Augmented Intelligence Research Articles

Augmented Intelligence in Smart Intersections: Local Digital Twins-Assisted Hybrid Autonomous Driving

Augmented Intelligence in Smart Intersections: Local Digital Twins-Assisted Hybrid Autonomous Driving

Declaration of Computational Neurosurgery.

Computational neurosurgery is a novel and disruptive field where artificial intelligence and computational modeling are used to improve the diagnosis, treatment, and prognosis of patients affected by diseases of neurosurgical relevance. The field aims to bring new knowledge to clinical neurosciences and inform on the profound questions related to the human brain by applying augmented intelligence, where the power of artificial intelligence and computational inference can enhance human expertise. This transformative field requires the articulation of ethical considerations that will enable scientists, engineers, and clinical neuroscientists, including neurosurgeons, to ensure that the use of such a powerful application is conducted based on the highest moral and ethical standards with a patient-centric approach to predict and prevent mistakes. This declaration is a first attempt to draw a roadmap to guide the application of practical or applied ethics to computational neurosurgery. It is intended for the use of practitioners, ethicists, and scientists using artificial intelligence to understand and treat all the pathophysiological conditions related to the human brain.

A Cooperative Vehicle-Road System for Anomaly Detection on Vehicle Tracks With Augmented Intelligence of Things

A Cooperative Vehicle-Road System for Anomaly Detection on Vehicle Tracks With Augmented Intelligence of Things

Augmented Intelligence of Things for Emergency Vehicle Secure Trajectory Prediction and Task Offloading

Augmented Intelligence of Things for Emergency Vehicle Secure Trajectory Prediction and Task Offloading

Augmented Intelligence Forecasting and What-If-Scenario Analytics With Quantified Uncertainty for Big Real-Time Slope Monitoring Data

Augmented Intelligence Forecasting and What-If-Scenario Analytics With Quantified Uncertainty for Big Real-Time Slope Monitoring Data

Development and Proposal of an Interactive Educational Platform Based on the Concept of Augmented Intelligence

Development and Proposal of an Interactive Educational Platform Based on the Concept of Augmented Intelligence

Computational Neurosurgery: Foundation.

Computational neurosurgery is a novel translational field where computational modeling and artificial intelligence are used to improve diagnosis, treatment, and prognosis of patients affected by diseases of neurosurgical relevance. By laying the foundations of the field, this chapter summarizes the main aspects and implications of artificial intelligence in the clinical neurosciences, with particular emphasis on the necessity to provide an augmented intelligence (AI+) framework to be implemented in modern and future healthcare, aimed to improve the knowledge of the brain, in all its physiopathological spectrum, and to enhance the understanding and treatment of neurological and neurosurgical diseases.

Artificial Intelligence ChatGPT’s Perspective on Implementation of Augmented Intelligence within Orthopaedic Practice—A Comparative Narrative Synthesis?

Artificial Intelligence ChatGPT’s Perspective on Implementation of Augmented Intelligence within Orthopaedic Practice—A Comparative Narrative Synthesis?

The Augmented Intelligence Perspective on Human-in-the-Loop Reinforcement Learning: Review, Concept Designs, and Future Directions

The Augmented Intelligence Perspective on Human-in-the-Loop Reinforcement Learning: Review, Concept Designs, and Future Directions

The Hybrid Augmented Intelligence Open Innovation Platform's Architecture and Scheme, Which Combines Interconnected Virtual and Actual Systems

The Hybrid Augmented Intelligence Open Innovation Platform's Architecture and Scheme, Which Combines Interconnected Virtual and Actual Systems

Augmented Intelligence Based COVID-19 Diagnostics and Deep Feature Categorization Based on Federated Learning

Augmented Intelligence Based COVID-19 Diagnostics and Deep Feature Categorization Based on Federated Learning

Artificial intelligence (AI) or augmented intelligence? How big data and AI are transforming healthcare: Challenges and opportunities.

The sanctity of the doctor-patient relationship is deeply embedded in tradition - the Hippocratic oath, medical ethics, professional codes of conduct, and legislation - all of which are being disrupted by big data and 'artificial' intelligence (AI). The transition from paper-based records to electronic health records, wearables, mobile health applications and mobile phone data has created new opportunities to scale up data collection. Databases of unimaginable magnitude can be harnessed to develop algorithms for AI and to refine machine learning. Complex neural networks now lie at the core of ubiquitous AI systems in healthcare. A transformed healthcare environment enhanced by innovation, robotics, digital technology, and improved diagnostics and therapeutics is plagued by ethical, legal and social challenges. Global guidelines are emerging to ensure governance in AI, but many low- and middle-income countries have yet to develop context- specific frameworks. Legislation must be developed to frame liability and account for negligence due to robotics in the same way human healthcare providers are held accountable. The digital divide between high- and low-income settings is significant and has the potential to exacerbate health inequities globally.

Consistency of convolutional neural networks in dermoscopic melanoma recognition: A prospective real-world study about the pitfalls of augmented intelligence.

Deep-learning convolutional neural networks (CNNs) have outperformed even experienced dermatologists in dermoscopic melanoma detection under controlled conditions. It remains unexplored how real-world dermoscopic image transformations affect CNN robustness. To investigate the consistency of melanoma risk assessment by two commercially available CNNs to help formulate recommendations for current clinical use. A comparative cohort study was conducted from January to July 2022 at the Department of Dermatology, University Hospital Basel. Five dermoscopic images of 116 different lesions on the torso of 66 patients were captured consecutively by the same operator without deliberate rotation. Classification was performed by two CNNs (CNN-1/CNN-2). Lesions were divided into four subgroups based on their initial risk scoring and clinical dignity assessment. Reliability was assessed by variation and intraclass correlation coefficients. Excisions were performed for melanoma suspicion or two consecutively elevated CNN risk scores, and benign lesions were confirmed by expert consensus (n = 3). 117 repeated image series of 116 melanocytic lesions (2 melanomas, 16 dysplastic naevi, 29 naevi, 1 solar lentigo, 1 suspicious and 67 benign) were classified. CNN-1 demonstrated superior measurement repeatability for clinically benign lesions with an initial malignant risk score (mean variation coefficient (mvc): CNN-1: 49.5(±34.3)%; CNN-2: 71.4(±22.5)%; p = 0.03), while CNN-2 outperformed for clinically benign lesions with benign scoring (mvc: CNN-1: 49.7(±22.7)%; CNN-2: 23.8(±29.3)%; p = 0.002). Both systems exhibited lowest score consistency for lesions with an initial malignant risk score and benign assessment. In this context, averaging three initial risk scores achieved highest sensitivity of dignity assessment (CNN-1: 94%; CNN-2: 89%). Intraclass correlation coefficients indicated 'moderate'-to-'good' reliability for both systems (CNN-1: 0.80, 95% CI:0.71-0.87, p < 0.001; CNN-2: 0.67, 95% CI:0.55-0.77, p < 0.001). Potential user-induced image changes can significantly influence CNN classification. For clinical application, we recommend using the average of three initial risk scores. Furthermore, we advocate for CNN robustness optimization by cross-validation with repeated image sets. ClinicalTrials.gov (NCT04605822).

Atopic dermatitis: A global health perspective.

The International Society of AD (ISAD) organized a roundtable on global aspects of AD at the WCD 2023 in Singapore. According to the Global Burden of Disease (GBD) consortium, at least 171 million individuals were affected with AD in 2019, corresponding to 2.23% of the world population, with age-standardized prevalence and incidence rates that were relatively stable from 1990 to 2019. Based on the panel experience, most AD cases are mild-to-moderate. Without parallel data on disease prevalence and severity, the GBD data are difficult to interpret in many regions. This gap is particularly important in countries with limited medical infrastructure, but indirect evidence suggests a significant burden of AD in low-and-medium resource settings, especially urban areas. The Singapore roundtable was an opportunity to compare experiences in World Bank category 1 (Madagascar and Mali), 3 (Brazil, China) and 4 (Australia, Germany, Qatar, USA, Singapore, Japan) countries. The panel concluded that current AD guidelines are not adapted for low resource settings and a more pragmatic approach, as developed by WHO for skin NTDs, would be advisable for minimal access to moisturizers and topical corticosteroids. The panel also recommended prioritizing prevention studies, regardless of the level of existing resources. For disease long-term control in World Bank category 3 and most category 4 countries, the main problem is not access to drugs for most mild-to-moderate cases, but rather poor compliance due to insufficient time at visits. Collaboration with WHO, patient advocacy groups and industry may promote global change, improve capacity training and fight current inequalities. Finally, optimizing management of AD and its comorbidities needs more action at the primary care level, because reaching specialist care is merely aspirational in most settings. Primary care empowerment with store and forward telemedicine and algorithms based on augmented intelligence is a future goal.

Mixed reality-based online 3D pallet loading problem to achieve augmented intelligence in e-fulfilment processes

Mixed reality-based online 3D pallet loading problem to achieve augmented intelligence in e-fulfilment processes

Artificial Intelligence or Augmented Intelligence?

Artificial Intelligence or Augmented Intelligence?

Artificial intelligence and radical uncertainty

AbstractArtificial intelligence (AI) offers new possibilities to augment human decision‐making under radical uncertainty. This viewpoint commentary explores how AI can relax limits of bounded rationality. It offers a framework for analyzing how AI can support human decision‐makers confronted with deep uncertainty by bolstering key decision‐making sub‐processes. Specifically, AI can help set agendas by scanning environments, formulate problems by providing contextual insights, identify creative alternatives through combinatorial abilities, select options by modelling scenarios and enable rapid experimentation cycles. Connecting the role of AI with the contributions in this special issue, this viewpoint commentary concludes by outlining directions for future research regarding the function of AI and augmented human intelligence in decision‐making under conditions of radical uncertainty.

AITel: eHealth Augmented-Intelligence-Based Telemedicine Resource Recommendation Framework for IoT Devices in Smart Cities

Telemedicine was introduced on connected IoT based resource networking infrastructure. With technological evolution and artificial intelligence, the framework has expanded the grips beyond connecting remote patients. Augmented Intelligence (AuI) adds value to current telemedicine framework by coordinating with resource management pools of IoT devices and communication channels to provide a reliable enterprise ecosystem of smart cities. In this article, a novel approach for resource recommendation in telemedicine via AuI and IoT is proposed. The framework acquires data from the existing eHealth infrastructure of smart cities and telemedicine environment of IoT to provide an intelligent recommendation based on telemedicine services. The proposed framework is based on smart Enterprise Management System (EMS) for eHealth services. The proposed Augmented Intelligent Telemedicine (AITel) framework postulates the 94.83% accuracy from augmented intelligence assisted telemedicine and would create a reliable ecosystem on resource recommendations to build a resilient healthcare system for remote patients, communities and healthcare infrastructure.

FOG-Empowered Augmented-Intelligence-Based Proactive Defensive Mechanism for IoT-Enabled Smart Industries

The recent innovations in the network communication domain have entirely revolutionized the conventional industrial sector by introducing a new era of automatic communication. The Industrial Internet of Things (IIoT) is acknowledged as an exclusive space where IoT seems to divulge expressive manifestations. However, the expanded connectivity, more openness, and the widespread use of low-power communication devices of IIoT makes them vulnerable to malicious attacks, and criminal activities. Moreover, the heterogeneous and prevalent nature of the IIoT devices makes it very difficult to come up with a centralized threat detection mechanism, thus its security remains a major concern. Motivated by this, we propose a fog-empowered Augmented Intelligence (IA)-based defensive mechanism to ensure secure communication in IoT-enabled smart industries. The designed framework incorporates the aggregated potentials of two highly acclaimed Deep Learning (DL) classifiers: Gated Recurrent Unit (GRU) and Bidirectional Long Short-Term Memory (BiLSTM). Where the DL-based scheme detects anomalies in such industrial networks and the FOG-based scenario promotes the routing flexibility and interoperability of the heterogeneous devices of the IoT-enabled smart industrial network. The validity of the proposed mechanism is analytically investigated by conducting a comparison with the benchmark threat detection techniques. The proposed solution is also generically examined parallel to some state-of-the-art approaches. The Cu-GRU-BiLSTM framework achieved up to 99.91% accuracy with a low false alarm rate and outperforms the baseline and recent threat detection approaches. The simulation and comparison results validate the effectiveness of the proposed mechanism and advocate it as a phenomenal choice to ensure efficacious and secure communication in IoT-based smart industries.

Unlocking the potential of augmented intelligence: a discussion on its role in boardroom decision-making

Unlocking the potential of augmented intelligence: a discussion on its role in boardroom decision-making