scholarly journals Artificial Intelligence (Online Resource): A panacea for SMEs in healthcare

2021 ◽  
Vol 16 (4) ◽  
pp. 230-235
Author(s):  
ANUJ KUMAR ◽  
Asif Ali Syed ◽  
Anoop Pandey
Keyword(s):  
Artificial Intelligence ◽  
Health Care ◽  
Medical Device ◽  
Circular Economy ◽  
Emerging Technology ◽  
Healthcare Sector ◽  
Research Papers ◽  
Private Hospitals ◽  
Indian Medical ◽  
Online Resource

This paper presents a review of the most recent and popular research papers on the use of artificial intelligence in the healthcare sector. SMEs consist of 60-65% of Indian medical device market. Many doctors are operating through private hospitals which come under the category of SMEs segment. Technology is proving to be a boon for all the sectors, artificial intelligence an emerging technology has the potential to change the fortune of SMEs in health care sector. In this paper, there will be discussion on how artificial intelligence can help the healthcare sector in different ways. SMEs working in healthcare can take a learning from this paper and can utilize it for betterment. (*The paper was presented at the AICTE International Conference on Circular Economy, Management and Industry, Bharati Vidyapeeth’s Institute of Management Studies and Research, Navi Mumbai and Apeejay School of Management, Dwarka, Delhi, India. October 2021)

A Study on Role of A.I. and Robotics in Health Care Sector: Its Impact and Contribution

2021 ◽  
Vol 07 (3&4) ◽  
pp. 7-14
Author(s):  
Devnath Jayaswal ◽  
Keyword(s):  
Artificial Intelligence ◽  
Health Care ◽  
Deep Learning ◽  
Health Care Sector ◽  
Healthcare Sector ◽  
Current Scenario ◽  
Health Complications ◽  
And Robotics ◽  
Major Domain

Health Care is one of the major domain sectors of our country. As this domain has many different aspect of implementation, as per the current scenario of Diseases and health complications. This paper will discuss about how, the Artificial Intelligence (A.I.) and robotics can be beneficial and plays a major role on, health care domain with respect to the Efficiently Diagnose, Developing New Medicines, Earlier Detection of Diseases, Advance Treatment Care, A.I-Deep learning For the Critical Decision’s. As this Information will help to give more clarity on what, A.I. & Robotics contributes for the major Diseases Treatment by the advancement of Technology. This can be beneficial for not only Doctors, Patients, or Firm but can also be helpful for citizen people as well. The objective of this paper is to study the role of AI and Robotics in Healthcare Sector and its impact.

scholarly journals Accelerating AI Adoption with Responsible AI Signals and Employee Engagement Mechanisms in Health Care

2021 ◽  
Author(s):  
Weisha Wang ◽  
Long Chen ◽  
Mengran Xiong ◽  
Yichuan Wang
Keyword(s):  
Artificial Intelligence ◽  
Health Care ◽  
Employee Engagement ◽  
Mediating Effect ◽  
Healthcare Sector ◽  
Ethical Implications ◽  
Underlying Mechanisms ◽  
The Relationship

AbstractArtificial Intelligence (AI) technology is transforming the healthcare sector. However, despite this, the associated ethical implications remain open to debate. This research investigates how signals of AI responsibility impact healthcare practitioners’ attitudes toward AI, satisfaction with AI, AI usage intentions, including the underlying mechanisms. Our research outlines autonomy, beneficence, explainability, justice, and non-maleficence as the five key signals of AI responsibility for healthcare practitioners. The findings reveal that these five signals significantly increase healthcare practitioners’ engagement, which subsequently leads to more favourable attitudes, greater satisfaction, and higher usage intentions with AI technology. Moreover, ‘techno-overload’ as a primary ‘techno-stressor’ moderates the mediating effect of engagement on the relationship between AI justice and behavioural and attitudinal outcomes. When healthcare practitioners perceive AI technology as adding extra workload, such techno-overload will undermine the importance of the justice signal and subsequently affect their attitudes, satisfaction, and usage intentions with AI technology.

The circular economy meets artificial intelligence (AI): understanding the opportunities of AI for reverse logistics

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Matthew Wilson ◽  
Jeannette Paschen ◽  
Leyland Pitt
Keyword(s):  
Artificial Intelligence ◽  
Reverse Logistics ◽  
Circular Economy ◽  
Design Methodology ◽  
Emerging Technology ◽  
Future Research ◽  
Entrepreneurial Ecosystem ◽  
Content Type ◽  
Entrepreneurial Opportunities ◽  
Logistics Process

PurposeTechnology is an important force in the entrepreneurial ecosystem as it has the potential to impact entrepreneurial opportunities and processes. This paper explores the emerging technology of artificial intelligence (AI) and its implications for reverse logistics within the circular economy (CE). It considers key reverse logistics functions and outlines how AI is known to, or has the potential to, impact these functions.Design/methodology/approachThe paper is conceptual and utilizes the literature from entrepreneurship, the CE and reverse logistics to explore the implications of AI for reverse logistics functions.FindingsAI provides significant benefits across all functions and tasks in the reverse logistics process; however, the various reverse logistics functions and tasks rely on different forms of AI (mechanical, analytical, intuitive).Research limitations/implicationsThe paper highlights the importance of technology, and in particular AI, as a key force in the digital entrepreneurial ecosystem and discusses the specific implications of AI for entrepreneurial practice. For researchers, the paper outlines avenues for future research within the entrepreneurship and/or CE domains of the study.Originality/valueThis paper is the first to present a structured discussion of AI's implications for reverse logistics functions and tasks. It addresses a call for more research on AI and its opportunities for the CE and emphasizes the importance of emerging technologies, particularly AI, as an external force within the entrepreneurial ecosystem. The paper also outlines avenues for future research on AI in reverse logistics.

scholarly journals The Beginning of a New Era: Artificial Intelligence in Healthcare

2020 ◽  
Vol 11 (3) ◽  
pp. 414-425
Author(s):  
Akshara Kumar ◽  
Shivaprasad Gadag ◽  
Usha Yogendra Nayak
Keyword(s):  
Artificial Intelligence ◽  
Health Care ◽  
Drug Discovery ◽  
Science Fiction ◽  
Patient Monitoring ◽  
Basic Knowledge ◽  
Healthcare Sector ◽  
Human Beings ◽  
External Data ◽  
Applications Of Ai

The healthcare sector is considered to be one of the largest and fast-growing industries in the world. Innovations and novel approaches have always remained the prime aims in order to bring massive development. Before the emergence of technology, all the sectors, including the healthcare sector was dependant dependent on man power, which was time-consuming, and less accurate with lack of efficiency. With the recent advancements in machine learning, the condition is has been steadily revolutionizing. in the practice of the health care industry. Artificial Intelligence intelligence (AI) lies in the computer science department, which stresses on the intelligent machines’ creation, that work and react just like human beings. In simple words, AI is the capability of a computer program to think and learn, almost satisfying natural intelligence. It is the ability of a system to interpret the external data correctly, learn from it and finally use those learnings to execute some particular goals and tasks through adaptation. It utilizes multiple technologies to comprehend, act and understand from past experiences. Involving AI is not a science fiction that was once a very long time ago. It AI being an emerging technology has been adopted in various facets of healthcare ranging from drug discovery to patient monitoring. rapidly penetrated its wings developed itself into almost all the industries. Irrespective of the person’s background, whether he/she is a student, industry worker, an entrepreneur, or a scientist, having basic knowledge about the importance and applications of AI would be impactful. Currently, the applications of AI has have been expanding into those fields, which was once thought to be the only domain of human expertise such as health care sector. In this review article, we have shedthrown light on the present usage of AI in the healthcare sector, such as its working, and the way this system is being implemented in different domains, such as drug discovery, diagnosis of diseases, clinical trials, remote patient monitoring, and nanotechnology. We have also slightlybriefly touched upon its applications in touching other sectors as well. The public opinions have also been analyszed and discussed along with the future prospects.The main goals have been briefed. prospects. We have discussed the Along with the merits, we have also discussed about and the other side of AI, i.e. the disadvantages of this as wellin the last part of the manuscript.

scholarly journals Strategic Investment Decisions for Emerging Technology Fields in the Health Care Sector Based on M&A Analysis

2021 ◽  
Vol 13 (7) ◽  
pp. 3644
Author(s):  
Jinho Choi ◽  
Nina Shin ◽  
Yong Sik Chang
Keyword(s):  
Health Care ◽  
Emerging Technologies ◽  
Time Lag ◽  
Emerging Technology ◽  
Health Care Sector ◽  
Growth Potential ◽  
Healthcare Sector ◽  
Integrative Approach ◽  
Activity Data ◽  
Strategic Investment

The existing approaches to identification of emerging technologies create a prominent opportunity for technology convergence and market growth potential. However, existing approaches either suffer from the time lag issue or have yet to explorethe assessment’s uncertainty and ambiguity. Based on a total of 14 years of mergers and acquisitions (M&A) activity data in the Health Care sector, the complex patterns between growth velocity and accelerating of M&A activities are analyzed with two quantitative indicators (Promising Index and Promising Index Sharpe Ratio) to identify emerging technological opportunities. The proposed integrative approach offers a mean to resolve the time lag issue, deal with market trend irregularity, and manage expectations of investors for emerging technology and industry. Specifically, this study aims to (i) provide a decision support system integrating M&A activity information for strategic investment planning and (ii) identify promising technologies in the Healthcare sector to manage the irregularities of market trend and investment outcome. This study is one of the first research that employs a prior data-based approach to delineate emerging technologies by analyzing the growth momentum properties of specific industry areas based on the M&A activity data.

scholarly journals Challenges of Applying AI in Healthcare in India

2021 ◽  
pp. 203-209
Author(s):  
Verda Nizam ◽  
Avinash Aslekar
Keyword(s):  
Artificial Intelligence ◽  
Health Care ◽  
Rural Communities ◽  
Health Care Services ◽  
Clinical Decision ◽  
Healthcare Services ◽  
Healthcare Sector ◽  
Care Services ◽  
Massive Change ◽  
Front Line Workers

With the advent of digitalization, upcoming technologies like Artificial Intelligence (AI) are being utilized by healthcare services to manage various healthcare services to mimic human cognitive functions. This technology is expected to bring about a massive change in healthcare. Patient management, clinical decision support, patient tracking, and health care services are the four main AI-enabled fields of the healthcare industry. The method carrying out the study was based on secondary research by the themes of the studies performed earlier using Artificial intelligence in healthcare sector, through observations, interviews and valid documentations from prominent databases, by means of challenges and its analysis and the last by the issues associated with the study and the target groups are the front line workers in healthcare sectors. The AI applications in health care have gathered much attention, but AI's adoption issues have not been significantly tended. There are several challenges of its implementation, such as resolving the unequal relationship between trained physicians and patients and increasing physicians' efficiency to be more effective in their work; providing AI-enabled healthcare equipment in rural communities; and educating physicians or doctors in handling it. AI technologies have the potential to enhance patient outcomes. Still, they may also pose significant risks in terms of inadequate patient risk assessment, medical error, and suggestions for treatment, privacy violations, and others.

scholarly journals Artificial Intelligence in Health Care: Bibliometric Analysis (Preprint)

2020 ◽  
Author(s):  
Yuqi Guo ◽  
Zhichao Hao ◽  
Shichong Zhao ◽  
Jiaqi Gong ◽  
Fan Yang
Keyword(s):  
Artificial Intelligence ◽  
Neural Networks ◽  
Health Care ◽  
Growth Rate ◽  
Bibliometric Analysis ◽  
Convolutional Neural Networks ◽  
Health Care Research ◽  
Support Vector ◽  
Research Papers ◽  
Care Research

BACKGROUND As a critical driving power to promote health care, the health care–related artificial intelligence (AI) literature is growing rapidly. OBJECTIVE The purpose of this analysis is to provide a dynamic and longitudinal bibliometric analysis of health care–related AI publications. METHODS The Web of Science (Clarivate PLC) was searched to retrieve all existing and highly cited AI-related health care research papers published in English up to December 2019. Based on bibliometric indicators, a search strategy was developed to screen the title for eligibility, using the abstract and full text where needed. The growth rate of publications, characteristics of research activities, publication patterns, and research hotspot tendencies were computed using the HistCite software. RESULTS The search identified 5235 hits, of which 1473 publications were included in the analyses. Publication output increased an average of 17.02% per year since 1995, but the growth rate of research papers significantly increased to 45.15% from 2014 to 2019. The major health problems studied in AI research are cancer, depression, Alzheimer disease, heart failure, and diabetes. Artificial neural networks, support vector machines, and convolutional neural networks have the highest impact on health care. Nucleosides, convolutional neural networks, and tumor markers have remained research hotspots through 2019. CONCLUSIONS This analysis provides a comprehensive overview of the AI-related research conducted in the field of health care, which helps researchers, policy makers, and practitioners better understand the development of health care–related AI research and possible practice implications. Future AI research should be dedicated to filling in the gaps between AI health care research and clinical applications.

scholarly journals Artificial Intelligence in Health Care: Bibliometric Analysis

10.2196/18228 ◽  
2020 ◽  
Vol 22 (7) ◽  
pp. e18228 ◽  
Author(s):  
Yuqi Guo ◽  
Zhichao Hao ◽  
Shichong Zhao ◽  
Jiaqi Gong ◽  
Fan Yang
Keyword(s):  
Artificial Intelligence ◽  
Neural Networks ◽  
Health Care ◽  
Growth Rate ◽  
Bibliometric Analysis ◽  
Convolutional Neural Networks ◽  
Health Care Research ◽  
Support Vector ◽  
Research Papers ◽  
Care Research

Background As a critical driving power to promote health care, the health care–related artificial intelligence (AI) literature is growing rapidly. Objective The purpose of this analysis is to provide a dynamic and longitudinal bibliometric analysis of health care–related AI publications. Methods The Web of Science (Clarivate PLC) was searched to retrieve all existing and highly cited AI-related health care research papers published in English up to December 2019. Based on bibliometric indicators, a search strategy was developed to screen the title for eligibility, using the abstract and full text where needed. The growth rate of publications, characteristics of research activities, publication patterns, and research hotspot tendencies were computed using the HistCite software. Results The search identified 5235 hits, of which 1473 publications were included in the analyses. Publication output increased an average of 17.02% per year since 1995, but the growth rate of research papers significantly increased to 45.15% from 2014 to 2019. The major health problems studied in AI research are cancer, depression, Alzheimer disease, heart failure, and diabetes. Artificial neural networks, support vector machines, and convolutional neural networks have the highest impact on health care. Nucleosides, convolutional neural networks, and tumor markers have remained research hotspots through 2019. Conclusions This analysis provides a comprehensive overview of the AI-related research conducted in the field of health care, which helps researchers, policy makers, and practitioners better understand the development of health care–related AI research and possible practice implications. Future AI research should be dedicated to filling in the gaps between AI health care research and clinical applications.

scholarly journals Application of Artificial Intelligence in Healthcare: Chances and Challenges

2021 ◽  
pp. 78-89
Author(s):  
Ravi Manne ◽  
Sneha C. Kantheti
Keyword(s):  
Artificial Intelligence ◽  
Health Care ◽  
Drug Design ◽  
Healthcare Management ◽  
Patient Needs ◽  
Research Papers ◽  
Reducing Costs

Use of Artificial intelligence (AI) has increased in the healthcare in many sectors. Organizations from health care of different sizes, types and different specialties are now a days more interested in how artificial intelligence has evolved and is helping patient needs and their care, also reducing costs, and increasing efficiency. This study explores the implications of AI on healthcare management, and challenges involved with using AI in healthcare along with the review of several research papers that used AI models in different sectors of healthcare like Dermatology, Radiology, Drug design etc.

Developing an Artificial Intelligence Project in your Radiology Department

2020 ◽  
Vol 2 ◽  
pp. 58-61 ◽  
Author(s):  
Syed Junaid ◽  
Asad Saeed ◽  
Zeili Yang ◽  
Thomas Micic ◽  
Rajesh Botchu
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Health Care ◽  
Deep Learning ◽  
Learning Algorithms ◽  
Radiology Department ◽  
Short Article ◽  
Computing Power ◽  
Road Map ◽  
Key Concepts

The advances in deep learning algorithms, exponential computing power, and availability of digital patient data like never before have led to the wave of interest and investment in artificial intelligence in health care. No radiology conference is complete without a substantial dedication to AI. Many radiology departments are keen to get involved but are unsure of where and how to begin. This short article provides a simple road map to aid departments to get involved with the technology, demystify key concepts, and pique an interest in the field. We have broken down the journey into seven steps; problem, team, data, kit, neural network, validation, and governance.

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