mHealth

Mobile health (mHealth), the application of mobile technologies for healthcare services, has been the driving force in healthcare in the last few decades; from healthcare service delivery to low-cost tools for effective disease diagnosis, prediction, monitoring, and management. The main purpose of this chapter was to identify the scope and range of studies on mHealth used as low-cost tools for effective disease diagnosis, prediction, monitoring, and management. The authors identified 55 papers that met the inclusion and exclusion criteria after searching different academic databases. The findings revealed that low-cost mHealth approaches such as text messaging and mobile applications developed using artificial intelligence algorithms have been used for disease diagnosis, prediction, monitoring, and management. The findings of this scoping review present information regarding different mHealth approaches that can be used by researchers and practitioners interested in the application of low-cost mHealth solutions in low-resource settings.

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5G and intelligence medicine—how the next generation of wireless technology will reconstruct healthcare?

Precision Clinical Medicine ◽

10.1093/pcmedi/pbz020 ◽

2019 ◽

Vol 2 (4) ◽

pp. 205-208 ◽

Cited By ~ 11

Author(s):

Dong Li

Keyword(s):

Artificial Intelligence ◽

Big Data ◽

Internet Of Things ◽

Patient Experience ◽

Healthcare Service ◽

Healthcare Services ◽

Wireless Transmission ◽

Wireless Technology ◽

Total Cost ◽

5G Technologies

Abstract Despite intensive efforts, there are still enormous challenges in provision of healthcare services to the increasing aging population. Recent observations have raised concerns regarding the soaring costs of healthcare, the imbalance of medical resources, inefficient healthcare system administration, and inconvenient medical experiences. However, cutting-edge technologies are being developed to meet these challenges, including, but not limited to, Internet of Things (IoT), big data, artificial intelligence, and 5G wireless transmission technology to improve the patient experience and healthcare service quality, while cutting the total cost attributable to healthcare. This is not an unrealistic fantasy, as these emerging technologies are beginning to impact and reconstruct healthcare in subtle ways. Although the technologies mentioned above are integrated, in this review we take a brief look at cases focusing on the application of 5G wireless transmission technology in healthcare. We also highlight the potential pitfalls to availability of 5G technologies.

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An IoT based Tele-Health WBAN Model for Elderly People – A Review

Engineering and Scientific International Journal ◽

10.30726/esij/v8.i3.2021.83021 ◽

2021 ◽

Vol 8 (3) ◽

Author(s):

Vajubunnisa Begum R ◽

Dharmarajan K

Keyword(s):

Elderly People ◽

Old Age ◽

Low Cost ◽

Wireless Body Area Network ◽

The Elderly ◽

Healthcare Services ◽

Disease Diagnosis ◽

Area Network ◽

Wearable Sensor ◽

Sensor Devices

The Tele-Health WBAN (Wireless Body Area Network) Model for patients required more attention especially old age people’s healthcare services in Low-cost Internet of Things (IoT) Devices. The advancements in telemedicine have increased drastically towards wearable sensor devices and mobile phone-based applications in the last few years. The study presents the integration of IoT and wearable sensor devices in the Tele - Health system developed for tracking heart patients among the elderly people and also to prevent them from stroke. In order to meet the demand for old age people healthcare services, it is very much essential to provide assistance in cardiac disease diagnosis and suggest medication in their home with comfortable environment. Hence, they can avoid frequent visit to hospitals and long stays.

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Systematic review of mobile health behavioural interventions to improve uptake of HIV testing for vulnerable and key populations

Journal of Telemedicine and Telecare ◽

10.1177/1357633x16639186 ◽

2016 ◽

Vol 23 (2) ◽

pp. 347-359 ◽

Cited By ~ 27

Author(s):

Donaldson F Conserve ◽

Larissa Jennings ◽

Carolina Aguiar ◽

Grace Shin ◽

Lara Handler ◽

...

Keyword(s):

Hiv Testing ◽

Mobile Health ◽

Text Messaging ◽

Low Cost ◽

Mobile Technologies ◽

Key Populations ◽

Behavioural Interventions ◽

Positive Effects ◽

Methodological Rigor ◽

Persons Living With Hiv

Introduction This systematic narrative review examined the empirical evidence on the effectiveness of mobile health (mHealth) behavioural interventions designed to increase the uptake of HIV testing among vulnerable and key populations. Methods MEDLINE/PubMed, Embase, Web of Science, and Global Health electronic databases were searched. Studies were eligible for inclusion if they were published between 2005 and 2015, evaluated an mHealth intervention, and reported an outcome relating to HIV testing. We also reviewed the bibliographies of retrieved studies for other relevant citations. The methodological rigor of selected articles was assessed, and narrative analyses were used to synthesize findings from mixed methodologies. Results A total of seven articles met the inclusion criteria. Most mHealth interventions employed a text-messaging feature and were conducted in middle- and high-income countries. The methodological rigor was moderate among studies. The current literature suggests that mHealth interventions can have significant positive effects on HIV testing initiation among vulnerable and key populations, as well as the general public. In some cases, null results were observed. Qualitative themes relating to the use of mobile technologies to increase HIV testing included the benefits of having low-cost, confidential, and motivational communication. Reported barriers included cellular network restrictions, poor linkages with physical testing services, and limited knowledge of appropriate text-messaging dose. Discussion MHealth interventions may prove beneficial in reducing the proportion of undiagnosed persons living with HIV, particularly among vulnerable and key populations. However, more rigorous and tailored interventions are needed to assess the effectiveness of widespread use.

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Applications of digital health for public health responses to COVID-19: a systematic scoping review of artificial intelligence, telehealth and related technologies

npj Digital Medicine ◽

10.1038/s41746-021-00412-9 ◽

2021 ◽

Vol 4 (1) ◽

Author(s):

Dinesh Visva Gunasekeran ◽

Rachel Marjorie Wei Wen Tseng ◽

Yih-Chung Tham ◽

Tien Yin Wong

Keyword(s):

Public Health ◽

Artificial Intelligence ◽

Scoping Review ◽

Real World ◽

Digital Health ◽

Large Body ◽

Healthcare Services ◽

Digital Platforms ◽

Medical Needs ◽

Wide Range

AbstractThe coronavirus disease 2019 (COVID-19) pandemic has overwhelmed healthcare services, faced with the twin challenges in acutely meeting the medical needs of patients with COVID-19 while continuing essential services for non-COVID-19 illnesses. The need to re-invent, re-organize and transform healthcare and co-ordinate clinical services at a population level is urgent as countries that controlled initial outbreaks start to experience resurgences. A wide range of digital health solutions have been proposed, although the extent of successful real-world applications of these technologies is unclear. This study aims to review applications of artificial intelligence (AI), telehealth, and other relevant digital health solutions for public health responses in the healthcare operating environment amidst the COVID-19 pandemic. A systematic scoping review was performed to identify potentially relevant reports. Key findings include a large body of evidence for various clinical and operational applications of telehealth (40.1%, n = 99/247). Although a large quantity of reports investigated applications of artificial intelligence (AI) (44.9%, n = 111/247) and big data analytics (36.0%, n = 89/247), weaknesses in study design limit generalizability and translation, highlighting the need for more pragmatic real-world investigations. There were also few descriptions of applications for the internet of things (IoT) (2.0%, n = 5/247), digital platforms for communication (DC) (10.9%, 27/247), digital solutions for data management (DM) (1.6%, n = 4/247), and digital structural screening (DS) (8.9%, n = 22/247); representing gaps and opportunities for digital public health. Finally, the performance of digital health technology for operational applications related to population surveillance and points of entry have not been adequately evaluated.

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Improving Diagnosis through Digital Pathology: A Proof-of-concept Implementation using Smart Contracts and Decentralized file storage (IPFS) (Preprint)

10.2196/preprints.34207 ◽

2021 ◽

Author(s):

Hemang Subramanian ◽

Susmitha Subramanian

Keyword(s):

Artificial Intelligence ◽

Data Storage ◽

Low Cost ◽

Digital Pathology ◽

Disease Diagnosis ◽

Security Requirements ◽

Smart Contracts ◽

Data Systems ◽

Network Bandwidth ◽

File Storage

BACKGROUND Recent advancements in digital pathology resulting from advances in imaging and digitization have increased the convenience and usability of pathology for disease diagnosis, especially in oncology, urology, and gastro-enteric diagnosis. However, despite the possibilities to include low-cost diagnosis and viable telemedicine, remote diagnosis potential, digital pathology is not yet accessible due to expensive storage, data security requirements, and network bandwidth limitations to transfer high-resolution images and associated data. The increase in storage, transmission and security complexity concerning data collection and diagnosis makes it even more challenging to use artificial intelligence algorithms for machine-assisted disease diagnosis. We design and prototype a digital pathology system that uses blockchain-based smart contracts using the Non-fungible Token standard and the Inter-Planetary File System (IPFS) for data storage. Our design remediates shortcomings in the existing digital pathology systems infrastructure, which is centralized. The proposed design is extendable to other fields of medicine that require high-fidelity image and data storage. Our solution is implemented in data systems that can improve access, quality of care and reduce the cost of access to specialized pathological diagnosis, reducing cycle times for diagnosis. OBJECTIVE The study's main objectives are to highlight the issues in digital pathology and suggest a software architecture-based blockchain and IPFS create a low-cost data storage and transmission technology. METHODS We use the design science research method (DSRM) consisting of six stages to inform our design overall. We innovate over existing public-private designs for blockchains but using a two-layered approach that separates actual file storage from meta-data and data persistence. RESULTS Here, we identify key challenges to adopting digital pathology, including challenges concerning long-term storage, the transmission of information, etc. Next, using accepted frameworks in non-fungible token-based intelligent contracts and recent innovations in distributed secure storage, we propose a decentralized, secure, and privacy-preserving digital pathology system. Our design and prototype implementation using Solidity, web3.js, Ethereum, and node.js help us address several challenges facing digital pathology. We demonstrate how our solution that combines non-fungible token (NFT) smart contract standard with persistent decentralized file storage to solve most of the challenges of digital pathology and sets the stage for reducing costs and improving patient care and speed of diagnosis. CONCLUSIONS We identify technical limitations that increase costs and reduce mass adoption of digital pathology. We present several design innovations by using standards in NFT decentralized storage to prototype a system. We also present implementation details of a unique security architecture for a digital pathology system. We illustrate how this design can overcome privacy, security, network-based storage, and data transmission limitations. We illustrate how improving these factors sets the stage for improving data quality and standardized application of machine learning and Artificial Intelligence to such data CLINICALTRIAL Not applicable

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CoAI: Cost-Aware Artificial Intelligence for Health Care

10.1101/2021.01.19.21249356 ◽

2021 ◽

Author(s):

Gabriel Erion ◽

Joseph D. Janizek ◽

Carly Hudelson ◽

Richard B. Utarnachitt ◽

Andrew M. McCoy ◽

...

Keyword(s):

Artificial Intelligence ◽

Intensive Care ◽

Predictive Model ◽

Low Cost ◽

The United States ◽

Disease Diagnosis ◽

Performance Model ◽

Risk Scores ◽

Recent Emergence ◽

The Cost

AbstractThe recent emergence of accurate artificial intelligence (AI) models for disease diagnosis raises the possibility that AI-based clinical decision support could substantially lower the workload of healthcare providers. However, for this to occur, the input data to an AI predictive model, i.e., the patient’s features, must themselves be low-cost, that is, efficient, inexpensive, or low-effort to acquire. When time or financial resources for gathering data are limited, as in emergency or critical care medicine, modern high-accuracy AI models that use thousands of patient features are likely impractical. To address this problem, we developed the CoAI (Cost-aware AI) framework to enable any kind of AI predictive model (e.g., deep neural networks, tree ensemble models, etc.) to make accurate predictions given a small number of low-cost features. We show that CoAI dramatically reduces the cost of predicting prehospital acute traumatic coagulopathy, intensive care mortality, and outpatient mortality relative to existing risk scores, while improving prediction accuracy. It also outperforms existing state-of-the-art cost-sensitive prediction approaches in terms of predictive performance, model cost, and training time. Extrapolating these results to all trauma patients in the United States shows that, at a fixed false positive rate, CoAI could alert providers of tens of thousands more dangerous events than other risk scores while reducing providers’ data-gathering time by about 90 percent, leading to a savings of 200,000 cumulative hours per year across all providers. We extrapolate similar increases in clinical utility for CoAI in intensive care. These benefits stem from several unique strengths: First, CoAI uses axiomatic feature attribution methods that enable precise estimation of feature importance. Second, CoAI is model-agnostic, allowing users to choose the predictive model that performs the best for the prediction task and data at hand. Finally, unlike many existing methods, CoAI finds high-performance models within a given budget without any tuning of the cost-vs-performance tradeoff. We believe CoAI will dramatically improve patient care in the domains of medicine in which predictions need to be made with limited time and resources.

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Proposal of a novel Artificial Intelligence Distribution Service platform for healthcare

F1000Research ◽

10.12688/f1000research.36775.1 ◽

2021 ◽

Vol 10 ◽

pp. 245

Author(s):

Antti Väänänen ◽

Keijo Haataja ◽

Katri Vehviläinen-Julkunen ◽

Pekka Toivanen

Keyword(s):

Artificial Intelligence ◽

Open Access ◽

Healthcare Service ◽

Service Providers ◽

Research Work ◽

Health Assessment ◽

Healthcare Services ◽

Service Platform ◽

Healthcare Evaluation ◽

Multiple Health

In this paper, we focus on presenting a novel AI-based service platform proposal called AIDI (Artificial Intelligence Distribution Interface for healthcare). AIDI proposal is based on our earlier research work in which we evaluated AI-based healthcare services which have been used successfully in practice among healthcare service providers. We have also used our systematic review about AI-based healthcare services benefits in various healthcare sectors. This novel AIDI proposal contains services for health assessment, healthcare evaluation, and cognitive assistant which can be used by researchers, healthcare service provides, clinicians, and consumers. AIDI integrates multiple health databases and data lakes with AI service providers and open access AI algorithms. It also gives healthcare service providers open access to state-of-the-art AI-based diagnosis and analysis services. This paper provides a description of AIDI platform, how it could be developed, what can become obstacles in the development, and how the platform can provide benefits to healthcare when it will be operational in the future.

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Machine Learning in Agriculture

International Journal of Advanced Research in Computer Science and Software Engineering ◽

10.23956/ijarcsse.v8i6.713 ◽

2018 ◽

Vol 8 (6) ◽

pp. 26 ◽

Cited By ~ 1

Author(s):

Matthew N. O. Sadiku ◽

Chandra M. M Kotteti ◽

Sarhan M. Musa

Keyword(s):

Climate Change ◽

Artificial Intelligence ◽

Machine Learning ◽

Disease Diagnosis ◽

Automated Detection ◽

Disease Prediction ◽

Paper Briefly ◽

Agriculture Sector ◽

Modern Agriculture ◽

Crop Disease

Machine learning is an emerging field of artificial intelligence which can be applied to the agriculture sector. It refers to the automated detection of meaningful patterns in a given data. Modern agriculture seeks ways to conserve water, use nutrients and energy more efficiently, and adapt to climate change. Machine learning in agriculture allows for more accurate disease diagnosis and crop disease prediction. This paper briefly introduces what machine learning can do in the agriculture sector.

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Text Messaging as a Mental Health Treatment Intervention: A Corpus-Based Study

10.31234/osf.io/gt73p ◽

2019 ◽

Author(s):

Megan Partch ◽

Cass Dykeman

Keyword(s):

Mental Health ◽

Mental Health Treatment ◽

Text Messaging ◽

Low Cost ◽

Statistical Significance ◽

Text Message ◽

Treatment Session ◽

Text Messages ◽

Health Treatment ◽

Emotional Tone

Mental health treatment providers seek high-impact and low-cost means of engaging clients in care. As such, text messaging is becoming more frequently utilized as a means of communication between provider and client. Research demonstrates that text message interventions increase treatment session attendance, decrease symptomology, and improve overall functioning. However, research is lacking related to the linguistic make up of provider communications. Text messages were collected from previously published articles related to the treatment of mental health disorders. A corpus of 39 mental health treatment text message interventions was composed totaling 286 words. Using Linguistic Inquiry and Word Count (LIWC) software, messages were analyzed for prevalence of terminology thought to enhance client engagement. Clout, demonstrating the writer’s confidence and expertise, and positive Emotional Tone were found to be at a high level within the corpus. Results demonstrated statistical significance for five linguistic variables. When compared with national blog norms derived from Twitter, Clout, Emotional Tone, and use of Biological terminology were found to be at higher rates than expected. Authenticity and Informal terminology were found at significantly lesser rates.

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Artificial Intelligence for Skin Cancer Detection: A Scoping Review (Preprint)

10.2196/preprints.22934 ◽

2020 ◽

Author(s):

Abdulrahman Takiddin ◽

Jens Schneider ◽

Yin Yang ◽

Alaa Abd-Alrazaq ◽

Mowafa Househ

Keyword(s):

Artificial Intelligence ◽

Skin Cancer ◽

Scoping Review ◽

Performance Metrics ◽

Data Extraction ◽

Characteristic Curve ◽

Evaluation Metrics ◽

Cancer Type ◽

Computer Algorithms ◽

Scoping Reviews

BACKGROUND Skin cancer is the most common cancer type affecting humans. Traditional skin cancer diagnosis methods are costly, require a professional physician, and take time. Hence, to aid in diagnosing skin cancer, Artificial Intelligence (AI) tools are being used, including shallow and deep machine learning-based techniques that are trained to detect and classify skin cancer using computer algorithms and deep neural networks. OBJECTIVE The aim of this study is to identify and group the different types of AI-based technologies used to detect and classify skin cancer. The study also examines the reliability of the selected papers by studying the correlation between the dataset size and number of diagnostic classes with the performance metrics used to evaluate the models. METHODS We conducted a systematic search for articles using IEEE Xplore, ACM DL, and Ovid MEDLINE databases following the PRISMA Extension for Scoping Reviews (PRISMA-ScR) guidelines. The study included in this scoping review had to fulfill several selection criteria; to be specifically about skin cancer, detecting or classifying skin cancer, and using AI technologies. Study selection and data extraction were conducted by two reviewers independently. Extracted data were synthesized narratively, where studies were grouped based on the diagnostic AI techniques and their evaluation metrics. RESULTS We retrieved 906 papers from the 3 databases, but 53 studies were eligible for this review. While shallow techniques were used in 14 studies, deep techniques were utilized in 39 studies. The studies used accuracy (n=43/53), the area under receiver operating characteristic curve (n=5/53), sensitivity (n=3/53), and F1-score (n=2/53) to assess the proposed models. Studies that use smaller datasets and fewer diagnostic classes tend to have higher reported accuracy scores. CONCLUSIONS The adaptation of AI in the medical field facilitates the diagnosis process of skin cancer. However, the reliability of most AI tools is questionable since small datasets or low numbers of diagnostic classes are used. In addition, a direct comparison between methods is hindered by a varied use of different evaluation metrics and image types.

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