Technology Integration in Healthcare and Communication: A Source Guide

Frame the question

This guide examines how artificial intelligence, 6G wireless networks, and deep learning are reshaping technology landscapes. The anchor source [1] explores AI's role in healthcare, while [2] discusses 6G's dual-function networks. [3] addresses machine learning adaptation challenges, and [4] reviews deep learning's impact on computer vision. Together, these sources form a framework for understanding technological innovation across domains.

What the evidence shows

The complexity and rise of data in healthcare mean AI will increasingly be applied within the field [1]. Integrated Sensing and Communications (ISAC) is emerging as a key feature of 6G Radio Access Networks (RAN), enabling dense cell infrastructures to create perceptive networks [2]. Concept drift adaptation involves managing changes in input-output relationships over time, requiring dynamic machine learning strategies [3]. Deep learning methods have surpassed traditional techniques in computer vision, with applications in object detection and human pose estimation [4].

Follow the source trail

Source [1] establishes AI's potential in healthcare, while [2] expands this to wireless communication systems. [3] provides a foundational understanding of machine learning challenges that apply to AI healthcare applications. [4] complements [1] by showing how deep learning techniques could enhance diagnostic systems. The interplay between these sources reveals technological convergence: AI in healthcare (1) intersects with 6G's sensing capabilities (2), while concept drift adaptation (3) and deep learning (4) address the technical challenges of implementing these systems.

Use these sources well

For an essay on technological innovation, begin with [1] to establish AI's healthcare applications. Use [2] to contrast this with 6G's communication advancements, highlighting how both rely on data processing. Incorporate [3] to discuss the limitations of AI systems, then use [4] to show how deep learning mitigates these challenges. Structure the conclusion by comparing the scalability of 6G networks (2) with the ethical concerns of AI in healthcare (1).

What to search next

How might ISAC's dual-function capabilities (2) address the implementation barriers faced by AI in healthcare (1)? What role could concept drift adaptation (3) play in maintaining the accuracy of deep learning models (4) used for medical diagnostics? How do the ethical concerns raised in [1] compare to the technical challenges in [3] and [4]? What are the implications of 6G's perceptive networks (2) for future healthcare applications?

Verbatim source abstracts

[1] The potential for artificial intelligence in healthcare — Future Healthcare Journal, 2019-06-01, doi:10.7861/futurehosp.6-2-94

The complexity and rise of data in healthcare means that artificial intelligence (AI) will increasingly be applied within the field. Several types of AI are already being employed by payers and providers of care, and life sciences companies. The key categories of applications involve diagnosis and treatment recommendations, patient engagement and adherence, and administrative activities. Although there are many instances in which AI can perform healthcare tasks as well or better than humans, implementation factors will prevent large-scale automation of healthcare professional jobs for a considerable period. Ethical issues in the application of AI to healthcare are also discussed. [1]

[2] Integrated Sensing and Communications: Toward Dual-Functional Wireless Networks for 6G and Beyond — IEEE Journal on Selected Areas in Communications, 2022-03-17, doi:10.1109/jsac.2022.3156632

As the standardization of 5G solidifies, researchers are speculating what 6G will be. The integration of sensing functionality is emerging as a key feature of the 6G Radio Access Network (RAN), allowing for the exploitation of dense cell infrastructures to construct a perceptive network. In this IEEE Journal on Selected Areas in Communications (JSAC) Special Issue overview, we provide a comprehensive review on the background, range of key applications and state-of-the-art approaches of Integrated Sensing and Communications (ISAC). We commence by discussing the interplay between sensing and communications (S&C) from a historical point of view, and then consider the multiple facets of ISAC and the resulting performance gains. By introducing both ongoing and potential use cases, we shed light on the industrial progress and standardization activities related to ISAC. We analyze a number of performance tradeoffs between S&C, spanning from information theoretical limits to physical layer performance tradeoffs, and the cross-layer design tradeoffs. Next, we discuss the signal processing aspects of ISAC, namely ISAC waveform design and receive signal processing. As a step further, we provide our vision on the deeper integration between S&C within the framework of perceptive networks, where the two functionalities are expected to mutually assist each other, i.e., via communication-assisted sensing and sensing-assisted communications. Finally, we identify the potential integration of ISAC with other emerging communication technologies, and their positive impacts on the future of wireless networks. [2]

[3] A survey on concept drift adaptation — ACM Computing Surveys, 2014-03-01, doi:10.1145/2523813

Concept drift primarily refers to an online supervised learning scenario when the relation between the input data and the target variable changes over time. Assuming a general knowledge of supervised learning in this article, we characterize adaptive learning processes; categorize existing strategies for handling concept drift; overview the most representative, distinct, and popular techniques and algorithms; discuss evaluation methodology of adaptive algorithms; and present a set of illustrative applications. The survey covers the different facets of concept drift in an integrated way to reflect on the existing scattered state of the art. Thus, it aims at providing a comprehensive introduction to the concept drift adaptation for researchers, industry analysts, and practitioners. [3]

[4] Deep Learning for Computer Vision: A Brief Review — Computational Intelligence and Neuroscience, 2018-01-01, doi:10.1155/2018/7068349

Over the last years deep learning methods have been shown to outperform previous state-of-the-art machine learning techniques in several fields, with computer vision being one of the most prominent cases. This review paper provides a brief overview of some of the most significant deep learning schemes used in computer vision problems, that is, Convolutional Neural Networks, Deep Boltzmann Machines and Deep Belief Networks, and Stacked Denoising Autoencoders. A brief account of their history, structure, advantages, and limitations is given, followed by a description of their applications in various computer vision tasks, such as object detection, face recognition, action and activity recognition, and human pose estimation. Finally, a brief overview is given of future directions in designing deep learning schemes for computer vision problems and the challenges involved therein. [4]

Source dossiers

Reference cards for every cited source, using only verified record metadata.

[1] The potential for artificial intelligence in healthcare

• Authors: Thomas H. Davenport, Ravi Kalakota
• Venue: Future Healthcare Journal
• Published: 2019-06-01
• DOI: 10.7861/futurehosp.6-2-94
• Citation count: 3,806
• Institutions: Babson College; Deloitte (United States)
• Topics: Artificial Intelligence in Healthcare and Education, Machine Learning in Healthcare, Clinical Reasoning and Diagnostic Skills, Health care, Key (lock), Field (mathematics), Automation, Scale (ratio), Applications of artificial intelligence, Computer science
• License/access: open access — license unspecified (open access)
• Record: https://doi.org/10.7861/futurehosp.6-2-94
• Abstract (verbatim): "The complexity and rise of data in healthcare means that artificial intelligence (AI) will increasingly be applied within the field. Several types of AI are already being employed by payers and providers of care, and life sciences companies. The key categories of applications involve diagnosis and treatment recommendations, patient engagement and adherence, and administrative activities. Although there are many instances in which AI can perform healthcare tasks as well or better than humans, implementation factors will prevent large-scale automation of healthcare professional jobs for a considerable period. Ethical issues in the application of AI to healthcare are also discussed." [1]

[2] Integrated Sensing and Communications: Toward Dual-Functional Wireless Networks for 6G and Beyond

• Authors: Fan Liu, Yuanhao Cui, Christos Masouros, Jie Xu, Tony Xiao Han, Yonina C. Eldar, Stefano Buzzi
• Venue: IEEE Journal on Selected Areas in Communications
• Published: 2022-03-17
• DOI: 10.1109/jsac.2022.3156632
• Citation count: 3,557
• Institutions: Southern University of Science and Technology; Beijing University of Posts and Telecommunications; University College London; Chinese University of Hong Kong, Shenzhen; Huawei Technologies (China); Weizmann Institute of Science; Università degli studi di Cassino e del Lazio Meridionale; Consorzio Nazionale Interuniversitario per le Telecomunicazioni
• Topics: Indoor and Outdoor Localization Technologies, Advanced Wireless Communication Technologies, Full-Duplex Wireless Communications, Computer science, Standardization, Physical layer, Telecommunications, Key (lock), Wireless, Wireless network
• License/access: cc-by (open access)
• Record: https://doi.org/10.1109/jsac.2022.3156632
• Abstract (verbatim): "As the standardization of 5G solidifies, researchers are speculating what 6G will be. The integration of sensing functionality is emerging as a key feature of the 6G Radio Access Network (RAN), allowing for the exploitation of dense cell infrastructures to construct a perceptive network. In this IEEE Journal on Selected Areas in Communications (JSAC) Special Issue overview, we provide a comprehensive review on the background, range of key applications and state-of-the-art approaches of Integrated Sensing and Communications (ISAC). We commence by discussing the interplay between sensing and communications (S&C) from a historical point of view, and then consider the multiple facets of ISAC and the resulting performance gains. By introducing both ongoing and potential use cases, we shed light on the industrial progress and standardization activities related to ISAC. We analyze a number of performance tradeoffs between S&C, spanning from information theoretical limits to physical layer performance tradeoffs, and the cross-layer design tradeoffs. Next, we discuss the signal processing aspects of ISAC, namely ISAC waveform design and receive signal processing. As a step further, we provide our vision on the deeper integration between S&C within the framework of perceptive networks, where the two functionalities are expected to mutually assist each other, i.e., via communication-assisted sensing and sensing-assisted communications. Finally, we identify the potential integration of ISAC with other emerging communication technologies, and their positive impacts on the future of wireless networks." [2]

[3] A survey on concept drift adaptation

• Authors: João Gama, Indrė Žliobaitė, Albert Bifet, Mykola Pechenizkiy, Abdelhamid Bouchachia
• Venue: ACM Computing Surveys
• Published: 2014-03-01
• DOI: 10.1145/2523813
• Citation count: 3,549
• Institutions: Universidade do Porto; Aalto University; Yahoo (Spain); Eindhoven University of Technology; Bournemouth University
• Topics: Data Stream Mining Techniques, Spam and Phishing Detection, Innovative Microfluidic and Catalytic Techniques Innovation, Concept drift, Computer science, Categorization, Adaptation (eye), Relation (database), Set (abstract data type), Machine learning
• License/access: open access — license unspecified (open access)
• Record: https://doi.org/10.1145/2523813
• Abstract (verbatim): "Concept drift primarily refers to an online supervised learning scenario when the relation between the input data and the target variable changes over time. Assuming a general knowledge of supervised learning in this article, we characterize adaptive learning processes; categorize existing strategies for handling concept drift; overview the most representative, distinct, and popular techniques and algorithms; discuss evaluation methodology of adaptive algorithms; and present a set of illustrative applications. The survey covers the different facets of concept drift in an integrated way to reflect on the existing scattered state of the art. Thus, it aims at providing a comprehensive introduction to the concept drift adaptation for researchers, industry analysts, and practitioners." [3]

[4] Deep Learning for Computer Vision: A Brief Review

• Authors: Athanasios Voulodimos, Nikolaos Doulamis, Anastasios Doulamis, Eftychios Protopapadakis
• Venue: Computational Intelligence and Neuroscience
• Published: 2018-01-01
• DOI: 10.1155/2018/7068349
• Citation count: 3,373
• Institutions: National Technical University of Athens; Technological Educational Institute of Athens
• Topics: Advanced Neural Network Applications, Video Surveillance and Tracking Methods, Human Pose and Action Recognition, Artificial intelligence, Deep learning, Computer science, Boltzmann machine, Convolutional neural network, Machine learning, Restricted Boltzmann machine
• License/access: cc-by (open access)
• Record: https://doi.org/10.1155/2018/7068349
• Abstract (verbatim): "Over the last years deep learning methods have been shown to outperform previous state-of-the-art machine learning techniques in several fields, with computer vision being one of the most prominent cases. This review paper provides a brief overview of some of the most significant deep learning schemes used in computer vision problems, that is, Convolutional Neural Networks, Deep Boltzmann Machines and Deep Belief Networks, and Stacked Denoising Autoencoders. A brief account of their history, structure, advantages, and limitations is given, followed by a description of their applications in various computer vision tasks, such as object detection, face recognition, action and activity recognition, and human pose estimation. Finally, a brief overview is given of future directions in designing deep learning schemes for computer vision problems and the challenges involved therein." [4]