scholarly journals IMPLICATIONS OF ARTIFICIAL INTELLIGENCE IN HEALTHCARE DELIVERY IN THE HOSPITAL SETTINGS: A LITERATURE REVEIW

2019 ◽  
Vol 6 (5) ◽  
Keyword(s):  
Artificial Intelligence ◽  
Healthcare Delivery

Emerging Artificial Intelligence-Empowered Mobile Health: A scoping review (Preprint)

2021 ◽  
Author(s):  
Paras Bhatt ◽  
Jia Liu ◽  
Yanmin Gong ◽  
Jing Wang ◽  
Yuanxiong Guo
Keyword(s):  
Mental Health ◽  
Artificial Intelligence ◽  
Disease Management ◽  
Scoping Review ◽  
Predictive Models ◽  
Healthcare Delivery ◽  
Chronic Health Conditions ◽  
Health Conditions ◽  
Health Issues ◽  
Mental Health Issues

BACKGROUND Artificial Intelligence (AI) has revolutionized healthcare delivery in recent years. There is an increase in research for advanced AI techniques, such as deep learning to build predictive models for the early detection of diseases. Such predictive models leverage mobile health (mHealth) data from wearable sensors and smartphones to discover novel ways for detecting and managing chronic diseases and mental health conditions. OBJECTIVE Currently, little is known about the use of AI-powered mHealth settings. Therefore, this scoping review aims to map current research on the emerging use of AI-powered mHealth (AIM) for managing diseases and promoting health. Our objective is to synthesize research in AIM models that have increasingly been used for healthcare delivery in the last two years. METHODS Using Arksey and O’Malley’s 5-point framework for conducting scoping reviews, we review AIM literature from the past two years in the fields of Biomedical Technology, AI, and Information Systems (IS). We searched three databases - informs PubsOnline, e-journal archive at MIS Quarterly, and ACM Digital Library using keywords such as mobile healthcare, wearable medical sensors, smartphones and AI. We include AIM articles and exclude technical articles focused only on AI models. Also, we use the PRISMA technique for identifying articles that represent a comprehensive view of current research in the AIM domain. RESULTS We screened 108 articles focusing on developing AIM models for ensuring better healthcare delivery, detecting diseases early, and diagnosing chronic health conditions, and 37 articles were eligible for inclusion. A majority of the articles were published last year (31/37). In the selected articles, AI models were used to detect serious mental health issues such as depression and suicidal tendencies and chronic health conditions such as sleep apnea and diabetes. The articles also discussed the application of AIM models for remote patient monitoring and disease management. The primary health concerns addressed relate to three categories: mental health, physical health, and health promotion & wellness. Of these, AIM applications were majorly used to research physical health, representing 46% of the total studies. Finally, a majority of studies use proprietary datasets (28/37) rather than public datasets. We found a lack of research in addressing chronic mental health issues and a lack of publicly available datasets for AIM research. CONCLUSIONS The application of AIM models for disease detection and management is a growing research domain. These models provide accurate predictions for enabling preventive care on a broader scale in the healthcare domain. Given the ever-increasing need for remote disease management during the pandemic, recent AI techniques such as Federated Learning (FL) and Explainable AI (XAI) can act as a catalyst to increase the adoption of AIM and enable secure data sharing across the healthcare industry.

scholarly journals Use of artificial intelligence in healthcare delivery in India

2021 ◽  
Vol 0 ◽  
pp. 0-0
Author(s):  
Keerti Pradhan ◽  
Preethi John ◽  
Namrata Sandhu
Keyword(s):  
Artificial Intelligence ◽  
Healthcare Delivery

scholarly journals Case Report: Utilizing AI and NLP to Assist with Healthcare and Rehabilitation During the COVID-19 Pandemic

2021 ◽  
Vol 4 ◽  
Author(s):  
Jay Carriere ◽  
Hareem Shafi ◽  
Katelyn Brehon ◽  
Kiran Pohar Manhas ◽  
Katie Churchill ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Language Processing ◽  
Chronic Conditions ◽  
Healthcare Delivery ◽  
Occupational Therapists ◽  
Healthcare Systems ◽  
Research Development ◽  
Policy Makers ◽  
Neurological Conditions

The COVID-19 pandemic has profoundly affected healthcare systems and healthcare delivery worldwide. Policy makers are utilizing social distancing and isolation policies to reduce the risk of transmission and spread of COVID-19, while the research, development, and testing of antiviral treatments and vaccines are ongoing. As part of these isolation policies, in-person healthcare delivery has been reduced, or eliminated, to avoid the risk of COVID-19 infection in high-risk and vulnerable populations, particularly those with comorbidities. Clinicians, occupational therapists, and physiotherapists have traditionally relied on in-person diagnosis and treatment of acute and chronic musculoskeletal (MSK) and neurological conditions and illnesses. The assessment and rehabilitation of persons with acute and chronic conditions has, therefore, been particularly impacted during the pandemic. This article presents a perspective on how Artificial Intelligence and Machine Learning (AI/ML) technologies, such as Natural Language Processing (NLP), can be used to assist with assessment and rehabilitation for acute and chronic conditions.

scholarly journals Artificial Intelligence in Primary Health Care: Perceptions, Issues, and Challenges

2019 ◽  
Vol 28 (01) ◽  
pp. 041-046 ◽  
Author(s):  
Harshana Liyanage ◽  
Siaw-Teng Liaw ◽  
Jitendra Jonnagaddala ◽  
Richard Schreiber ◽  
Craig Kuziemsky ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Primary Care ◽  
Health Care ◽  
Primary Health Care ◽  
Medical Informatics ◽  
Panel Discussion ◽  
Healthcare Delivery ◽  
Primary Health ◽  
The Impact ◽  
Care Informatics

Background: Artificial intelligence (AI) is heralded as an approach that might augment or substitute for the limited processing power of the human brain of primary health care (PHC) professionals. However, there are concerns that AI-mediated decisions may be hard to validate and challenge, or may result in rogue decisions. Objective: To form consensus about perceptions, issues, and challenges of AI in primary care. Method: A three-round Delphi study was conducted. Round 1 explored experts’ viewpoints on AI in PHC (n=20). Round 2 rated the appropriateness of statements arising from round one (n=12). The third round was an online panel discussion of findings (n=8) with the members of both the International Medical Informatics Association and the European Federation of Medical Informatics Primary Health Care Informatics Working Groups. Results: PHC and informatics experts reported AI has potential to improve managerial and clinical decisions and processes, and this would be facilitated by common data standards. The respondents did not agree that AI applications should learn and adapt to clinician preferences or behaviour and they did not agree on the extent of AI potential for harm to patients. It was more difficult to assess the impact of AI-based applications on continuity and coordination of care. Conclusion: While the use of AI in medicine should enhance healthcare delivery, we need to ensure meticulous design and evaluation of AI applications. The primary care informatics community needs to be proactive and to guide the ethical and rigorous development of AI applications so that they will be safe and effective.

scholarly journals Stigma, biomarkers, and algorithmic bias: recommendations for precision behavioral health with artificial intelligence

JAMIA Open ◽  
2020 ◽  
Vol 3 (1) ◽  
pp. 9-15 ◽  
Author(s):  
Colin G Walsh ◽  
Beenish Chaudhry ◽  
Prerna Dua ◽  
Kenneth W Goodman ◽  
Bonnie Kaplan ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Intelligent Systems ◽  
Social Stigma ◽  
Model Development ◽  
Healthcare Delivery ◽  
Clinical Markers ◽  
Behavioral Healthcare ◽  
Effective Implementation ◽  
Design And Implementation ◽  
Algorithmic Bias

Abstract Effective implementation of artificial intelligence in behavioral healthcare delivery depends on overcoming challenges that are pronounced in this domain. Self and social stigma contribute to under-reported symptoms, and under-coding worsens ascertainment. Health disparities contribute to algorithmic bias. Lack of reliable biological and clinical markers hinders model development, and model explainability challenges impede trust among users. In this perspective, we describe these challenges and discuss design and implementation recommendations to overcome them in intelligent systems for behavioral and mental health.

scholarly journals Requirement of artificial intelligence technology awareness for thoracic surgeons

2021 ◽  
Vol 29 (1) ◽  
Author(s):  
Anshuman Darbari ◽  
Krishan Kumar ◽  
Shubhankar Darbari ◽  
Prashant L. Patil
Keyword(s):  
Artificial Intelligence ◽  
Autonomous Robots ◽  
Healthcare Delivery ◽  
Data Interpretation ◽  
Review Article ◽  
Physiological Data ◽  
Main Body ◽  
Thoracic Imaging ◽  
Thoracic Surgical Procedures ◽  
Artificial Intelligence Technology

Abstract Background We have recently witnessed incredible interest in computer-based, internet web-dependent mechanisms and artificial intelligence (AI)-dependent technique emergence in our day-to-day lives. In the recent era of COVID-19 pandemic, this nonhuman, machine-based technology has gained a lot of momentum. Main body of the abstract The supercomputers and robotics with AI technology have shown the potential to equal or even surpass human experts’ accuracy in some tasks in the future. Artificial intelligence (AI) is prompting massive data interweaving with elements from many digital sources such as medical imaging sorting, electronic health records, and transforming healthcare delivery. But in thoracic surgical and our counterpart pulmonary medical field, AI’s main applications are still for interpretation of thoracic imaging, lung histopathological slide evaluation, physiological data interpretation, and biosignal testing only. The query arises whether AI-enabled technology-based or autonomous robots could ever do or provide better thoracic surgical procedures than current surgeons but it seems like an impossibility now. Short conclusion This review article aims to provide information pertinent to the use of AI to thoracic surgical specialists. In this review article, we described AI and related terminologies, current utilisation, challenges, potential, and current need for awareness of this technology.

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