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Smart Healthcare Systems Based on the Internet of Things and Artificial Intelligence

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A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Intelligent Sensors".

Deadline for manuscript submissions: closed (31 December 2022) | Viewed by 48585

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Special Issue Editors

Dr. Hemant Ghayvat

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Guest Editor

Computer Science and Media Technology Department, Linnaeus University, 392 34 Växjö, Sweden
Interests: ambient assisted living; preventive healthcare monitoring; smart home and health informatics
Special Issues, Collections and Topics in MDPI journals

Dr. Sharnil Pandya

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Guest Editor

Department of Computer Science, Symbiosis Institute of Technology, Symbiosis International University, Pune, India
Interests: smart sensing; health informatics; acoustics health; block chaining
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Public healthcare informatics and its management is a critical issue for the wellbeing of the public healthcare community. It is imperative that public welfare organizations and the available technologies must acquire, manage, process, and analyze healthcare information of various healthcare disease patients such as dementia/Parkinson’s, Alzheimer’s, cardiac arrest, etc. In recent times, there is an immediate need to design and develop healthcare frameworks, public healthcare systems, and quality healthcare services to offer real-time updates related to patient conditions and medical reports to the doctors and caretakers. To advance the concept of smart public healthcare, there is a need to propose fog and edge computing-based medical tools which can collect health sensor data, process them, analyze them, and integrate them with intelligent healthcare applications.

However, the development of fog computing/edge computing-based public healthcare tools is a challenging task. Additionally, numerous challenges are present in designing and developing intelligent healthcare tools; there is an immediate need to address this issue by proposing innovative public healthcare frameworks and smart public healthcare tools which can offer computation and processing of medical sensor data, migrate the required tasks at the edge level, and meet the current demands of intelligent healthcare setups.

This Special Issue aims to carry out recent advances in the Internet of Medical Things, Artificial Intelligence of Medical Things, consolidate them for public healthcare analytics, and do rigorous research in public healthcare applications and tools.

Topics of interest include but are not limited to the following:

Design and development of smart public healthcare tools;
Fog and edge computing-based healthcare frameworks;
Design and development of healthcare tools to perform medical diagnosis using techniques such as medical imaging;
Application of big data in healthcare;
Multimodal data collection and sensor-fusion based analysis of healthcare sensor data;
AI-based healthcare diagnosis and prevention;
Public healthcare systems and frameworks;
Using data analytics in public healthcare for safety and security;
Assessment of public healthcare data analytics;
Innovative ideas related to public healthcare management and safety;
Discussion of use cases related to public healthcare data analytics;
Latest trends and technologies in the IoMT and AIoMT area;
Fog, mist, edge, and cloud computing based healthcare frameworks for AIoMT.

Dr. Hemant Ghayvat
Dr. Sharnil Pandya
Guest Editors

Manuscript Submission Information

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Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

Internet of Medical Things
Artificial Intelligence of Medical Things
Ambient Intelligence
Ambient assistive systems
Ambient healthcare frameworks
Big data
Multimodal data
Sensor fusion
Preventive healthcare
Ubiquitous healthcare systems
Fog computing
Healthcare diagnosis
Edge computing
Cloud computing
Healthcare services
Acoustic classification

Published Papers (11 papers)

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Research

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23 pages, 2607 KiB

Open AccessArticle

Secure Bluetooth Communication in Smart Healthcare Systems: A Novel Community Dataset and Intrusion Detection System

by Mohammed Zubair, Ali Ghubaish, Devrim Unal, Abdulla Al-Ali, Thomas Reimann, Guillaume Alinier, Mohammad Hammoudeh and Junaid Qadir

Sensors 2022, 22(21), 8280; https://doi.org/10.3390/s22218280 - 28 Oct 2022

Cited by 9 | Viewed by 3252

Abstract

Smart health presents an ever-expanding attack surface due to the continuous adoption of a broad variety of Internet of Medical Things (IoMT) devices and applications. IoMT is a common approach to smart city solutions that deliver long-term benefits to critical infrastructures, such as smart healthcare. Many of the IoMT devices in smart cities use Bluetooth technology for short-range communication due to its flexibility, low resource consumption, and flexibility. As smart healthcare applications rely on distributed control optimization, artificial intelligence (AI) and deep learning (DL) offer effective approaches to mitigate cyber-attacks. This paper presents a decentralized, predictive, DL-based process to autonomously detect and block malicious traffic and provide an end-to-end defense against network attacks in IoMT devices. Furthermore, we provide the BlueTack dataset for Bluetooth-based attacks against IoMT networks. To the best of our knowledge, this is the first intrusion detection dataset for Bluetooth classic and Bluetooth low energy (BLE). Using the BlueTack dataset, we devised a multi-layer intrusion detection method that uses deep-learning techniques. We propose a decentralized architecture for deploying this intrusion detection system on the edge nodes of a smart healthcare system that may be deployed in a smart city. The presented multi-layer intrusion detection models achieve performances in the range of 97–99.5% based on the F1 scores. Full article

(This article belongs to the Special Issue Smart Healthcare Systems Based on the Internet of Things and Artificial Intelligence)

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12 pages, 282 KiB

Open AccessArticle

Association between Self-Reported Prior Night’s Sleep and Single-Task Gait in Healthy, Young Adults: A Study Using Machine Learning

by Ali Boolani, Joel Martin, Haikun Huang, Lap-Fai Yu, Maggie Stark, Zachary Grin, Marissa Roy, Chelsea Yager, Seema Teymouri, Dylan Bradley, Rebecca Martin, George Fulk and Rumit Singh Kakar

Sensors 2022, 22(19), 7406; https://doi.org/10.3390/s22197406 - 29 Sep 2022

Cited by 1 | Viewed by 1242

Abstract

Failure to obtain the recommended 7–9 h of sleep has been associated with injuries in youth and adults. However, most research on the influence of prior night’s sleep and gait has been conducted on older adults and clinical populations. Therefore, the objective of this study was to identify individuals who experience partial sleep deprivation and/or sleep extension the prior night using single task gait. Participants (n = 123, age 24.3 ± 4.0 years; 65% female) agreed to participate in this study. Self-reported sleep duration of the night prior to testing was collected. Gait data was collected with inertial sensors during a 2 min walk test. Group differences (<7 h and >9 h, poor sleepers; 7–9 h, good sleepers) in gait characteristics were assessed using machine learning and a post-hoc ANCOVA. Results indicated a correlation (r = 0.79) between gait parameters and prior night’s sleep. The most accurate machine learning model was a Random Forest Classifier using the top 9 features, which had a mean accuracy of 65.03%. Our findings suggest that good sleepers had more asymmetrical gait patterns and were better at maintaining gait speed than poor sleepers. Further research with larger subject sizes is needed to develop more accurate machine learning models to identify prior night’s sleep using single-task gait. Full article

(This article belongs to the Special Issue Smart Healthcare Systems Based on the Internet of Things and Artificial Intelligence)

25 pages, 6067 KiB

Open AccessArticle

DBGC: Dimension-Based Generic Convolution Block for Object Recognition

by Chirag Patel, Dulari Bhatt, Urvashi Sharma, Radhika Patel, Sharnil Pandya, Kirit Modi, Nagaraj Cholli, Akash Patel, Urvi Bhatt, Muhammad Ahmed Khan, Shubhankar Majumdar, Mohd Zuhair, Khushi Patel, Syed Aziz Shah and Hemant Ghayvat

Sensors 2022, 22(5), 1780; https://doi.org/10.3390/s22051780 - 24 Feb 2022

Cited by 33 | Viewed by 2696

Abstract

The object recognition concept is being widely used a result of increasing CCTV surveillance and the need for automatic object or activity detection from images or video. Increases in the use of various sensor networks have also raised the need of lightweight process frameworks. Much research has been carried out in this area, but the research scope is colossal as it deals with open-ended problems such as being able to achieve high accuracy in little time using lightweight process frameworks. Convolution Neural Networks and their variants are widely used in various computer vision activities, but most of the architectures of CNN are application-specific. There is always a need for generic architectures with better performance. This paper introduces the Dimension-Based Generic Convolution Block (DBGC), which can be used with any CNN to make the architecture generic and provide a dimension-wise selection of various height, width, and depth kernels. This single unit which uses the separable convolution concept provides multiple combinations using various dimension-based kernels. This single unit can be used for height-based, width-based, or depth-based dimensions; the same unit can even be used for height and width, width and depth, and depth and height dimensions. It can also be used for combinations involving all three dimensions of height, width, and depth. The main novelty of DBGC lies in the dimension selector block included in the proposed architecture. Proposed unoptimized kernel dimensions reduce FLOPs by around one third and also reduce the accuracy by around one half; semi-optimized kernel dimensions yield almost the same or higher accuracy with half the FLOPs of the original architecture, while optimized kernel dimensions provide 5 to 6% higher accuracy with around a 10 M reduction in FLOPs. Full article

(This article belongs to the Special Issue Smart Healthcare Systems Based on the Internet of Things and Artificial Intelligence)

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19 pages, 3905 KiB

Open AccessArticle

FL-PMI: Federated Learning-Based Person Movement Identification through Wearable Devices in Smart Healthcare Systems

by K. S. Arikumar, Sahaya Beni Prathiba, Mamoun Alazab, Thippa Reddy Gadekallu, Sharnil Pandya, Javed Masood Khan and Rajalakshmi Shenbaga Moorthy

Sensors 2022, 22(4), 1377; https://doi.org/10.3390/s22041377 - 11 Feb 2022

Cited by 62 | Viewed by 5099

Abstract

Recent technological developments, such as the Internet of Things (IoT), artificial intelligence, edge, and cloud computing, have paved the way in transforming traditional healthcare systems into smart healthcare (SHC) systems. SHC escalates healthcare management with increased efficiency, convenience, and personalization, via use of wearable devices and connectivity, to access information with rapid responses. Wearable devices are equipped with multiple sensors to identify a person’s movements. The unlabeled data acquired from these sensors are directly trained in the cloud servers, which require vast memory and high computational costs. To overcome this limitation in SHC, we propose a federated learning-based person movement identification (FL-PMI). The deep reinforcement learning (DRL) framework is leveraged in FL-PMI for auto-labeling the unlabeled data. The data are then trained using federated learning (FL), in which the edge servers allow the parameters alone to pass on the cloud, rather than passing vast amounts of sensor data. Finally, the bidirectional long short-term memory (BiLSTM) in FL-PMI classifies the data for various processes associated with the SHC. The simulation results proved the efficiency of FL-PMI, with 99.67% accuracy scores, minimized memory usage and computational costs, and reduced transmission data by 36.73%. Full article

(This article belongs to the Special Issue Smart Healthcare Systems Based on the Internet of Things and Artificial Intelligence)

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15 pages, 6874 KiB

Open AccessArticle

End-User Skin Analysis (Moles) through Image Acquisition and Processing System

by Lorant Andras Szolga, Denisa Alice Bozga and Camelia Florea

Sensors 2022, 22(3), 1123; https://doi.org/10.3390/s22031123 - 01 Feb 2022

Viewed by 2191

Abstract

Skin moles and lesions can be the first signs of severe skin diseases such as cancer. This paper presents the development of an end-user device capable of capturing images, segmentation and diagnosis of moles by using the ABCD rule, which stands for analyzing moles’ parameters as: asymmetry, border, color, and diameter. These are the main mole characteristics that doctors look at, each of them having a different factor of importance, and depending on these an accurate diagnosis can be given. For the hardware, we developed a small and compact device that can be manipulated easily by anyone without knowledge of medicine, in which we considered a custom-designed 3D enclosure with two white LEDs to control the light. The device has the role of facilitating analysis of the suspicious moles regularly at home, even if only from an indicative and not from a medical point of view. The developed PC software permits the storage of the images in a local database for easy tracking and analysis in time. The image processing developed for the ABCD rule is incorporated into the PC software and tested extensively on the international PH2 database with skin melanoma images to validate our segmentation and criteria evaluation. Using the developed device, we captured mole images for patients, who also took a medical examination by a specialist using the standard dermatoscope. Therefore, we obtained our own database containing 26 images for which we have also the specialists’ diagnosis. The performance evaluation measures obtained using our device are—Accuracy: 0.92, Precision: 1.0, Recall: 0.92, F1-score: 0.96. Full article

(This article belongs to the Special Issue Smart Healthcare Systems Based on the Internet of Things and Artificial Intelligence)

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16 pages, 1218 KiB

Open AccessArticle

Secure Smart Wearable Computing through Artificial Intelligence-Enabled Internet of Things and Cyber-Physical Systems for Health Monitoring

by Lakshmana Kumar Ramasamy, Firoz Khan, Mohammad Shah, Balusupati Veera Venkata Siva Prasad, Celestine Iwendi and Cresantus Biamba

Sensors 2022, 22(3), 1076; https://doi.org/10.3390/s22031076 - 29 Jan 2022

Cited by 39 | Viewed by 5648

Abstract

The functionality of the Internet is continually changing from the Internet of Computers (IoC) to the “Internet of Things (IoT)”. Most connected systems, called Cyber-Physical Systems (CPS), are formed from the integration of numerous features such as humans and the physical environment, smart objects, and embedded devices and infrastructure. There are a few critical problems, such as security risks and ethical issues that could affect the IoT and CPS. When every piece of data and device is connected and obtainable on the network, hackers can obtain it and utilise it for different scams. In medical healthcare IoT-CPS, everyday medical and physical data of a patient may be gathered through wearable sensors. This paper proposes an AI-enabled IoT-CPS which doctors can utilise to discover diseases in patients based on AI. AI was created to find a few disorders such as Diabetes, Heart disease and Gait disturbances. Each disease has various symptoms among patients or elderly. Dataset is retrieved from the Kaggle repository to execute AI-enabled IoT-CPS technology. For the classification, AI-enabled IoT-CPS Algorithm is used to discover diseases. The experimental results demonstrate that compared with existing algorithms, the proposed AI-enabled IoT-CPS algorithm detects patient diseases and fall events in elderly more efficiently in terms of Accuracy, Precision, Recall and F-measure. Full article

(This article belongs to the Special Issue Smart Healthcare Systems Based on the Internet of Things and Artificial Intelligence)

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30 pages, 13280 KiB

Open AccessArticle

Novel Privacy Preserving Non-Invasive Sensing-Based Diagnoses of Pneumonia Disease Leveraging Deep Network Model

by Mujeeb Ur Rehman, Arslan Shafique, Kashif Hesham Khan, Sohail Khalid, Abdullah Alhumaidi Alotaibi, Turke Althobaiti, Naeem Ramzan, Jawad Ahmad, Syed Aziz Shah and Qammer H. Abbasi

Sensors 2022, 22(2), 461; https://doi.org/10.3390/s22020461 - 08 Jan 2022

Cited by 12 | Viewed by 2629

Abstract

This article presents non-invasive sensing-based diagnoses of pneumonia disease, exploiting a deep learning model to make the technique non-invasive coupled with security preservation. Sensing and securing healthcare and medical images such as X-rays that can be used to diagnose viral diseases such as pneumonia is a challenging task for researchers. In the past few years, patients’ medical records have been shared using various wireless technologies. The wireless transmitted data are prone to attacks, resulting in the misuse of patients’ medical records. Therefore, it is important to secure medical data, which are in the form of images. The proposed work is divided into two sections: in the first section, primary data in the form of images are encrypted using the proposed technique based on chaos and convolution neural network. Furthermore, multiple chaotic maps are incorporated to create a random number generator, and the generated random sequence is used for pixel permutation and substitution. In the second part of the proposed work, a new technique for pneumonia diagnosis using deep learning, in which X-ray images are used as a dataset, is proposed. Several physiological features such as cough, fever, chest pain, flu, low energy, sweating, shaking, chills, shortness of breath, fatigue, loss of appetite, and headache and statistical features such as entropy, correlation, contrast dissimilarity, etc., are extracted from the X-ray images for the pneumonia diagnosis. Moreover, machine learning algorithms such as support vector machines, decision trees, random forests, and naive Bayes are also implemented for the proposed model and compared with the proposed CNN-based model. Furthermore, to improve the CNN-based proposed model, transfer learning and fine tuning are also incorporated. It is found that CNN performs better than other machine learning algorithms as the accuracy of the proposed work when using naive Bayes and CNN is 89% and 97%, respectively, which is also greater than the average accuracy of the existing schemes, which is 90%. Further, K-fold analysis and voting techniques are also incorporated to improve the accuracy of the proposed model. Different metrics such as entropy, correlation, contrast, and energy are used to gauge the performance of the proposed encryption technology, while precision, recall, F1 score, and support are used to evaluate the effectiveness of the proposed machine learning-based model for pneumonia diagnosis. The entropy and correlation of the proposed work are 7.999 and 0.0001, respectively, which reflects that the proposed encryption algorithm offers a higher security of the digital data. Moreover, a detailed comparison with the existing work is also made and reveals that both the proposed models work better than the existing work. Full article

(This article belongs to the Special Issue Smart Healthcare Systems Based on the Internet of Things and Artificial Intelligence)

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16 pages, 3974 KiB

Open AccessArticle

Deep Learning-Based Transfer Learning for Classification of Skin Cancer

by Satin Jain, Udit Singhania, Balakrushna Tripathy, Emad Abouel Nasr, Mohamed K. Aboudaif and Ali K. Kamrani

Sensors 2021, 21(23), 8142; https://doi.org/10.3390/s21238142 - 06 Dec 2021

Cited by 40 | Viewed by 7927

Abstract

One of the major health concerns for human society is skin cancer. When the pigments producing skin color turn carcinogenic, this disease gets contracted. A skin cancer diagnosis is a challenging process for dermatologists as many skin cancer pigments may appear similar in appearance. Hence, early detection of lesions (which form the base of skin cancer) is definitely critical and useful to completely cure the patients suffering from skin cancer. Significant progress has been made in developing automated tools for the diagnosis of skin cancer to assist dermatologists. The worldwide acceptance of artificial intelligence-supported tools has permitted usage of the enormous collection of images of lesions and benevolent sores approved by histopathology. This paper performs a comparative analysis of six different transfer learning nets for multi-class skin cancer classification by taking the HAM10000 dataset. We used replication of images of classes with low frequencies to counter the imbalance in the dataset. The transfer learning nets that were used in the analysis were VGG19, InceptionV3, InceptionResNetV2, ResNet50, Xception, and MobileNet. Results demonstrate that replication is suitable for this task, achieving high classification accuracies and F-measures with lower false negatives. It is inferred that Xception Net outperforms the rest of the transfer learning nets used for the study, with an accuracy of 90.48. It also has the highest recall, precision, and F-Measure values. Full article

(This article belongs to the Special Issue Smart Healthcare Systems Based on the Internet of Things and Artificial Intelligence)

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22 pages, 5637 KiB

Open AccessArticle

Memcached: An Experimental Study of DDoS Attacks for the Wellbeing of IoT Applications

by Nivedita Mishra, Sharnil Pandya, Chirag Patel, Nagaraj Cholli, Kirit Modi, Pooja Shah, Madhuri Chopade, Sudha Patel and Ketan Kotecha

Sensors 2021, 21(23), 8071; https://doi.org/10.3390/s21238071 - 02 Dec 2021

Cited by 3 | Viewed by 3100

Abstract

Distributed denial-of-service (DDoS) attacks are significant threats to the cyber world because of their potential to quickly bring down victims. Memcached vulnerabilities have been targeted by attackers using DDoS amplification attacks. GitHub and Arbor Networks were the victims of Memcached DDoS attacks with 1.3 Tbps and 1.8 Tbps attack strengths, respectively. The bandwidth amplification factor of nearly 50,000 makes Memcached the deadliest DDoS attack vector to date. In recent times, fellow researchers have made specific efforts to analyze and evaluate Memcached vulnerabilities; however, the solutions provided for security are based on best practices by users and service providers. This study is the first attempt at modifying the architecture of Memcached servers in the context of improving security against DDoS attacks. This study discusses the Memcached protocol, the vulnerabilities associated with it, the future challenges for different IoT applications associated with caches, and the solutions for detecting Memcached DDoS attacks. The proposed solution is a novel identification-pattern mechanism using a threshold scheme for detecting volume-based DDoS attacks. In the undertaken study, the solution acts as a pre-emptive measure for detecting DDoS attacks while maintaining low latency and high throughput. Full article

(This article belongs to the Special Issue Smart Healthcare Systems Based on the Internet of Things and Artificial Intelligence)

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Review

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35 pages, 1074 KiB

Open AccessReview

Review on the Evaluation and Development of Artificial Intelligence for COVID-19 Containment

by Md. Mahadi Hasan, Muhammad Usama Islam, Muhammad Jafar Sadeq, Wai-Keung Fung and Jasim Uddin

Sensors 2023, 23(1), 527; https://doi.org/10.3390/s23010527 - 03 Jan 2023

Cited by 24 | Viewed by 6435

Abstract

Artificial intelligence has significantly enhanced the research paradigm and spectrum with a substantiated promise of continuous applicability in the real world domain. Artificial intelligence, the driving force of the current technological revolution, has been used in many frontiers, including education, security, gaming, finance, robotics, autonomous systems, entertainment, and most importantly the healthcare sector. With the rise of the COVID-19 pandemic, several prediction and detection methods using artificial intelligence have been employed to understand, forecast, handle, and curtail the ensuing threats. In this study, the most recent related publications, methodologies and medical reports were investigated with the purpose of studying artificial intelligence’s role in the pandemic. This study presents a comprehensive review of artificial intelligence with specific attention to machine learning, deep learning, image processing, object detection, image segmentation, and few-shot learning studies that were utilized in several tasks related to COVID-19. In particular, genetic analysis, medical image analysis, clinical data analysis, sound analysis, biomedical data classification, socio-demographic data analysis, anomaly detection, health monitoring, personal protective equipment (PPE) observation, social control, and COVID-19 patients’ mortality risk approaches were used in this study to forecast the threatening factors of COVID-19. This study demonstrates that artificial-intelligence-based algorithms integrated into Internet of Things wearable devices were quite effective and efficient in COVID-19 detection and forecasting insights which were actionable through wide usage. The results produced by the study prove that artificial intelligence is a promising arena of research that can be applied for disease prognosis, disease forecasting, drug discovery, and to the development of the healthcare sector on a global scale. We prove that artificial intelligence indeed played a significantly important role in helping to fight against COVID-19, and the insightful knowledge provided here could be extremely beneficial for practitioners and research experts in the healthcare domain to implement the artificial-intelligence-based systems in curbing the next pandemic or healthcare disaster. Full article

(This article belongs to the Special Issue Smart Healthcare Systems Based on the Internet of Things and Artificial Intelligence)

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37 pages, 5442 KiB

Open AccessReview

A Study of the Recent Trends of Immunology: Key Challenges, Domains, Applications, Datasets, and Future Directions

by Sharnil Pandya, Aanchal Thakur, Santosh Saxena, Nandita Jassal, Chirag Patel, Kirit Modi, Pooja Shah, Rahul Joshi, Sudhanshu Gonge, Kalyani Kadam and Prachi Kadam

Sensors 2021, 21(23), 7786; https://doi.org/10.3390/s21237786 - 23 Nov 2021

Cited by 11 | Viewed by 6137

Abstract

The human immune system is very complex. Understanding it traditionally required specialized knowledge and expertise along with years of study. However, in recent times, the introduction of technologies such as AIoMT (Artificial Intelligence of Medical Things), genetic intelligence algorithms, smart immunological methodologies, etc., has made this process easier. These technologies can observe relations and patterns that humans do and recognize patterns that are unobservable by humans. Furthermore, these technologies have also enabled us to understand better the different types of cells in the immune system, their structures, their importance, and their impact on our immunity, particularly in the case of debilitating diseases such as cancer. The undertaken study explores the AI methodologies currently in the field of immunology. The initial part of this study explains the integration of AI in healthcare and how it has changed the face of the medical industry. It also details the current applications of AI in the different healthcare domains and the key challenges faced when trying to integrate AI with healthcare, along with the recent developments and contributions in this field by other researchers. The core part of this study is focused on exploring the most common classifications of health diseases, immunology, and its key subdomains. The later part of the study presents a statistical analysis of the contributions in AI in the different domains of immunology and an in-depth review of the machine learning and deep learning methodologies and algorithms that can and have been applied in the field of immunology. We have also analyzed a list of machine learning and deep learning datasets about the different subdomains of immunology. Finally, in the end, the presented study discusses the future research directions in the field of AI in immunology and provides some possible solutions for the same. Full article

(This article belongs to the Special Issue Smart Healthcare Systems Based on the Internet of Things and Artificial Intelligence)

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