hazardous materials including nitroaromatic explosives, pesticides, nerve agents, and infectious pathogens such as SARS-CoV-2. The M-Bot decodes the surface electromyography signals collected from the human body through machine learning algorithms for remote robotic control and can perform in situ threat compound detection in extreme or contaminated environments with user-interactive tactile and threat alarm feedback. The printed electronic skin-based robotic sensing technology can be further generalized and applied to other remote sensing platforms. Such diversity was validated on an intelligent multimodal robotic boat platform that can efficiently track the source of trace amounts of hazardous compounds through autonomous and intelligent decision-making algorithms. This fully printed human-machine interactive multimodal sensing technology could play a crucial role in designing future intelligent robotic systems and can be easily reconfigured toward numerous practical wearable and robotic applications.

GuLiM: A Hybrid Motion Mapping Technique for Teleoperation of Medical Assistive Robot in Combating the COVID-19 Pandemic

Driven by the demand to largely mitigate nosocomial infection problems in combating the coronavirus disease 2019 (COVID-19) pandemic, the trend of developing technologies for teleoperation of medical assistive robots is emerging. However, traditional teleoperation of robots requires professional training and sophisticated manipulation, imposing a burden on healthcare workers, taking a long time to deploy, and conflicting the urgent demand for a timely and effective response to the pandemic. This paper presents a novel motion synchronization method enabled by the hybrid mapping technique of hand gesture and upper-limb motion (GuLiM). It tackles a limitation that the existing motion mapping scheme has to be customized according to the kinematic configuration of operators. The operator awakes the robot from any initial pose state without extra calibration procedure, thereby reducing operational complexity and relieving unnecessary pre-training, making it user-friendly for healthcare workers to master teleoperation skills. Experimenting with robotic grasping tasks verifies the outperformance of the proposed GuLiM method compared with the traditional direct mapping method. Moreover, a field investigation of GuLiM illustrates its potential for the teleoperation of medical assistive robots in the isolation ward as the Second Body of healthcare workers for telehealthcare, avoiding exposure of healthcare workers to the COVID-19.

Application of Artificial Intelligence in Ophthalmology for the Diagnosis and Treatment of Eye Diseases

In this paper, we present the main aspects of artificial intelligence application in ophthalmology for the diagnosis and treatment of eye diseases on the example of developing a computer system for personalizing retinal laser photocoagulation. Approaches to the automation of eye disease prediction and treatment based on fundus images are described. Four problems of applying the neural network approach are highlighted. Decision support information technology for personalizing laser treatment of diabetic macular edema and identifying prognostic factors of surgical outcome using methods of intellectual analysis of large unstructured data is described. The system allows the doctor to form a plan of optimal coagulation arrangement for retinal laser coagulation for each case, to predict the quality of laser coagulation depending on the initial data on the localization and severity of edema and to improve his skills by comparing the result of coagulation performed and the coagulation plan proposed by the system.

Five years of Science Robotics

[Figure: see text].

A Novel Training and Collaboration Integrated Framework for Human-Agent Teleoperation

Human operators have the trend of increasing physical and mental workloads when performing teleoperation tasks in uncertain and dynamic environments. In addition, their performances are influenced by subjective factors, potentially leading to operational errors or task failure. Although agent-based methods offer a promising solution to the above problems, the human experience and intelligence are necessary for teleoperation scenarios. In this paper, a truncated quantile critics reinforcement learning-based integrated framework is proposed for human–agent teleoperation that encompasses training, assessment and agent-based arbitration. The proposed framework allows for an expert training agent, a bilateral training and cooperation process to realize the co-optimization of agent and human. It can provide efficient and quantifiable training feedback. Experiments have been conducted to train subjects with the developed algorithm. The performances of human–human and human–agent cooperation modes are also compared. The results have shown that subjects can complete the tasks of reaching and picking and placing with the assistance of an agent in a shorter operational time, with a higher success rate and less workload than human–human cooperation.

Design of Novel End-effectors for Robot-assisted Swab Sampling to Combat Respiratory Infectious Diseases

The COVID-19 outbreak has caused the mortality worldwide and the use of swab sampling is a common way of screening and diagnosis. To combat respiratory infectious diseases and assist sampling, robots have been utilized and shown promising potentials. Nonetheless, a safe, patient-friendly, and low-cost swabbing system would be crucial for the practical implementation of robots in hospitals or inspection stations. In this study, we proposed two recyclable and cost-efficient end-effector designs that can be equipped at the distal end of a robot to passively regulate or actively sense the force exerted onto patients. One way is to introduce passive compliant mechanisms with soft material to increase the flexibility of the swabbing system, while the other way is utilizing a force-sensing gripper with embedded optoelectronic sensors to actively sense the force or torque. The proposed designs were modelled computationally and tested experimentally. It is identified that the passive compliant mechanisms can increase the flexibility of the swabbing system when subjected to the lateral force and mitigate the vertical force resulted from buckling. The lateral force range that the force-sensing gripper can detect is 0-0.35 N and the vertical force range causing buckling effect that can be sensed by gripper is 1.5-2.5 N.

Public Perception and Reception of Robotic Applications in Public Health Emergencies Based on a Questionnaire Survey Conducted during COVID-19

Various intelligent technologies have been applied during COVID-19, which has become a worldwide public health emergency and brought significant challenges to the medical systems around the world. Notably, the application of robots has played a role in hospitals, quarantine facilities and public spaces and has attracted much attention from the media and the public. This study is based on a questionnaire survey on the perception and reception of robots used for medical care in the pandemic among the Chinese population. A total of 1667 people participated in the survey, 93.6% of respondents were pursuing or had completed a bachelor, master or even doctorate degree. The results show that Chinese people generally held positive attitudes towards "anti-pandemic robots" and affirmed their contribution to reducing the burden of medical care and virus transmission. A few respondents were concerned about the issues of robots replacing humans and it was apparent that their ethical views on robots were not completely consistent across their demographics (e.g., age, industry). Nevertheless, most respondents tended to be optimistic about robot applications and dialectical about the ethical issues involved. This is related to the prominent role robots played during the pandemic, the Chinese public's expectations of new technologies and technology-friendly public opinion in China. Exploring the perception and reception of anti-pandemic robots in different countries or cultures is important because it can shed some light on the future applications of robots, especially in the field of infectious disease control.

A fully implantable device for intraperitoneal drug delivery refilled by ingestible capsules

Creating fully implantable robots that replace or restore physiological processes is a great challenge in medical robotics. Restoring blood glucose homeostasis in patients with type 1 diabetes is particularly interesting in this sense. Intraperitoneal insulin delivery could revolutionize type 1 diabetes treatment. At present, the intraperitoneal route is little used because it relies on accessing ports connecting intraperitoneal catheters to external reservoirs. Drug-loaded pills transported across the digestive system to refill an implantable reservoir in a minimally invasive fashion could open new possibilities in intraperitoneal delivery. Here, we describe PILLSID (PILl-refiLled implanted System for Intraperitoneal Delivery), a fully implantable robotic device refillable through ingestible magnetic pills carrying drugs. Once refilled, the device acts as a programmable microinfusion system for precise intraperitoneal delivery. The robotic device is grounded on a combination of magnetic switchable components, miniaturized mechatronic elements, a wireless powering system, and a control unit to implement the refilling and control the infusion processes. In this study, we describe the PILLSID prototyping. The device key blocks are validated as single components and within the integrated device at the preclinical level. We demonstrate that the refilling mechanism works efficiently in vivo and that the blood glucose level can be safely regulated in diabetic swine. The device weights 165 grams and is 78 millimeters by 63 millimeters by 35 millimeters, comparable with commercial implantable devices yet overcoming the urgent critical issues related to reservoir refilling and powering.