Influences on User Trust in Healthcare Artificial Intelligence (HAI): A Systematic Review (Preprint)

2021 ◽  
Author(s):  
Eva Jermutus ◽  
Dylan Kneale ◽  
James Thomas ◽  
Susan Michie
Keyword(s):  
Artificial Intelligence ◽  
Systematic Review ◽  
Data Privacy ◽  
Publication Date ◽  
Related Data ◽  
Healthcare Data ◽  
Healthcare It ◽  
Starting Point ◽  
Health Related ◽  
User Trust

BACKGROUND Artificial Intelligence (AI) is becoming increasingly prominent in domains such as healthcare. It is argued to be transformative through altering the way in which healthcare data is used as well as tackling rising costs and staff shortages. The realisation and success of AI depends heavily on people’s trust in its applications. Yet, the influences on trust in AI applications in healthcare so far have been underexplored OBJECTIVE The objective of this study was to identify aspects (related to users, the AI application and the wider context) influencing trust in healthcare AI (HAI). METHODS We performed a systematic review to map out influences on user trust in HAI. To identify relevant studies, we searched 7 electronic databases in November 2019 (ACM digital library, IEEE Explore, NHS Evidence, Ovid ProQuest Dissertations & Thesis Global, Ovid PsycINFO, PubMed, Web of Science Core Collection). Searches were restricted to publications available in English and German with no publication date restriction. To be included studies had to be empirical; focus on an AI application (excluding robotics) in a health-related setting; and evaluate applications with regards to users. RESULTS Overall, 3 studies, one mixed-method and 2 qualitative studies in English were included. Influences on trust fell into three broad categories: human-related (knowledge, expectation, mental model, self-efficacy, type of user, age, gender), AI-related (data privacy and safety, operational safety, transparency, design, customizability, trialability, explainability, understandability, power-control-balance, benevolence) and related to wider context (AI company, media, social network of the user). The factors resulted in an updated logic model illustrating the relationship between these aspects. CONCLUSIONS Trust in healthcare AI depends on a variety of factors, both external and internal to the AI application. This study contributes to our understanding of what influences trust in HAI by highlighting key influences as well as pointing to gaps and issues in existing research on trust and AI. In so doing, it offers a starting point for further investigation of trust environments as well as trustworthy AI applications.

Security and Privacy for Electronic Healthcare Records Using AI in Blockchain

2021 ◽  
pp. 90-102
Author(s):  
Ramani Selvanambi ◽  
Samarth Bhutani ◽  
Komal Veauli
Keyword(s):  
Data Privacy ◽  
Correct Diagnosis ◽  
Medical Data ◽  
Medical Advice ◽  
Data Availability ◽  
Security And Privacy ◽  
Privacy And Security ◽  
Related Data ◽  
Healthcare Data ◽  
Health Related

In yesteryears, the healthcare data related to each patient was limited. It was stored and controlled by the hospital authorities and was seldom regulated. With the increase in awareness and technology, the amount of medical data per person has increased exponentially. All this data is essential for the correct diagnosis of the patient. The patients also want access to their data to seek medical advice from different doctors. This raises several challenges like security, privacy, data regulation, etc. As health-related data are privacy-sensitive, the increase in data stored increases the risk of data exposure. Data availability and privacy are essential in healthcare. The availability of correct information is critical for the treatment of the patient. Information not easily accessed by the patients also complicates seeking medical advice from different hospitals. However, if data is easily accessible to everyone, it makes privacy and security difficult. Blockchains to store and secure data will not only ensure data privacy but will also provide a common method of data regulation.

scholarly journals Use of Patient Generated Health Data collected from consumer wearable devices in clinical care: a systematic review and meta-analysis (Preprint)

2017 ◽  
Author(s):  
Robab Abdolkhani ◽  
Kathleen Gray ◽  
Ann Borda
Keyword(s):  
Systematic Review ◽  
Life Cycle ◽  
Low Cost ◽  
Meta Analysis ◽  
Clinical Care ◽  
Self Care ◽  
Health Data ◽  
Related Data ◽  
Life Cycle Stages ◽  
Health Related

BACKGROUND PGHD (Patient Generated Health Data) are health-related data created or recorded by patients to inform their self-care. The availability of low-cost easy-to-use consumer wearable technologies has facilitated patients’ engagement in their self-care and increased production of PGHD but the uptake of this data in clinical environments has been slow. Studies showing opportunities and challenges affecting PGHD adoption and use in clinical care have not investigated these factors in detail during all stages of the PGHD life cycle. OBJECTIVE This study aims to provide deeper insight into various issues influencing the use of PGHD at each stage of its life cycle from the perspectives of key stakeholders including patients, healthcare professionals, and the health IT managers. METHODS A systematic review was undertaken on the scholarly and industry literature published from 2012 to 2017. Thematic analysis of content was applied to uncover perspectives of the key PGHD stakeholders on opportunities and challenges related to all life cycle stages of PGHD from consumer wearables. RESULTS Thirty-six papers were identified for detailed analysis. Challenges were discussed more frequently than opportunities. Most studies done in real-world settings were limited to the collection stage of PGHD life cycle that captured through consumer wearables. CONCLUSIONS There are many gaps in knowledge on opportunities and challenges affecting PGHD captured through consumer wearables in each stage of its life cycle. A conceptual framework involving all the stakeholders in overcoming various technical, clinical, cultural, and regulatory challenges affecting PGHD during its life cycle could help to advance the integration with and use of PGHD in clinical care.

scholarly journals European citizens’ perspectives on direct-to-consumer genetic testing: an updated systematic review

2020 ◽  
Author(s):  
Ilda Hoxhaj ◽  
Jovana Stojanovic ◽  
Stefania Boccia
Keyword(s):  
Systematic Review ◽  
Data Privacy ◽  
English Language ◽  
Population Group ◽  
Original Data ◽  
European Countries ◽  
Common Disease ◽  
Publication Date ◽  
Genetic Tests ◽  
Direct To Consumer

Abstract Background Direct-to-consumer genetic tests (DTC-GTs) are genetic tests for a medical or non-medical trait that are sold directly to the public, usually ordered without the engagement of a healthcare professional. Our aim was to explore the knowledge, attitudes and behaviors toward DTC-GTs among European citizens. Methods We updated the most recent systematic review on citizens’ perspectives toward DTC-GTs. Relevant English language studies were searched on PubMed, ISI Web of Science, Scopus, Embase and Google Scholar from October 2014 to April 2019. We extended our search on Scopus without publication date restriction, since it was not included in the former review. Eligible studies were conducted in European countries and reported original data. The quality of the studies was evaluated using a checklist developed by Kmet et al. Results We included six studies conducted in European countries between 2015 and 2018. The studies were performed among general population in the Netherlands, students in Italy and Greece, laypeople in Germany and older adults in Switzerland. The level of awareness, in overall low, differed by country and population group. Most of the participants were interested in undergoing a DTC-GT, mainly for knowing the risk predisposition to a common disease. Concerns were raised about tests’ validity and utility and data privacy. Conclusions Our review shows that European citizens, overall, have a low level of knowledge on DTC-GTs and a high interest in their purchase. This understanding might contribute to the development of educational programs in order to the increase of general public capabilities to make appropriate health decisions.

scholarly journals Would patients undergo postoperative follow-up by using a smartphone application?

BMC Surgery ◽  
2020 ◽  
Vol 20 (1) ◽  
Author(s):  
Julian Scherer ◽  
Frank Keller ◽  
Hans-Christoph Pape ◽  
Georg Osterhoff
Keyword(s):  
Mobile Application ◽  
Smartphone Application ◽  
Mobile App ◽  
Web Based ◽  
Direct Benefit ◽  
Orthopaedic Practice ◽  
Related Data ◽  
Healthcare Data ◽  
Health Related

Abstract Background eHealth applications have been proposed as an alternative to monitor patients in frequent intervals or over long distances. The aim of this study was to assess whether patients would accept an application on their smartphone to be monitored by their physicians. Methods During September 2017 and December 2017 a survey amongst smartphone users was conducted via paper and web-based questionnaires. Results More than half of the 962 participants (54%) were older than 55 years of age. The majority of the participants (68.7%) would accept a follow-up by a smartphone application obtaining personal healthcare data. 72.6% of all patients older than 55 years of age would use the application. The most prevalent reason against installing the application was data protection. Patients being currently treated in an orthopaedic practice and pedestrians were more eager to accept a follow-up by a mobile app than participants from social media. Conclusion The majority of participants would accept a mobile application, collecting personal health-related data for postoperative follow-up, and saw a direct benefit for the patient in such an application.

scholarly journals Big Data Analytics & Artificial Intelligence in Healthcare

2021 ◽  
Author(s):  
PRANJAL KUMAR ◽  
Siddhartha Chauhan
Keyword(s):  
Artificial Intelligence ◽  
Big Data ◽  
Large Scale ◽  
Health Sector ◽  
Big Data Analytics ◽  
Personal Data ◽  
Related Data ◽  
Large Scale Data ◽  
Ethical Policy ◽  
Health Related

Abstract Big data analysis and Artificial Intelligence have received significant attention recently in creating more opportunities in the health sector for aggregating or collecting large-scale data. Today, our genomes and microbiomes can be sequenced i.e., all information exchanged between physicians and patients in Electronic Health Records (EHR) can be collected and traced at least theoretically. Social media and mobile devices today obviously provide many health-related data regarding activity, diets, social contacts, and so on. However, it is increasingly difficult to use this information to answer health questions and, in particular, because the data comes from various domains and lives in different infrastructures and of course it also is very variable quality. The massive collection and aggregation of personal data come with a number of ethical policy, methodological, technological challenges. It should be acknowledged that large-scale clinical evidence remains to confirm the promise of Big Data and Artificial Intelligence (AI) in health care. This paper explores the complexities of big data & artificial intelligence in healthcare as well as the benefits and prospects.

scholarly journals Promises of Big Data and Artificial Intelligence in Nephrology and Transplantation

2020 ◽  
Vol 9 (4) ◽  
pp. 1107 ◽  
Author(s):  
Charat Thongprayoon ◽  
Wisit Kaewput ◽  
Karthik Kovvuru ◽  
Panupong Hansrivijit ◽  
Swetha R. Kanduri ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Big Data ◽  
Kidney Diseases ◽  
Major Health ◽  
Related Data ◽  
Form Part ◽  
Health Related ◽  
Health Burdens ◽  
Excellent Tool ◽  
Advance Knowledge

Kidney diseases form part of the major health burdens experienced all over the world. Kidney diseases are linked to high economic burden, deaths, and morbidity rates. The great importance of collecting a large quantity of health-related data among human cohorts, what scholars refer to as “big data”, has increasingly been identified, with the establishment of a large group of cohorts and the usage of electronic health records (EHRs) in nephrology and transplantation. These data are valuable, and can potentially be utilized by researchers to advance knowledge in the field. Furthermore, progress in big data is stimulating the flourishing of artificial intelligence (AI), which is an excellent tool for handling, and subsequently processing, a great amount of data and may be applied to highlight more information on the effectiveness of medicine in kidney-related complications for the purpose of more precise phenotype and outcome prediction. In this article, we discuss the advances and challenges in big data, the use of EHRs and AI, with great emphasis on the usage of nephrology and transplantation.

scholarly journals A 21st Century Embarrassment of Riches: The Balance Between Health Data Access, Usage, and Sharing

2018 ◽  
Vol 27 (01) ◽  
pp. 005-006 ◽  
Author(s):  
John Holmes ◽  
Lina Soualmia ◽  
Brigitte Séroussi
Keyword(s):  
Data Sharing ◽  
21St Century ◽  
Data Privacy ◽  
Data Access ◽  
Health Data ◽  
Editorial Team ◽  
Special Topic ◽  
Related Data ◽  
Health Related ◽  
Different Sources

Objectives: To provide an introduction to the 2018 International Medical Informatics Association (IMIA) Yearbook by the editors. Methods: This editorial provides an overview and introduction to the 2018 IMIA Yearbook which special topic is: “Between access and privacy: Challenges in sharing health data”. The special topic editors and section are discussed, and the new section of the 2018 Yearbook, Cancer Informatics, is introduced. Changes in the Yearbook editorial team are also described. Results: With the exponential burgeoning of health-related data, and attendant demands for sharing and using these data, the special topic for 2018 is noteworthy for its timeliness. Data sharing brings responsibility for preservation of data privacy, and for this, patient perspectives are of paramount importance in understanding how patients view their health data and how their privacy should be protected. Conclusion: With the increase in availability of health-related data from many different sources and contexts, there is an urgent need for informaticians to become aware of their role in maintaining the balance between data sharing and privacy.

scholarly journals mHealth wearables and smartphone health tracking apps: A changing privacy landscape

2021 ◽  
pp. 1-9
Author(s):  
Christine Suver ◽  
Ellen Kuwana
Keyword(s):  
Data Privacy ◽  
Digital Health ◽  
Well Being ◽  
Unintended Consequences ◽  
Digital Data ◽  
Health Technologies ◽  
Related Data ◽  
Health Apps ◽  
Health Related ◽  
Being There

The use of digital health technologies is changing the ways people monitor and manage their health and well-being. There is increasing interest in using wearables and smartphone health apps to collect health-related data, a domain within digital health referred to as mHealth. Wearables and health apps can continuously monitor metrics such as physical activity, sleep, and heart rate, to name a few. These mHealth data can supplement the measures taken by healthcare professionals during regular doctor’s visits, with mHealth having the advantage of a much greater frequency of collection. But what are the privacy considerations with mHealth? This paper explores global data privacy protections, enumerates principles to guide regulations, discusses the tension between anonymity and data utility, and proposes ways to improve how we as a society talk about and safeguard data privacy. We include brief discussions about inadvertent or unintended consequences of digital data collection and the trade-off between privacy and public health interests, such as is illustrated by COVID-19 contract tracing apps. This paper concludes by offering suggestions for consideration about improving privacy and confidentiality notices.

Implementasi Algoritma Dijkstra Pada Game Pacman

CCIT Journal ◽  
2019 ◽  
Vol 12 (2) ◽  
pp. 170-176
Author(s):  
Anggit Dwi Hartanto ◽  
Aji Surya Mandala ◽  
Dimas Rio P.L. ◽  
Sidiq Aminudin ◽  
Andika Yudirianto
Keyword(s):  
Artificial Intelligence ◽  
Greedy Algorithm ◽  
Dijkstra Algorithm ◽  
Testing Phase ◽  
A Value ◽  
Route Problem ◽  
Starting Point ◽  
The Greedy Algorithm

Pacman is one of the labyrinth-shaped games where this game has used artificial intelligence, artificial intelligence is composed of several algorithms that are inserted in the program and Implementation of the dijkstra algorithm as a method of solving problems that is a minimum route problem on ghost pacman, where ghost plays a role chase player. The dijkstra algorithm uses a principle similar to the greedy algorithm where it starts from the first point and the next point is connected to get to the destination, how to compare numbers starting from the starting point and then see the next node if connected then matches one path with the path). From the results of the testing phase, it was found that the dijkstra algorithm is quite good at solving the minimum route solution to pursue the player, namely by getting a value of 13 according to manual calculations

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