Towards Understanding the Usability Attributes of AI-Enabled eHealth Mobile Applications

Alzahrani, Adel; Gay, Valerie; Alturki, Ryan; AlGhamdi, Mohammed J.

doi:10.1155/2021/5313027

Cited by 21 publications

(16 citation statements)

References 59 publications

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“…Digital health management has offered long-held hope for extending clinical resources in understanding and managing diseases by virtually connecting patients and healthcare providers through digital technology, such as mobile applications in a bring-your-own-device (BYOD) setting [ 9 , 14 , 41 , 42 ]. Data from personal devices can be gathered to support just-in-time adaptive interventions and health behaviors.…”

Section: Adoptionsmentioning

confidence: 99%

Enhanced Patient-Centricity: How the Biopharmaceutical Industry Is Optimizing Patient Care through AI/ML/DL

Zou

2022

Healthcare

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Technologies utilizing cutting-edge methodologies, including artificial intelligence (AI), machine learning (ML) and deep learning (DL), present powerful opportunities to help evaluate, predict, and improve patient outcomes by drawing insights from real-world data (RWD) generated during medical care. They played a role during and following the Coronavirus Disease 2019 (COVID-19) pandemic by helping protect healthcare providers, prioritize care for vulnerable populations, predict disease trends, and find optimal therapies. Potential applications across therapeutic areas include diagnosis, disease management and patient journey mapping. Use of fit-for-purpose datasets for ML models is seeing growth and may potentially help additional enterprises develop AI strategies. However, biopharmaceutical companies often face specific challenges, including multi-setting data, system interoperability, data governance, and patient privacy requirements. There remains a need for evolving regulatory frameworks, operating models, and data governance to enable further developments and additional research. We explore recent literature and examine the hurdles faced by researchers in the biopharmaceutical industry to fully realize the promise of AI/ML/DL for patient-centric purposes.

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Section: Adoptionsmentioning

confidence: 99%

Enhanced Patient-Centricity: How the Biopharmaceutical Industry Is Optimizing Patient Care through AI/ML/DL

Zou

2022

Healthcare

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“…In addition, usability is a crucial concern for mobile health application systems because it contributes significantly to their success. However, Poor usability is a major obstacle to the widespread use of mobile applications [32]. Therefore, Mhealth could have unforeseen, detrimental effects if usability is not taken into account, including an increase in medical errors and issues with provider communication [31].…”

Section: B Barriers To Using Mobile Health Applications On Self-manag...mentioning

confidence: 99%

Impact of Mobile Technology Solution on Self-Management in Patients with Hypertension: Advantages and Barriers

Alzahrani¹,

Gay²,

Alturki³

2022

IJACSA

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Hypertension is a major risk factor for cardiovascular morbidity and mortality. It is a condition that increases the high risk of heart, liver, and other diseases. Since hypertension is one of the biggest global public health issues, patients require more interventions to manage their blood pressure. The vast use of mobile phones and applications with medication features has turned a smartphone into a medical device. These tools are helpful for a physician in the treatment of hypertension. Mobile health applications are utilised to manage hypertension at the moment; however, there is a lack of information regarding their efficacy. Smartphones and their applications are evolving quickly hence the rise in the innovation of mobile health applications. Mobile-based applications are helpful in-patient education and reinforce the behaviour through constant reminders, medication, and appointment alarms. The main objective of this study is to determine the impact of mobile health applications on self-management in patients with hypertension and its advantages and disadvantages. We used publications from 2015 and later as a time frame and searched on the first five pages of Google Scholar, JSTOR, Hindawi, PubMed, and ResearchGate. We group all associated terms that might turn up articles on this subject in the search results. The total number of database records that we identified were 213, and the duplicate identified and removed were 117; hence the screened records were 96. The reports excluded based on abstract and title were 31. Articles with full text and have been accessed for final inclusion were 65. The excluded articles were 51, and the studies included in the qualitative analysis were 14.

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“…Specific smartphone applications are designed to answer medical questions or facilitate the process of finding a diagnosis. 1 Such applications are often supported or run by artificial intelligence (AI). Recently, due to the limitations of real-life consultations caused by the COVID-19 pandemic, such applications have experienced a major increase in popularity.…”

Section: Introductionmentioning

confidence: 99%

“…Medicine as a dynamic field has embraced revolutionary theories and technologies in the past and thus enabled entirely new opportunities to emerge. Specific smartphone applications are designed to answer medical questions or facilitate the process of finding a diagnosis 1 . Such applications are often supported or run by artificial intelligence (AI).…”

Section: Introductionmentioning

confidence: 99%

Basic principles of artificial intelligence in dermatology explained using melanoma

Hartmann,

Passauer,

Hartmann

et al. 2024

J Deutsche Derma Gesell

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SummaryThe use of artificial intelligence (AI) continues to establish itself in the most diverse areas of medicine at an increasingly fast pace. Nevertheless, many healthcare professionals lack the basic technical understanding of how this technology works, which severely limits its application in clinical settings and research. Thus, we would like to discuss the functioning and classification of AI using melanoma as an example in this review to build an understanding of the technology behind AI. For this purpose, elaborate illustrations are used that quickly reveal the technology involved. Previous reviews tend to focus on the potential applications of AI, thereby missing the opportunity to develop a deeper understanding of the subject matter that is so important for clinical application. Malignant melanoma has become a significant burden for healthcare systems. If discovered early, a better prognosis can be expected, which is why skin cancer screening has become increasingly popular and is supported by health insurance. The number of experts remains finite, reducing their availability and leading to longer waiting times. Therefore, innovative ideas need to be implemented to provide the necessary care. Thus, machine learning offers the ability to recognize melanomas from images at a level comparable to experienced dermatologists under optimized conditions.

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Towards Understanding the Usability Attributes of AI-Enabled eHealth Mobile Applications

Cited by 21 publications

References 59 publications

Enhanced Patient-Centricity: How the Biopharmaceutical Industry Is Optimizing Patient Care through AI/ML/DL

Enhanced Patient-Centricity: How the Biopharmaceutical Industry Is Optimizing Patient Care through AI/ML/DL

Impact of Mobile Technology Solution on Self-Management in Patients with Hypertension: Advantages and Barriers

Basic principles of artificial intelligence in dermatology explained using melanoma

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