Research on potential disruptive technology identification based on technology network.

Three evident and meaningful characteristics of disruptive technology are the zeroing effect that causes sustaining technology useless for its remarkable and unprecedented progress, reshaping the landscape of technology and economy, and leading the future mainstream of technology system, all of which have profound impacts and positive influences. The identification of disruptive technology is a universally difficult task. Therefore, this paper aims to enhance the technical relevance of potential disruptive technology identification results and improve the granularity and effectiveness of potential disruptive technology identification topics. According to the life cycle theory, dividing the time stage, then constructing and analyzing the dynamic of technology networks to identify potential disruptive technology. Thereby, using the Latent Dirichlet Allocation (LDA) topic model further to clarify the topic content of potential disruptive technologies. This paper takes the large civil unmanned aerial vehicles (UAVs) as an example to prove the feasibility and effectiveness of the model. The results show that the potential disruptive technology in this field is the data acquisition, main equipment, and ground platform intelligence.

An Autonomous Sleep-Stage Detection Technique in Disruptive Technology Environment.

Autonomous sleep tracking at home has become inevitable in today's fast-paced world. A crucial aspect of addressing sleep-related issues involves accurately classifying sleep stages. This paper introduces a novel approach PSO-XGBoost, combining particle swarm optimisation (PSO) with extreme gradient boosting (XGBoost) to enhance the XGBoost model's performance. Our model achieves improved overall accuracy and faster convergence by leveraging PSO to fine-tune hyperparameters. Our proposed model utilises features extracted from EEG signals, spanning time, frequency, and time-frequency domains. We employed the Pz-oz signal dataset from the sleep-EDF expanded repository for experimentation. Our model achieves impressive metrics through stratified-K-fold validation on ten selected subjects: 95.4% accuracy, 95.4% F1-score, 95.4% precision, and 94.3% recall. The experiment results demonstrate the effectiveness of our technique, showcasing an average accuracy of 95%, outperforming traditional machine learning classifications. The findings revealed that the feature-shifting approach supplements the classification outcome by 3 to 4 per cent. Moreover, our findings suggest that prefrontal EEG derivations are ideal options and could open up exciting possibilities for using wearable EEG devices in sleep monitoring. The ease of obtaining EEG signals with dry electrodes on the forehead enhances the feasibility of this application. Furthermore, the proposed method demonstrates computational efficiency and holds significant value for real-time sleep classification applications.

Reflections about Blockchain in Health Data Sharing: Navigating a Disruptive Technology.

A comprehensive analysis was performed, considering blockchain technology (BT) properties in digital health, addressing medicolegal, privacy, and regulatory considerations. Adherence to personal data protection and healthcare regulatory guidelines were analyzed and compared for GDPR (Europe), HIPAA (United States), CCPA (California), PIPEDA (Canada), the Privacy Act of 1988 (Australia), APPI (Japan), and LGPD (Brazil). Issues such as health systems, strengthening and aligning policy orientations and initiatives, and emphasizing the role of data analysis in shaping health policies were explored. The study addressed conflicts between the legal frameworks and blockchain, comparing and suggesting solutions like the revision of laws and the integration of compliance mechanisms. Additionally, it sought to enhance IT-health literacy by integrating the healthcare and legal domains. Ongoing collaboration between legal, health, and IT experts is essential for designing systems that effectively balance privacy rights and data protection while maximizing the benefits of disruptive technologies like blockchain.

Innovative and disruptive technologies to prescribe, encourage, and evaluate physical exercise in healthy adults: a protocol of exploratory study followed by a noninferiority, investigator-blinded randomized clinical trial.

BACKGROUND: Cardiovascular diseases are a leading cause of mortality worldwide. A significant contributing factor to this mortality is the lack of engagement in preventive activities. Consequently, strategies for enhancing adherence to and duration of physical activity (PA) have become pivotal. This project aims to create and validate innovative, disruptive, and secure technologies that ensure appropriate exercise intensity, bolster adherence to PA, and monitor health biomarker responses pre-, during, and post-physical activity. METHODS: This exploratory study, followed by a noninferiority, investigator-blinded randomized clinical trial, will be divided into three phases: (1) development and validation of a sensor for real-time biofeedback during a functional assessment test; (2) integration of biofeedback and gamification into an app for the structured prescription of physical training within a controlled setting; and (3) implementation of biofeedback and gamification into an app for the prescription and monitoring of physical training in an uncontrolled setting. Phase 1 entails a validation test of a biosensor-monitoring heart rate (HR) and steps-during a modified shuttle walk test. In phase 2, the biosensor interfaces with a gamified smartphone application. The training regimen spans 6 weeks, 5 days weekly, with each session lasting 60 min: a five-min warm-up involving stationary gait, followed by 50 min of training at the target HR on the step and concluding with a five-min cool-down at a stationary pace. After 6 weeks of training, a new functional capacity test is conducted. Phase 3 involves an investigator-blinded, randomized clinical trial to demonstrate noninferiority. Participants are randomly assigned to either the intervention group (IG) or the control group (CG). IG participants practice exercise using the gamified application in an uncontrolled environment according to the prescribed method outlined in phase 2. CG participants receive PA practice guidelines exclusively. DISCUSSION: Anticipated outcomes include improved exercise adherence through the gamified application, better maintenance of prescribed exercise intensity, and enhanced health biomarkers. The results of this study will inform health-related decision-making. TRIAL REGISTRATION: The study protocol received approval from the Ethics Committee of Universidade Federal de Ciências da Saúde de Porto Alegre (54,492,221.80000.5345) and has been registered with the Brazilian Registry of Clinical Trials (ReBEC, RBR-359p69v).

Artificial Intelligence-driven Digital Cytology-based Cervical Cancer Screening: Is the Time Ripe to Adopt This Disruptive Technology in Resource-constrained Settings? A Literature Review.

Cervical cancer is still a public health scourge in the developing countries due to the lack of organized screening programs. Though liquid-based cytology methods improved the performance of cervical cytology, the interpretation still suffers from subjectivity. Artificial intelligence (AI) algorithms have offered objectivity leading to better sensitivity and specificity of cervical cancer screening. Whole slide imaging (WSI) that converts a glass slide to a virtual slide provides a new perspective to the application of AI, especially for cervical cytology. In the recent years, there have been a few studies employing various AI algorithms on WSI images of conventional or LBC smears and demonstrating differing sensitivity/specificity or accuracy at detection of abnormalities in cervical smears. Considering the interest in AI-based screening modalities, this well-timed review intends to summarize the progress in this field while highlighting the research gaps and providing future research directions.

Flow diversion: a disruptive technology coming of age. Lessons learned and challenges for the future.

Intracranial aneurysm treatment has been revolutionized over the last decade with the development of flow diversion technology. The use of this technology has evolved rapidly and has proven that cerebrovascular disease treatment remains one of the forefront innovation areas in neurosurgery. The good results on the treatment of internal carotid artery aneurysms up to the communicating segment have motivated the use of flow diversion beyond the circle of Willis and in the posterior circulation. Further advances and innovations of flow-diverting devices are underway and intended to improve the safety and efficacy of this therapy. This review article provides a detailed discussion about the origin, mechanism of action, initial experience, complications, types of devices, and future perspectives of flow diversion technology.

Robot Technology for the Elderly and the Value of Veracity: Disruptive Technology or Reinvigorating Entrenched Principles?

The implementation of care robotics in care settings is identified by some authors as a disruptive innovation, in the sense that it will upend the praxis of care. It is an open ethical question whether this alleged disruption will also have a transformative impact on established ethical concepts and principles. One prevalent worry is that the implementation of care robots will turn deception into a routine component of elderly care, at least to the extent that these robots will function as simulacra for something that they are not (i.e. human caregivers). At face value, this may indeed seem to indicate a concern for how this technology may upend existing practices and relationships within a care setting. Yet, on closer inspection, this reaction may rather point to a rediscovery and a revaluation of a particularly well-entrenched value or virtue, i.e. veracity. The virtue of veracity is one of the values that is mobilized to argue against a substitution of human caregivers (while a combination of care robots and human caregivers is much more accepted). The subject of this paper is to explore how the moral panic surrounding care robots should not so much be interpreted as an anticipated and probable disruptor in a care setting, but rather as a sensitizing - in a way conservationist - argument that identifies veracity as an established value that is supposed to be protected and advanced in present day and future care settings.

Disruptive technologies in health care disenchanted: a systematic review of concepts and examples.

OBJECTIVES: To clarify the concept of disruptive technologies in health care, provide examples and consider implications of potentially disruptive technologies for health technology assessment (HTA). METHODS: We conducted a systematic review of conceptual and empirical papers on healthcare technologies that are described as "disruptive." We searched MEDLINE and Embase from 2013 to April 2019 (updated in December 2021). Data extraction was done in duplicate by pairs of reviewers utilizing a data extraction form. A qualitative data analysis was undertaken based on an analytic framework for analysis of the concept and examples. Key arguments and a number of potential predictors of disruptive technologies were derived and implications for HTA organizations were discussed. RESULTS: Of 4,107 records, 28 were included in the review. Most of the papers included conceptual discussions and business models for disruptive technologies; only few papers presented empirical evidence. The majority of the evidence is related to the US healthcare system. Key arguments for describing a technology as disruptive include improvement of outcomes for patients, improved access to health care, reduction of costs and better affordability, shift in responsibilities between providers, and change in the organization of health care. A number of possible predictors for disruption were identified to distinguish these from "sustaining" innovations. CONCLUSIONS: Since truly disruptive technologies could radically change technology uptake and may modify provision of care patterns or treatment paths, they require a thorough evaluation of the consequences of using these technologies, including economic and organizational impact assessment and careful monitoring.

Disruptive technologies as a solution for disaster risk management: A review.

Integrating disruptive technologies within smart cities improves the infrastructure needed to potentially deal with disasters. This paper provides a perspective review of disruptive technologies such as the Internet of Things (IoT), image processing, artificial intelligence (AI), big data and smartphone applications which are in use and have been proposed for future improvements in disaster management of urban regions. The key focus of this paper is exploring ways in which smart cities could be established to harness the potential of disruptive technologies and improve post-disaster management. The key questions explored are a) what are the gaps or barriers to the utilization of disruptive technologies in the area of disaster management and b) How can the existing methods of disaster management be improved through the application of disruptive technologies. To respond to these questions, a novel framework based on integrated approaches based on big data analytics and AI is proposed for developing disaster management solutions using disruptive technologies.

Disruptive Technology, Leadership and the Future of Nursing.

Nurses need to take a strategic leadership role in managing disruptive health technologies that can be adopted to improve health and care within the population. While innovative technology developments continue to advance quickly, systematic changes to the health and care systems are not always geared to take advantage of these advances at the same rate. This panel will look at how disruptive technology will impact nursing practice and strategic leadership factors that shape acceptance/resistance to new technologies.

Building Nursing Knowledge to Meet the Needs of Disruptive Technology Healthcare Re-Design.

This paper provides a discourse based upon the key development of nursing in response to the emerging 4Ds of health technology re-design. Building informatics capability among health professionals is a workforce issue necessitated through the increasing prevalence of information technology and digitization of healthcare affecting the entire health workforce, specifically front-line nurses. The key concepts will be explored of Digitization, Distribution, Disruption and Diversity, a framework recognising the tsunami of technology such as Big Data analytics, comprehensive decision support systems for nursing, nanobots, robotics, and pharmacogenomics and the impact these have upon the nursing workforce.