1
|
Giacobbe DR, Marelli C, Guastavino S, Signori A, Mora S, Rosso N, Campi C, Piana M, Murgia Y, Giacomini M, Bassetti M. Artificial intelligence and prescription of antibiotic therapy: present and future. Expert Rev Anti Infect Ther 2024:1-15. [PMID: 39155449 DOI: 10.1080/14787210.2024.2386669] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/20/2024] [Accepted: 07/28/2024] [Indexed: 08/20/2024]
Abstract
INTRODUCTION In the past few years, the use of artificial intelligence in healthcare has grown exponentially. Prescription of antibiotics is not exempt from its rapid diffusion, and various machine learning (ML) techniques, from logistic regression to deep neural networks and large language models, have been explored in the literature to support decisions regarding antibiotic prescription. AREAS COVERED In this narrative review, we discuss promises and challenges of the application of ML-based clinical decision support systems (ML-CDSSs) for antibiotic prescription. A search was conducted in PubMed up to April 2024. EXPERT OPINION Prescribing antibiotics is a complex process involving various dynamic phases. In each of these phases, the support of ML-CDSSs has shown the potential, and also the actual ability in some studies, to favorably impacting relevant clinical outcomes. Nonetheless, before widely exploiting this massive potential, there are still crucial challenges ahead that are being intensively investigated, pertaining to the transparency of training data, the definition of the sufficient degree of prediction explanations when predictions are obtained through black box models, and the legal and ethical framework for decision responsibility whenever an antibiotic prescription is supported by ML-CDSSs.
Collapse
Affiliation(s)
- Daniele Roberto Giacobbe
- Department of Health Sciences (DISSAL), University of Genoa, Genoa, Italy
- UO Clinica Malattie Infettive, IRCCS Ospedale Policlinico San Martino, Genoa, Italy
| | - Cristina Marelli
- UO Clinica Malattie Infettive, IRCCS Ospedale Policlinico San Martino, Genoa, Italy
| | | | - Alessio Signori
- Section of Biostatistics, Department of Health Sciences (DISSAL), University of Genoa, Genoa, Italy
| | - Sara Mora
- UO Information and Communication Technologies, IRCCS Ospedale Policlinico San Martino, Genoa, Italy
| | - Nicola Rosso
- UO Information and Communication Technologies, IRCCS Ospedale Policlinico San Martino, Genoa, Italy
| | - Cristina Campi
- Department of Mathematics (DIMA), University of Genoa, Genoa, Italy
- Life Science Computational Laboratory (LISCOMP), IRCCS Ospedale Policlinico San Martino, Genoa, Italy
| | - Michele Piana
- Department of Mathematics (DIMA), University of Genoa, Genoa, Italy
- Life Science Computational Laboratory (LISCOMP), IRCCS Ospedale Policlinico San Martino, Genoa, Italy
| | - Ylenia Murgia
- Department of Informatics, Bioengineering, Robotics and System Engineering (DIBRIS), University of Genoa, Genoa, Italy
| | - Mauro Giacomini
- Department of Informatics, Bioengineering, Robotics and System Engineering (DIBRIS), University of Genoa, Genoa, Italy
| | - Matteo Bassetti
- Department of Health Sciences (DISSAL), University of Genoa, Genoa, Italy
- UO Clinica Malattie Infettive, IRCCS Ospedale Policlinico San Martino, Genoa, Italy
| |
Collapse
|
2
|
Jiang P, Niu W, Wang Q, Yuan R, Chen K. Understanding Users' Acceptance of Artificial Intelligence Applications: A Literature Review. Behav Sci (Basel) 2024; 14:671. [PMID: 39199067 PMCID: PMC11351494 DOI: 10.3390/bs14080671] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/24/2024] [Revised: 07/30/2024] [Accepted: 08/01/2024] [Indexed: 09/01/2024] Open
Abstract
In recent years, with the continuous expansion of artificial intelligence (AI) application forms and fields, users' acceptance of AI applications has attracted increasing attention from scholars and business practitioners. Although extant studies have extensively explored user acceptance of different AI applications, there is still a lack of understanding of the roles played by different AI applications in human-AI interaction, which may limit the understanding of inconsistent findings about user acceptance of AI. This study addresses this issue by conducting a systematic literature review on AI acceptance research in leading journals of Information Systems and Marketing disciplines from 2020 to 2023. Based on a review of 80 papers, this study made contributions by (i) providing an overview of methodologies and theoretical frameworks utilized in AI acceptance research; (ii) summarizing the key factors, potential mechanisms, and theorization of users' acceptance response to AI service providers and AI task substitutes, respectively; and (iii) proposing opinions on the limitations of extant research and providing guidance for future research.
Collapse
Affiliation(s)
- Pengtao Jiang
- School of Information Science and Engineering, NingboTech University, Ningbo 315100, China;
- Nottingham University Business School China, University of Nottingham Ningbo China, Ningbo 315100, China;
| | - Wanshu Niu
- Business School, Ningbo University, Ningbo 315211, China;
| | - Qiaoli Wang
- School of Management, Zhejiang University, Hangzhou 310058, China;
| | - Ruizhi Yuan
- Nottingham University Business School China, University of Nottingham Ningbo China, Ningbo 315100, China;
| | - Keyu Chen
- Business School, Ningbo University, Ningbo 315211, China;
| |
Collapse
|
3
|
Bankins S, Hu X, Yuan Y. Artificial intelligence, workers, and future of work skills. Curr Opin Psychol 2024; 58:101828. [PMID: 38964241 DOI: 10.1016/j.copsyc.2024.101828] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2024] [Revised: 06/14/2024] [Accepted: 06/18/2024] [Indexed: 07/06/2024]
Abstract
Historically, the use of technology in organizations has reshaped the nature of human work. In this article, we overview how current waves of artificially intelligent (AI) technologies are following this trend, showing how its uses can both automate and complement human labor, alongside creating new forms of human work. However, AI can also generate both upsides and downsides for workers' experiences, which are dependent upon a range of factors such as how the technology is used and the support employees receive during digital transitions. We conclude by outlining how AI literacy and other human-centered skills will play an increasingly important role in future workplaces.
Collapse
Affiliation(s)
- Sarah Bankins
- Department of Management, Macquarie Business School, Macquarie University, Wallumattagal Campus, Sydney, NSW, Australia.
| | - Xinyu Hu
- School of Business, Renmin University of China, 59 Zhongguancun Street, Haidian District, Beijing, China
| | - Yunyun Yuan
- School of Management, Beijing Institute of Technology, Zhongguancun, Beijing, China
| |
Collapse
|
4
|
Hirosawa T, Harada Y, Mizuta K, Sakamoto T, Tokumasu K, Shimizu T. Evaluating ChatGPT-4's Accuracy in Identifying Final Diagnoses Within Differential Diagnoses Compared With Those of Physicians: Experimental Study for Diagnostic Cases. JMIR Form Res 2024; 8:e59267. [PMID: 38924784 PMCID: PMC11237772 DOI: 10.2196/59267] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2024] [Revised: 04/28/2024] [Accepted: 05/04/2024] [Indexed: 06/28/2024] Open
Abstract
BACKGROUND The potential of artificial intelligence (AI) chatbots, particularly ChatGPT with GPT-4 (OpenAI), in assisting with medical diagnosis is an emerging research area. However, it is not yet clear how well AI chatbots can evaluate whether the final diagnosis is included in differential diagnosis lists. OBJECTIVE This study aims to assess the capability of GPT-4 in identifying the final diagnosis from differential-diagnosis lists and to compare its performance with that of physicians for case report series. METHODS We used a database of differential-diagnosis lists from case reports in the American Journal of Case Reports, corresponding to final diagnoses. These lists were generated by 3 AI systems: GPT-4, Google Bard (currently Google Gemini), and Large Language Models by Meta AI 2 (LLaMA2). The primary outcome was focused on whether GPT-4's evaluations identified the final diagnosis within these lists. None of these AIs received additional medical training or reinforcement. For comparison, 2 independent physicians also evaluated the lists, with any inconsistencies resolved by another physician. RESULTS The 3 AIs generated a total of 1176 differential diagnosis lists from 392 case descriptions. GPT-4's evaluations concurred with those of the physicians in 966 out of 1176 lists (82.1%). The Cohen κ coefficient was 0.63 (95% CI 0.56-0.69), indicating a fair to good agreement between GPT-4 and the physicians' evaluations. CONCLUSIONS GPT-4 demonstrated a fair to good agreement in identifying the final diagnosis from differential-diagnosis lists, comparable to physicians for case report series. Its ability to compare differential diagnosis lists with final diagnoses suggests its potential to aid clinical decision-making support through diagnostic feedback. While GPT-4 showed a fair to good agreement for evaluation, its application in real-world scenarios and further validation in diverse clinical environments are essential to fully understand its utility in the diagnostic process.
Collapse
Affiliation(s)
- Takanobu Hirosawa
- Department of Diagnostic and Generalist Medicine, Dokkyo Medical University, Tochigi, Japan
| | - Yukinori Harada
- Department of Diagnostic and Generalist Medicine, Dokkyo Medical University, Tochigi, Japan
| | - Kazuya Mizuta
- Department of Diagnostic and Generalist Medicine, Dokkyo Medical University, Tochigi, Japan
| | - Tetsu Sakamoto
- Department of Diagnostic and Generalist Medicine, Dokkyo Medical University, Tochigi, Japan
| | - Kazuki Tokumasu
- Department of General Medicine, Okayama University Graduate School of Medicine, Dentistry and Pharmaceutical Sciences, Okayama, Japan
| | - Taro Shimizu
- Department of Diagnostic and Generalist Medicine, Dokkyo Medical University, Tochigi, Japan
| |
Collapse
|
5
|
Negre P, Alonso RS, González-Briones A, Prieto J, Rodríguez-González S. Literature Review of Deep-Learning-Based Detection of Violence in Video. SENSORS (BASEL, SWITZERLAND) 2024; 24:4016. [PMID: 38931796 PMCID: PMC11207446 DOI: 10.3390/s24124016] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/17/2024] [Revised: 06/18/2024] [Accepted: 06/19/2024] [Indexed: 06/28/2024]
Abstract
Physical aggression is a serious and widespread problem in society, affecting people worldwide. It impacts nearly every aspect of life. While some studies explore the root causes of violent behavior, others focus on urban planning in high-crime areas. Real-time violence detection, powered by artificial intelligence, offers a direct and efficient solution, reducing the need for extensive human supervision and saving lives. This paper is a continuation of a systematic mapping study and its objective is to provide a comprehensive and up-to-date review of AI-based video violence detection, specifically in physical assaults. Regarding violence detection, the following have been grouped and categorized from the review of the selected papers: 21 challenges that remain to be solved, 28 datasets that have been created in recent years, 21 keyframe extraction methods, 16 types of algorithm inputs, as well as a wide variety of algorithm combinations and their corresponding accuracy results. Given the lack of recent reviews dealing with the detection of violence in video, this study is considered necessary and relevant.
Collapse
Affiliation(s)
- Pablo Negre
- BISITE Research Group, Universidad de Salamanca, Patio de Escuelas, 37008 Salamanca, Spain; (A.G.-B.); (S.R.-G.)
| | - Ricardo S. Alonso
- AIR Institute, Av. Santiago Madrigal, 37008 Salamanca, Spain;
- UNIR (International University of La Rioja), Av. de la Paz, 137, 26006 Logroño, Spain
| | - Alfonso González-Briones
- BISITE Research Group, Universidad de Salamanca, Patio de Escuelas, 37008 Salamanca, Spain; (A.G.-B.); (S.R.-G.)
| | - Javier Prieto
- BISITE Research Group, Universidad de Salamanca, Patio de Escuelas, 37008 Salamanca, Spain; (A.G.-B.); (S.R.-G.)
| | - Sara Rodríguez-González
- BISITE Research Group, Universidad de Salamanca, Patio de Escuelas, 37008 Salamanca, Spain; (A.G.-B.); (S.R.-G.)
| |
Collapse
|
6
|
De Haro LP. Biosecurity Risk Assessment for the Use of Artificial Intelligence in Synthetic Biology. APPLIED BIOSAFETY 2024; 29:96-107. [PMID: 39131181 PMCID: PMC11313549 DOI: 10.1089/apb.2023.0031] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/13/2024]
Abstract
Background The integration of Artificial Intelligence (AI) with synthetic biology is driving unprecedented progress in both fields. However, this integration introduces complex biosecurity challenges. Addressing these concerns, this article proposes a specialized biosecurity risk assessment process designed to evaluate the incorporation of AI in synthetic biology. Methods A set of tailored tools and methodology was developed for conducting biosecurity risk assessments of AI language models used for synthetic biology. These resources were developed to guide risk management professionals through a systematic process of identifying, evaluating, and mitigating potential risks. Results The tools and methodology provided offer a structured approach to risk assessment, enabling risk management professionals to comprehensively analyze the biosecurity implications of AI applications in synthetic biology. They facilitate the identification of potential risks and the development of effective mitigation strategies. An example of a risk assessment performed on the large language model "ChatGPT 4.0" is provided here. Conclusion AI's role in synthetic biology is rapidly expanding; thus, establishing proactive and secure practices is crucial. The biosecurity risk assessment tools and methodology presented here are the first provided in the literature and will be instrumental steps toward the responsible integration of AI in synthetic biology. By adopting these resources, the biorisk management community can effectively navigate and manage the biosecurity challenges posed by AI, ensuring its responsible and secure application in the field of synthetic biology.
Collapse
Affiliation(s)
- Leyma P. De Haro
- Environmental Health and Safety, California Institute of Technology, Pasadena, California, USA
| |
Collapse
|
7
|
Ashkanani Z, Mohtar R, Al-Enezi S, Smith PK, Calabrese S, Ma X, Abdullah M. AI-assisted systematic review on remediation of contaminated soils with PAHs and heavy metals. JOURNAL OF HAZARDOUS MATERIALS 2024; 468:133813. [PMID: 38402679 DOI: 10.1016/j.jhazmat.2024.133813] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/16/2023] [Revised: 02/05/2024] [Accepted: 02/15/2024] [Indexed: 02/27/2024]
Abstract
This systematic review addresses soil contamination by crude oil, a pressing global environmental issue, by exploring effective treatment strategies for sites co-contaminated with heavy metals and polycyclic aromatic hydrocarbons (PAHs). Our study aims to answer pivotal research questions: (1) What are the interaction mechanisms between heavy metals and PAHs in contaminated soils, and how do these affect the efficacy of different remediation methods? (2) What are the challenges and limitations of combined remediation techniques for co-contaminated soils compared to single-treatment methods in terms of efficiency, stability, and specificity? (3) How do various factors influence the effectiveness of biological, chemical, and physical remediation methods, both individually and combined, in co-contaminated soils, and what role do specific agents play in the degradation, immobilization, or removal of heavy metals and PAHs under diverse environmental conditions? (4) Do AI-powered search tools offer a superior alternative to conventional search methodologies for executing an exhaustive systematic review? Utilizing big-data analytics and AI tools such as Litmaps.co, ResearchRabbit, and MAXQDA, this study conducts a thorough analysis of remediation techniques for soils co-contaminated with heavy metals and PAHs. It emphasizes the significance of cation-π interactions and soil composition in dictating the solubility and behavior of these pollutants. The study pays particular attention to the interplay between heavy metals and PAH solubility, as well as the impact of soil properties like clay type and organic matter on heavy metal adsorption, which results in nonlinear sorption patterns. The research identifies a growing trend towards employing combined remediation techniques, especially biological strategies like biostimulation-bioaugmentation, noting their effectiveness in laboratory settings, albeit with potentially higher costs in field applications. Plants such as Medicago sativa L. and Solanum nigrum L. are highlighted for their effectiveness in phytoremediation, working synergistically with beneficial microbes to decompose contaminants. Furthermore, the study illustrates that the incorporation of biochar and surfactants, along with chelating agents like EDTA, can significantly enhance treatment efficiency. However, the research acknowledges that varying environmental conditions necessitate site-specific adaptations in remediation strategies. Life Cycle Assessment (LCA) findings indicate that while high-energy methods like Steam Enhanced Extraction and Thermal Resistivity - ERH are effective, they also entail substantial environmental and financial costs. Conversely, Natural Attenuation, despite being a low-impact and cost-effective option, may require prolonged monitoring. The study advocates for an integrative approach to soil remediation, one that harmoniously balances environmental sustainability, cost-effectiveness, and the specific requirements of contaminated sites. It underscores the necessity of a holistic strategy that combines various remediation methods, tailored to meet both regulatory compliance and the long-term sustainability of decontamination efforts.
Collapse
Affiliation(s)
- Zainab Ashkanani
- Department of Biological and Agricultural Engineering, Texas A&M University, College Station, TX 77843, USA.
| | - Rabi Mohtar
- Department of Biological and Agricultural Engineering, Texas A&M University, College Station, TX 77843, USA
| | - Salah Al-Enezi
- Petroleum Research Center, Kuwait Institute for Scientific Research, Al-Ahmadi, Kuwait
| | - Patricia K Smith
- Department of Biological and Agricultural Engineering, Texas A&M University, College Station, TX 77843, USA
| | - Salvatore Calabrese
- Department of Biological and Agricultural Engineering, Texas A&M University, College Station, TX 77843, USA
| | - Xingmao Ma
- Department of Civil and Environmental Engineering, Texas A&M University, College Station, TX 77840, USA
| | - Meshal Abdullah
- Sultan Qaboos University, College of Arts & Social Sciences. Al-Khoud, Sultanate of Oman
| |
Collapse
|
8
|
Al-Anazi S, Al-Omari A, Alanazi S, Marar A, Asad M, Alawaji F, Alwateid S. Artificial intelligence in respiratory care: Current scenario and future perspective. Ann Thorac Med 2024; 19:117-130. [PMID: 38766378 PMCID: PMC11100474 DOI: 10.4103/atm.atm_192_23] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/13/2023] [Revised: 09/08/2023] [Accepted: 09/11/2023] [Indexed: 05/22/2024] Open
Abstract
BACKGROUND This narrative review aims to explore the current state and future perspective of artificial intelligence (AI) in respiratory care. The objective is to provide insights into the potential impact of AI in this field. METHODS A comprehensive analysis of relevant literature and research studies was conducted to examine the applications of AI in respiratory care and identify areas of advancement. The analysis included studies on remote monitoring, early detection, smart ventilation systems, and collaborative decision-making. RESULTS The obtained results highlight the transformative potential of AI in respiratory care. AI algorithms have shown promising capabilities in enabling tailored treatment plans based on patient-specific data. Remote monitoring using AI-powered devices allows for real-time feedback to health-care providers, enhancing patient care. AI algorithms have also demonstrated the ability to detect respiratory conditions at an early stage, leading to timely interventions and improved outcomes. Moreover, AI can optimize mechanical ventilation through continuous monitoring, enhancing patient comfort and reducing complications. Collaborative AI systems have the potential to augment the expertise of health-care professionals, leading to more accurate diagnoses and effective treatment strategies. CONCLUSION By improving diagnosis, AI has the potential to revolutionize respiratory care, treatment planning, and patient monitoring. While challenges and ethical considerations remain, the transformative impact of AI in this domain cannot be overstated. By leveraging the advancements and insights from this narrative review, health-care professionals and researchers can continue to harness the power of AI to improve patient outcomes and enhance respiratory care practices. IMPROVEMENTS Based on the findings, future research should focus on refining AI algorithms to enhance their accuracy, reliability, and interpretability. In addition, attention should be given to addressing ethical considerations, ensuring data privacy, and establishing regulatory frameworks to govern the responsible implementation of AI in respiratory care.
Collapse
Affiliation(s)
- Saad Al-Anazi
- Lead Clincial Appliaction AzeerTrade (Lowenstein Medical Company), Riyadh, Saudi Arabia
| | - Awad Al-Omari
- Department of Intensive Care, Dr. Sulaiman Al-Habib Group Hospitals, Riyadh, Saudi Arabia
| | - Safug Alanazi
- Intensivist, Al Hammadi Hospital, Riyadh, Saudi Arabia
| | - Aqeelah Marar
- Respiratory Care Administration, King Fahad Medical City, Riyadh, Saudi Arabia
| | - Mohammed Asad
- Department of Emergency, Dr. Sulaiman Al-Habib Group Hospitals, Riyadh, Saudi Arabia
| | - Fadi Alawaji
- Ar Rass General Hospital, Qassim Health Cluster, Senior Laboratory Specialist, Rass Region, Qassim City, Saudi Arabia
| | - Salman Alwateid
- Respiratory Care Administration, King Fahad Medical City, Riyadh, Saudi Arabia
| |
Collapse
|
9
|
Maramraju S, Kowalczewski A, Kaza A, Liu X, Singaraju JP, Albert MV, Ma Z, Yang H. AI-organoid integrated systems for biomedical studies and applications. Bioeng Transl Med 2024; 9:e10641. [PMID: 38435826 PMCID: PMC10905559 DOI: 10.1002/btm2.10641] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2023] [Revised: 12/07/2023] [Accepted: 12/13/2023] [Indexed: 03/05/2024] Open
Abstract
In this review, we explore the growing role of artificial intelligence (AI) in advancing the biomedical applications of human pluripotent stem cell (hPSC)-derived organoids. Stem cell-derived organoids, these miniature organ replicas, have become essential tools for disease modeling, drug discovery, and regenerative medicine. However, analyzing the vast and intricate datasets generated from these organoids can be inefficient and error-prone. AI techniques offer a promising solution to efficiently extract insights and make predictions from diverse data types generated from microscopy images, transcriptomics, metabolomics, and proteomics. This review offers a brief overview of organoid characterization and fundamental concepts in AI while focusing on a comprehensive exploration of AI applications in organoid-based disease modeling and drug evaluation. It provides insights into the future possibilities of AI in enhancing the quality control of organoid fabrication, label-free organoid recognition, and three-dimensional image reconstruction of complex organoid structures. This review presents the challenges and potential solutions in AI-organoid integration, focusing on the establishment of reliable AI model decision-making processes and the standardization of organoid research.
Collapse
Affiliation(s)
- Sudhiksha Maramraju
- Department of Biomedical EngineeringUniversity of North TexasDentonTexasUSA
- Texas Academy of Mathematics and ScienceUniversity of North TexasDentonTexasUSA
| | - Andrew Kowalczewski
- Department of Biomedical & Chemical EngineeringSyracuse UniversitySyracuseNew YorkUSA
- BioInspired Institute for Material and Living SystemsSyracuse UniversitySyracuseNew YorkUSA
| | - Anirudh Kaza
- Department of Biomedical EngineeringUniversity of North TexasDentonTexasUSA
- Texas Academy of Mathematics and ScienceUniversity of North TexasDentonTexasUSA
| | - Xiyuan Liu
- Department of Mechanical & Aerospace EngineeringSyracuse UniversitySyracuseNew YorkUSA
| | - Jathin Pranav Singaraju
- Department of Biomedical EngineeringUniversity of North TexasDentonTexasUSA
- Texas Academy of Mathematics and ScienceUniversity of North TexasDentonTexasUSA
| | - Mark V. Albert
- Department of Biomedical EngineeringUniversity of North TexasDentonTexasUSA
- Department of Computer Science and EngineeringUniversity of North TexasDentonTexasUSA
| | - Zhen Ma
- Department of Biomedical & Chemical EngineeringSyracuse UniversitySyracuseNew YorkUSA
- BioInspired Institute for Material and Living SystemsSyracuse UniversitySyracuseNew YorkUSA
| | - Huaxiao Yang
- Department of Biomedical EngineeringUniversity of North TexasDentonTexasUSA
| |
Collapse
|
10
|
Howard A, Reza N, Aston S, Woods B, Gerada A, Buchan I, Hope W, Märtson AG. Antimicrobial treatment imprecision: an outcome-based model to close the data-to-action loop. THE LANCET. INFECTIOUS DISEASES 2024; 24:e47-e58. [PMID: 37660712 DOI: 10.1016/s1473-3099(23)00367-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/21/2023] [Revised: 06/01/2023] [Accepted: 06/01/2023] [Indexed: 09/05/2023]
Abstract
Health-care systems, food supply chains, and society in general are threatened by the inexorable rise of antimicrobial resistance. This threat is driven by many factors, one of which is inappropriate antimicrobial treatment. The ability of policy makers and leaders in health care, public health, regulatory agencies, and research and development to deliver frameworks for appropriate, sustainable antimicrobial treatment is hampered by a scarcity of tangible outcome-based measures of the damage it causes. In this Personal View, a mathematically grounded, outcome-based measure of antimicrobial treatment appropriateness, called imprecision, is proposed. We outline a framework for policy makers and health-care leaders to use this metric to deliver more effective antimicrobial stewardship interventions to future patient pathways. This will be achieved using learning antimicrobial systems built on public and practitioner engagement; solid implementation science; advances in artificial intelligence; and changes to regulation, research, and development. The outcomes of this framework would be more ecologically and organisationally sustainable patterns of antimicrobial development, regulation, and prescribing. We discuss practical, ethical, and regulatory considerations involved in the delivery of novel antimicrobial drug development, and policy and patient pathways built on artificial intelligence-augmented measures of antimicrobial treatment imprecision.
Collapse
Affiliation(s)
- Alex Howard
- Department of Antimicrobial Pharmacodynamics and Therapeutics, Institute of Systems, Molecular and Integrative Biology, University of Liverpool, Liverpool, UK; Department of Infection and Immunity, Liverpool Clinical Laboratories, Liverpool University Hospitals NHS Foundation Trust, Royal Liverpool Site, Liverpool, UK.
| | - Nada Reza
- Department of Antimicrobial Pharmacodynamics and Therapeutics, Institute of Systems, Molecular and Integrative Biology, University of Liverpool, Liverpool, UK
| | - Stephen Aston
- Department of Antimicrobial Pharmacodynamics and Therapeutics, Institute of Systems, Molecular and Integrative Biology, University of Liverpool, Liverpool, UK
| | - Beth Woods
- Centre for Health Economics, University of York, Heslington, York, UK
| | - Alessandro Gerada
- Department of Antimicrobial Pharmacodynamics and Therapeutics, Institute of Systems, Molecular and Integrative Biology, University of Liverpool, Liverpool, UK; Department of Infection and Immunity, Liverpool Clinical Laboratories, Liverpool University Hospitals NHS Foundation Trust, Royal Liverpool Site, Liverpool, UK
| | - Iain Buchan
- Department of Public Health, Policy & Systems, Institute of Population Health, University of Liverpool, Liverpool, UK
| | - William Hope
- Department of Antimicrobial Pharmacodynamics and Therapeutics, Institute of Systems, Molecular and Integrative Biology, University of Liverpool, Liverpool, UK; Department of Infection and Immunity, Liverpool Clinical Laboratories, Liverpool University Hospitals NHS Foundation Trust, Royal Liverpool Site, Liverpool, UK
| | - Anne-Grete Märtson
- Department of Antimicrobial Pharmacodynamics and Therapeutics, Institute of Systems, Molecular and Integrative Biology, University of Liverpool, Liverpool, UK
| |
Collapse
|
11
|
Geetha S, Biju AVN. Is green FinTech reshaping the finance sphere? Unravelling through a systematic literature review. ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH INTERNATIONAL 2024; 31:1790-1810. [PMID: 38057679 DOI: 10.1007/s11356-023-31382-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/13/2023] [Accepted: 12/01/2023] [Indexed: 12/08/2023]
Abstract
The revolutionary and transformative potential of FinTech has led to the Green Digital Finance Alliance, noting the dawn of a new era of FinTech-"the green FinTech", yet, surprisingly, the scholarly exploration surrounding climate finance in general and green FinTech and climate FinTech remains restrained. In our attempt to decode the intricate interlinkage between green finance and FinTech, the study wrestles with the theoretical complexity of the "green FinTech" concept through a systematic review of relevant studies and conceptual mapping. We develop a comprehensive grasp of the concept, how to leverage it to combat the pressing climate crisis, and its implications for the FinTech segment-the first of its kind in the scanty green FinTech literature. Based on the PRISMA analysis, we find that green FinTech promotes a green economy through its manifold impact on all aspects of the finance sphere, thereby channelling climate finance and promoting sustainability. It has the power to heighten inclusivity, disclosure, trust, and democratisation, thus reducing information asymmetry and greenwashing. Hence, FinTech integration can be game-changing in eliminating the hurdles before conventional green finance. However, the literature remains fragmented, along with a young, growing green FinTech market. Therefore, this study proposes a framework for future researchers by providing a holistic research agenda to fully integrate "green FinTech" into practical real-world applications.
Collapse
Affiliation(s)
- Sreelekshmi Geetha
- Department of Commerce, School of Business Management and Legal Studies, University of Kerala, Thiruvananthapuram, Kerala, India
| | | |
Collapse
|
12
|
Jeyaraman M, Ratna HVK, Jeyaraman N, Venkatesan A, Ramasubramanian S, Yadav S. Leveraging Artificial Intelligence and Machine Learning in Regenerative Orthopedics: A Paradigm Shift in Patient Care. Cureus 2023; 15:e49756. [PMID: 38161806 PMCID: PMC10757680 DOI: 10.7759/cureus.49756] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 11/28/2023] [Indexed: 01/03/2024] Open
Abstract
The integration of artificial intelligence (AI) and machine learning (ML) into regenerative orthopedics heralds a paradigm shift in clinical methodologies and patient management. This review article scrutinizes AI's role in augmenting diagnostic accuracy, refining predictive models, and customizing patient care in orthopedic medicine. Focusing on innovations such as KeyGene and CellNet, we illustrate AI's adeptness in navigating complex genomic datasets, cellular differentiation, and scaffold biodegradation, which are critical components of tissue engineering. Despite its transformative potential, AI's clinical adoption remains in its infancy, contending with challenges in validation, ethical oversight, and model training for clinical relevance. This review posits AI as a vital complement to human intelligence (HI), advocating for an interdisciplinary approach that merges AI's computational prowess with medical expertise to fulfill precision medicine's promise. By analyzing historical and contemporary developments in AI, from the foundational theories of McCullough and Pitts to sophisticated neural networks, the paper emphasizes the need for a synergistic alliance between AI and HI. This collaboration is imperative for improving surgical outcomes, streamlining therapeutic modalities, and enhancing the quality of patient care. Our article calls for robust interdisciplinary strategies to overcome current obstacles and harness AI's full potential in revolutionizing patient outcomes, thereby significantly contributing to the advancement of regenerative orthopedics and the broader field of scientific research.
Collapse
Affiliation(s)
- Madhan Jeyaraman
- Orthopaedics, ACS Medical College and Hospital, Dr. MGR Educational and Research Institute, Chennai, IND
| | | | - Naveen Jeyaraman
- Orthopaedics, ACS Medical College and Hospital, Dr. MGR Educational and Research Institute, Chennai, IND
| | | | | | - Sankalp Yadav
- Medicine, Shri Madan Lal Khurana Chest Clinic, New Delhi, IND
| |
Collapse
|
13
|
Lai UH, Wu KS, Hsu TY, Kan JKC. Evaluating the performance of ChatGPT-4 on the United Kingdom Medical Licensing Assessment. Front Med (Lausanne) 2023; 10:1240915. [PMID: 37795422 PMCID: PMC10547055 DOI: 10.3389/fmed.2023.1240915] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2023] [Accepted: 08/30/2023] [Indexed: 10/06/2023] Open
Abstract
Introduction Recent developments in artificial intelligence large language models (LLMs), such as ChatGPT, have allowed for the understanding and generation of human-like text. Studies have found LLMs abilities to perform well in various examinations including law, business and medicine. This study aims to evaluate the performance of ChatGPT in the United Kingdom Medical Licensing Assessment (UKMLA). Methods Two publicly available UKMLA papers consisting of 200 single-best-answer (SBA) questions were screened. Nine SBAs were omitted as they contained images that were not suitable for input. Each question was assigned a specialty based on the UKMLA content map published by the General Medical Council. A total of 191 SBAs were inputted in ChatGPT-4 through three attempts over the course of 3 weeks (once per week). Results ChatGPT scored 74.9% (143/191), 78.0% (149/191) and 75.6% (145/191) on three attempts, respectively. The average of all three attempts was 76.3% (437/573) with a 95% confidence interval of (74.46% and 78.08%). ChatGPT answered 129 SBAs correctly and 32 SBAs incorrectly on all three attempts. On three attempts, ChatGPT performed well in mental health (8/9 SBAs), cancer (11/14 SBAs) and cardiovascular (10/13 SBAs). On three attempts, ChatGPT did not perform well in clinical haematology (3/7 SBAs), endocrine and metabolic (2/5 SBAs) and gastrointestinal including liver (3/10 SBAs). Regarding to response consistency, ChatGPT provided correct answers consistently in 67.5% (129/191) of SBAs but provided incorrect answers consistently in 12.6% (24/191) and inconsistent response in 19.9% (38/191) of SBAs, respectively. Discussion and conclusion This study suggests ChatGPT performs well in the UKMLA. There may be a potential correlation between specialty performance. LLMs ability to correctly answer SBAs suggests that it could be utilised as a supplementary learning tool in medical education with appropriate medical educator supervision.
Collapse
Affiliation(s)
- U Hin Lai
- Sandwell and West Birmingham NHS Trust, West Bromwich, United Kingdom
- Aston Medical School, Birmingham, United Kingdom
| | - Keng Sam Wu
- Sandwell and West Birmingham NHS Trust, West Bromwich, United Kingdom
- University Hospitals Birmingham NHS Trust, Birmingham, United Kingdom
| | - Ting-Yu Hsu
- Aston Medical School, Birmingham, United Kingdom
- University Hospitals Birmingham NHS Trust, Birmingham, United Kingdom
| | - Jessie Kai Ching Kan
- Aston Medical School, Birmingham, United Kingdom
- Worcestershire Acute Hospitals NHS Trust, Worcester, United Kingdom
| |
Collapse
|
14
|
Batu T, Lemu HG, Shimels H. Application of Artificial Intelligence for Surface Roughness Prediction of Additively Manufactured Components. MATERIALS (BASEL, SWITZERLAND) 2023; 16:6266. [PMID: 37763543 PMCID: PMC10532807 DOI: 10.3390/ma16186266] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/30/2023] [Revised: 09/02/2023] [Accepted: 09/07/2023] [Indexed: 09/29/2023]
Abstract
Additive manufacturing has gained significant popularity from a manufacturing perspective due to its potential for improving production efficiency. However, ensuring consistent product quality within predetermined equipment, cost, and time constraints remains a persistent challenge. Surface roughness, a crucial quality parameter, presents difficulties in meeting the required standards, posing significant challenges in industries such as automotive, aerospace, medical devices, energy, optics, and electronics manufacturing, where surface quality directly impacts performance and functionality. As a result, researchers have given great attention to improving the quality of manufactured parts, particularly by predicting surface roughness using different parameters related to the manufactured parts. Artificial intelligence (AI) is one of the methods used by researchers to predict the surface quality of additively fabricated parts. Numerous research studies have developed models utilizing AI methods, including recent deep learning and machine learning approaches, which are effective in cost reduction and saving time, and are emerging as a promising technique. This paper presents the recent advancements in machine learning and AI deep learning techniques employed by researchers. Additionally, the paper discusses the limitations, challenges, and future directions for applying AI in surface roughness prediction for additively manufactured components. Through this review paper, it becomes evident that integrating AI methodologies holds great potential to improve the productivity and competitiveness of the additive manufacturing process. This integration minimizes the need for re-processing machined components and ensures compliance with technical specifications. By leveraging AI, the industry can enhance efficiency and overcome the challenges associated with achieving consistent product quality in additive manufacturing.
Collapse
Affiliation(s)
- Temesgen Batu
- Department of Aerospace Engineering, Ethiopian Space Science and Geospatial Institute, Addis Ababa P.O. Box 33679, Ethiopia;
- Center of Armament and High Energy Materials, Institute of Research and Development, Ethiopian Defence University, Bishoftu P.O. Box 1041, Ethiopia
| | - Hirpa G. Lemu
- Department of Mechanical and Structural Engineering and Materials Science, University of Stavanger (UiS), 4036 Stavanger, Norway
| | - Hailu Shimels
- Department of Mechanical Engineering, College of Engineering, Addis Ababa Science and Technology University, Addis Ababa P.O. Box 16417, Ethiopia;
| |
Collapse
|
15
|
Uzir MUH, Bukari Z, Al Halbusi H, Lim R, Wahab SN, Rasul T, Thurasamy R, Jerin I, Chowdhury MRK, Tarofder AK, Yaakop AY, Hamid ABA, Haque A, Rauf A, Eneizan B. Applied artificial intelligence: Acceptance-intention-purchase and satisfaction on smartwatch usage in a Ghanaian context. Heliyon 2023; 9:e18666. [PMID: 37560680 PMCID: PMC10407215 DOI: 10.1016/j.heliyon.2023.e18666] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/28/2023] [Revised: 07/19/2023] [Accepted: 07/24/2023] [Indexed: 08/11/2023] Open
Abstract
Technology and its continuous advancement facilitate human beings to get rid of their criticality and limitation. Applied artificial intelligence (AAI) is one of the latest forms that delimited the limitation of human beings. Smartwatch acts as an applied artificial intelligence to assist various patients to check medical care without going to hospital and physicians. This (three) multiple-study research focused on the intention to use, purchase, and their satisfaction and spread positive word of mouth among others in the Ghanaian. To investigate these issues two renowned theories were underpinned- TAM theory and the Stimulus-Organism-Response (S-O-R). Total 550, 320, and 170 respondents were interviewed with Google forms due to COVID-19 using social media. AI-enabled smartwatch considering Perceived Ease of Use (PEOU), Perceived Usefulness (PU), Perceived Credibility (PC), Perceived Self-Efficacy (PSE), and Perceived Financial Cost (PFC) were significant on intention to adoption and adoption intention on actual purchase. The final study showed device quality, its service level, their usage experience, perceived value, and the extent to which the satisfied customers made positive word of mouth to their friends and family, colleagues and followers. This research is significant in understanding the usage of AI-enabled smartwatches as a device doctor or electronic doctor (e-doctor).
Collapse
Affiliation(s)
- Md Uzir Hossain Uzir
- Marketing Department, Lincoln University College, Petaling Jaya, Selangor, Malaysia
- Marketing Department, Faculty of Business, Economics, and Social Development, University Malaysia Terengganu, Kuala Terengganu, Malaysia
| | - Zakari Bukari
- Department of Marketing and Customer Management, University of Professional Studies, Accra, Ghana
| | - Hussam Al Halbusi
- Department of Management at Ahmed Bin Mohammad Military College, Doha, Qatar
| | - Rodney Lim
- Marketing and E-Commerce, Swinburne University of Technology, Sarawak Campus, Hawthorn, 3122, Australia
| | - Siti Norida Wahab
- Faculty of Business and Management, Universiti Teknologi MARA, 42300, Bandar Puncak Alam, Selangor, Malaysia
| | - Tareq Rasul
- Department of Marketing, Australian Institute of Business (AIB), Adelaide, Australia
| | - Ramayah Thurasamy
- School of Management, Universiti Sains Malaysia, Minden, 11800, Penang, Malaysia
- Department of Information Technology & Management, Daffodil International University, Birulia, Bangladesh
- Department of Management, Sunway University Business School, 47500, Petaling Jaya, Selangor, Malaysia
- University Center for Research & Development (UCRD), Chandigarh University, Ludhiana, 140413, Punjab, India
- Fakulti Ekonomi Dan Pengurusan (FEP), Universiti Kebangsaan Malaysia (UKM), Hulu Langat, Malaysia
- Faculty of Economics and Business, Universitas Indonesia (UI), Depok City, West Java, 16424, Indonesia
- Azman Hashim International Business School, Universiti Teknologi Malaysia (UTM), Iskandar Puteri, Malaysia
- Faculty of Business, Economics and Social Development, Universiti Malaysia Terengganu (UMT), 21300, Kuala Terengganu, Malaysia
| | - Ishraq Jerin
- Putra Business School (PBS), Universiti Putra Malaysia (UPM), 43400, Seri Kembangan, Selangor, Malaysia
| | - M Rezaul Karim Chowdhury
- Faculty of Maritime Studies, Universiti Malaysia Terengganu, 21300, Kuala Terengganu, Terengganu, Malaysia
| | - Arun Kumar Tarofder
- Faculty of Business and Professional Studies, Management and Science University Malaysia, 40100, Shah Alam, Selangor, Malaysia
| | - Azizul Yadi Yaakop
- Faculty of Business, Economics and Social Development, Universiti Malaysia Terengganu, 21300, Kuala Terengganu, Terengganu, Malaysia
| | | | - Ahasanul Haque
- Department of Business Administration, International Islamic University Malaysia, Box No. 10, 50728, Kuala Lumpur, Malaysia
| | | | - Bilal Eneizan
- Business School, Jadara University, Irbid, Jordan
- College of Science and Humanities Studies, Prince Sattam Bin Abdulaziz University, Sulayyil, Saudi Arabia
| |
Collapse
|
16
|
Grassini S. Development and validation of the AI attitude scale (AIAS-4): a brief measure of general attitude toward artificial intelligence. Front Psychol 2023; 14:1191628. [PMID: 37554139 PMCID: PMC10406504 DOI: 10.3389/fpsyg.2023.1191628] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2023] [Accepted: 06/16/2023] [Indexed: 08/10/2023] Open
Abstract
The rapid advancement of artificial intelligence (AI) has generated an increasing demand for tools that can assess public attitudes toward AI. This study proposes the development and the validation of the AI Attitude Scale (AIAS), a concise self-report instrument designed to evaluate public perceptions of AI technology. The first version of the AIAS that the present manuscript proposes comprises five items, including one reverse-scored item, which aims to gauge individuals' beliefs about AI's influence on their lives, careers, and humanity overall. The scale is designed to capture attitudes toward AI, focusing on the perceived utility and potential impact of technology on society and humanity. The psychometric properties of the scale were investigated using diverse samples in two separate studies. An exploratory factor analysis was initially conducted on a preliminary 5-item version of the scale. Such exploratory validation study revealed the need to divide the scale into two factors. While the results demonstrated satisfactory internal consistency for the overall scale and its correlation with related psychometric measures, separate analyses for each factor showed robust internal consistency for Factor 1 but insufficient internal consistency for Factor 2. As a result, a second version of the scale is developed and validated, omitting the item that displayed weak correlation with the remaining items in the questionnaire. The refined final 1-factor, 4-item AIAS demonstrated superior overall internal consistency compared to the initial 5-item scale and the proposed factors. Further confirmatory factor analyses, performed on a different sample of participants, confirmed that the 1-factor model (4-items) of the AIAS exhibited an adequate fit to the data, providing additional evidence for the scale's structural validity and generalizability across diverse populations. In conclusion, the analyses reported in this article suggest that the developed and validated 4-items AIAS can be a valuable instrument for researchers and professionals working on AI development who seek to understand and study users' general attitudes toward AI.
Collapse
Affiliation(s)
- Simone Grassini
- Department of Psychosocial Science, University of Bergen, Bergen, Norway
- Cognitive and Behavioral Neuroscience Lab, University of Stavanger, Stavanger, Norway
| |
Collapse
|
17
|
Frehywot S, Vovides Y. An equitable and sustainable community of practice framework to address the use of artificial intelligence for global health workforce training. HUMAN RESOURCES FOR HEALTH 2023; 21:45. [PMID: 37312214 DOI: 10.1186/s12960-023-00833-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/05/2023] [Accepted: 06/07/2023] [Indexed: 06/15/2023]
Abstract
Artificial Intelligence (AI) technologies and data science models may hold potential for enabling an understanding of global health inequities and support decision-making related toward possible interventions. However, AI inputs should not perpetuate the biases and structural issues within our global societies that have created various health inequities. We need AI to be able to 'see' the full context of what it is meant to learn. AI trained with biased data produces biased outputs and providing health workforce training with such outputs further contributes to the buildup of biases and structural inequities. The accelerating and intricately evolving technology and digitalization will influence the education and practice of health care workers. Before we invest in utilizing AI in health workforce training globally, it is important to make sure that multiple stakeholders from the global arena are included in the conversation to address the need for training in 'AI and the role of AI in training'. This is a daunting task for any one entity and a multi-sectorial interactions and solutions are needed. We believe that partnerships among various national, regional, and global stakeholders involved directly or indirectly with health workforce training ranging to name a few, from public health & clinical science training institutions, computer science, learning design, data science, technology companies, social scientists, law, and AI ethicists, need to be developed in ways that enable the formation of an equitable and sustainable Communities of Practice (CoP) to address the use of AI for global health workforce training. This paper has laid out a framework for such CoP.
Collapse
Affiliation(s)
- Seble Frehywot
- Department of Global Health & Health Policy and Co-Founder of IT for Health and Education System Equity, George Washington University Milken Institute of Public Health, Washington, DC, USA.
| | - Yianna Vovides
- Senior Director of Learning Design and Research at the Center for New Designs in Learning and Scholarship (CNDLS), Curriculum Director for the Learning, Design, and Technology (LDT) program, and Co-Founder of IT for Health and Education System Equity, Georgetown University, Washington, DC, USA
| |
Collapse
|
18
|
Jacob Fernandes França T, São Mamede H, Pereira Barroso JM, Pereira Duarte dos Santos VM. Artificial intelligence applied to potential assessment and talent identification in an organisational context. Heliyon 2023; 9:e14694. [PMID: 37035361 PMCID: PMC10073758 DOI: 10.1016/j.heliyon.2023.e14694] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2022] [Revised: 03/14/2023] [Accepted: 03/15/2023] [Indexed: 03/28/2023] Open
Abstract
Our study provides valuable insights into the relationship between artificial intelligence (AI) and Human Resource Management (HRM). We have minimised bias and ensured reliable findings by employing a systematic literature review and the PRISMA statement. Our comprehensive synthesis of the studies included in this research, along with a bibliometric analysis of articles, journals, indexes, authors' affiliations, citations, keyword co-occurrences, and co-authorship analysis, has produced robust results. The discussion of our findings focuses on critical areas of interest, such as AI and Talent, AI Bias, Ethics and Law, and their impact on Human Resource (HR) management. Our research highlights the recognition by organisations of the importance of talent management in achieving a competitive advantage as higher-level skills become increasingly necessary. Although some HR managers have adopted AI technology for talent acquisition, our study reveals that there is still room for improvement. Our study is in line with previous research that acknowledges the potential for AI to revolutionise HR management and the future of work. Our findings emphasise the need for HR managers to be proactive in embracing technology and bridging the technological, human, societal, and governmental gaps. Our study contributes to the growing body of AI and HR management knowledge, providing essential insights and recommendations for future research. The importance of our study lies in its focus on the role of HR in promoting the benefits of AI-based applications, thereby creating a larger body of knowledge from an organisational perspective.
Collapse
|
19
|
Primiero G, Ceolin D, Doneda F. A computational model for assessing experts’ trustworthiness. J EXP THEOR ARTIF IN 2023. [DOI: 10.1080/0952813x.2023.2183272] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/05/2023]
Affiliation(s)
- G. Primiero
- Logic, Uncertainty, Computation and Information Group, Department of Philosophy, University of Milan, Milano,Italy
| | - D. Ceolin
- Centrum Wiskunde & Informatica, Amsterdam, The Netherlands
| | - F. Doneda
- Logic, Uncertainty, Computation and Information Group, and Doctoral School HUME, The Human Mind and its Explanations, Department of Philosophy, University of Milan, Milan, Italy
| |
Collapse
|
20
|
Aysolmaz B, Joshi A, Stubhan M. Examining and Comparing the Critical Success Factors Between Business Process Management and Business Process Automation. JOURNAL OF GLOBAL INFORMATION MANAGEMENT 2023. [DOI: 10.4018/jgim.318476] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/06/2023]
Abstract
Organizations constantly seek for means of improving their business processes to remain competitive. Recently, much effort has been devoted to business process automation (BPA) projects. However, it has been unclear how these projects differ from traditional business process management (BPM) projects and which critical success factors (CSFs) play a crucial role. This study comparatively assesses how CSFs change for BPA with respect to BPM. Using a survey sample of 139 BPM and BPA experts, the study shows that top management support as a CSF warrants attention for BPM than BPA, while the importance of choosing project methodology significantly differs for BPM and BPA. The comparison between lightweight and heavyweight BPA types indicates that the top management support is relatively less critical for lightweight BPA. The explorative and fsQCA analyses provide insights about the patterns for industries with different IT strategic roles, expert perspectives, and among CSFs. Overall, the findings may help project managers assess the role of key CSFs better based on project focus.
Collapse
|
21
|
Investigating the Influence of Artificial Intelligence on Business Value in the Digital Era of Strategy: A Literature Review. INFORMATION 2023. [DOI: 10.3390/info14020085] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/05/2023] Open
Abstract
For organizations, the development of new business models and competitive advantages through the integration of artificial intelligence (AI) in business and IT strategies holds considerable promise. The majority of businesses are finding it difficult to take advantage of the opportunities for value creation while other pioneers are successfully utilizing AI. On the basis of the research methodology of Webster and Watson (2020), 139 peer-reviewed articles were discussed. According to the literature, the performance advantages, success criteria, and difficulties of adopting AI have been emphasized in prior research. The results of this review revealed the open issues and topics that call for further research/examination in order to develop AI capabilities and integrate them into business/IT strategies in order to enhance various business value streams. Organizations will only succeed in the digital transformation alignment of the present era by precisely adopting and implementing these new, cutting-edge technologies. Despite the revolutionary potential advantages that AI capabilities may promote, the resource orchestration, along with governance in this dynamic environment, is still complex enough and in the early stages of research regarding the strategic implementation of AI in organizations, which is the issue this review aims to address and, as a result, assist present and future organizations effectively enhance various business value outcomes.
Collapse
|
22
|
Uren V, Edwards JS. Technology readiness and the organizational journey towards AI adoption: An empirical study. INTERNATIONAL JOURNAL OF INFORMATION MANAGEMENT 2023. [DOI: 10.1016/j.ijinfomgt.2022.102588] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
|
23
|
Vial G, Cameron A, Giannelia T, Jiang J. Managing artificial intelligence projects: Key insights from an AI consulting firm. INFORMATION SYSTEMS JOURNAL 2022. [DOI: 10.1111/isj.12420] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/25/2022]
Affiliation(s)
- Gregory Vial
- Department of Information Technology HEC Montreal Montreal Quebec Canada
| | | | - Tanya Giannelia
- Department of Information Technology HEC Montreal Montreal Quebec Canada
| | - Jinglu Jiang
- School of Management Binghamton University Binghamton New York USA
| |
Collapse
|
24
|
Lopez-Pujalte C, Tena-Mateos MJ, Muñoz-Cañavate A. A Technology Watch/Competitive Intelligence–based Decision-Support System optimised with Genetic Algorithms. J Inf Sci 2022. [DOI: 10.1177/01655515221133531] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/09/2022]
Abstract
To survive and prosper in a highly competitive environment where uncertainty and ambiguity are the norm, today’s firms are faced with the need for new information management methods and tools. Two of the most prominent strategies that take information and its treatment as a value-generating element in firms’ decision-making are Technology Watch and Competitive Intelligence. In addition, one of the fundamental components that a system based on these strategies must have is an efficient method of Information Retrieval. The present study describes a Competitive Intelligence–based decision-support system that uses a Genetic Algorithm. The system contributes to improving information retrieval through search optimisation, thus enhancing the performance of this knowledge-generating tool for organisations.
Collapse
|
25
|
Fosso Wamba S. Impact of artificial intelligence assimilation on firm performance: The mediating effects of organizational agility and customer agility. INTERNATIONAL JOURNAL OF INFORMATION MANAGEMENT 2022. [DOI: 10.1016/j.ijinfomgt.2022.102544] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/16/2022]
|
26
|
Badru LO, Vasudevan V, Lingam GI, Khan MGM. MERN Stack Web-Based Education Management Information Systems for Pacific Island Countries. SN COMPUTER SCIENCE 2022; 4:70. [PMCID: PMC9707281 DOI: 10.1007/s42979-022-01457-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/02/2022] [Accepted: 10/13/2022] [Indexed: 11/30/2022]
Affiliation(s)
- Lawal Olarotimi Badru
- The School of Information Technology, Engineering, Mathematics and Physics, The University of South Pacific, Suva, Fiji
| | - Vani Vasudevan
- Department of Computer Science and Engineering, Nitte Meenakshi Institute of Technology, Bengaluru, Karnataka India
| | - Govinda Ishwar Lingam
- The School of Pacific Arts, Communication and Education, The University of South Pacific, Suva, Fiji
| | - M. G. M. Khan
- The School of Information Technology, Engineering, Mathematics and Physics, The University of South Pacific, Suva, Fiji
| |
Collapse
|
27
|
Chen A, Xiang M, Wang M, Lu Y. Harmony in intelligent hybrid teams: the influence of the intellectual ability of artificial intelligence on human members’ reactions. INFORMATION TECHNOLOGY & PEOPLE 2022. [DOI: 10.1108/itp-01-2022-0059] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
PurposeThe purpose of this paper was to investigate the relationships among the intellectual ability of artificial intelligence (AI), cognitive emotional processes and the positive and negative reactions of human members. The authors also examined the moderating role of AI status in teams.Design/methodology/approachThe authors designed an experiment and recruited 120 subjects who were randomly distributed into one of three groups classified by the upper, middle and lower organization levels of AI in the team. The findings in this study were derived from subjects’ self-reports and their performance in the experiment.FindingsRegardless of the position held by AI, human members believed that its intelligence level is positively correlated with dependence behavior. However, when the AI and human members are at the same level, the higher the intelligence of AI, the more likely it is that its direct interaction with team members will lead to conflicts.Research limitations/implicationsThis paper only focuses on human–AI harmony in transactional work in hybrid teams in enterprises. As AI applications permeate, it should be considered whether the findings can be extended to a broader range of AI usage scenarios.Practical implicationsThese results are helpful for understanding how to improve team performance in light of the fact that team members have introduced AI into their enterprises in large quantities.Originality/valueThis study contributes to the literature on how the intelligence level of AI affects the positive and negative behaviors of human members in hybrid teams. The study also innovatively introduces “status” into hybrid organizations.
Collapse
|
28
|
Kortmann F, Fassmeyer P, Funk B, Drews P. Watch out, pothole! featuring road damage detection in an end-to-end system for autonomous driving. DATA KNOWL ENG 2022. [DOI: 10.1016/j.datak.2022.102091] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
|
29
|
Ganesh PS, Kim SY. A comparison of conventional and advanced electroanalytical methods to detect SARS-CoV-2 virus: A concise review. CHEMOSPHERE 2022; 307:135645. [PMID: 35817176 PMCID: PMC9270057 DOI: 10.1016/j.chemosphere.2022.135645] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 05/12/2022] [Revised: 07/04/2022] [Accepted: 07/05/2022] [Indexed: 06/15/2023]
Abstract
Respiratory viruses are a serious threat to human wellbeing that can cause pandemic disease. As a result, it is critical to identify virus in a timely, sensitive, and precise manner. The present novel coronavirus-2019 (COVID-19) disease outbreak has increased these concerns. The research of developing various methods for COVID-19 virus identification is one of the most rapidly growing research areas. This review article compares and addresses recent improvements in conventional and advanced electroanalytical approaches for detecting COVID-19 virus. The popular conventional methods such as polymerase chain reaction (PCR), loop mediated isothermal amplification (LAMP), serology test, and computed tomography (CT) scan with artificial intelligence require specialized equipment, hours of processing, and specially trained staff. Many researchers, on the other hand, focused on the invention and expansion of electrochemical and/or bio sensors to detect SARS-CoV-2, demonstrating that they could show a significant role in COVID-19 disease control. We attempted to meticulously summarize recent advancements, compare conventional and electroanalytical approaches, and ultimately discuss future prospective in the field. We hope that this review will be helpful to researchers who are interested in this interdisciplinary field and desire to develop more innovative virus detection methods.
Collapse
Affiliation(s)
- Pattan-Siddappa Ganesh
- Interaction Laboratory, Advanced Technology Research Center, Future Convergence Engineering, Korea University of Technology and Education (KoreaTech), Cheonan-si, Chungcheongnam-do, 330-708, Republic of Korea.
| | - Sang-Youn Kim
- Interaction Laboratory, Advanced Technology Research Center, Future Convergence Engineering, Korea University of Technology and Education (KoreaTech), Cheonan-si, Chungcheongnam-do, 330-708, Republic of Korea.
| |
Collapse
|
30
|
The effect of required warmth on consumer acceptance of artificial intelligence in service: The moderating role of AI-human collaboration. INTERNATIONAL JOURNAL OF INFORMATION MANAGEMENT 2022. [DOI: 10.1016/j.ijinfomgt.2022.102533] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
|
31
|
Zamani ED, Smyth C, Gupta S, Dennehy D. Artificial intelligence and big data analytics for supply chain resilience: a systematic literature review. ANNALS OF OPERATIONS RESEARCH 2022; 327:1-28. [PMID: 36212520 PMCID: PMC9524319 DOI: 10.1007/s10479-022-04983-y] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/14/2021] [Revised: 08/31/2022] [Accepted: 09/06/2022] [Indexed: 05/30/2023]
Abstract
Artificial Intelligence (AI) and Big Data Analytics (BDA) have the potential to significantly improve resilience of supply chains and to facilitate more effective management of supply chain resources. Despite such potential benefits and the increase in popularity of AI and BDA in the context of supply chains, research to date is dispersed into research streams that is largely based on the publication outlet. We curate and synthesise this dispersed knowledge by conducting a systematic literature review of AI and BDA research in supply chain resilience that have been published in the Chartered Association of Business School (CABS) ranked journals between 2011 and 2021. The search strategy resulted in 522 studies, of which 23 were identified as primary papers relevant to this research. The findings advance knowledge by (i) assessing the current state of AI and BDA in supply chain literature, (ii) identifying the phases of supply chain resilience (readiness, response, recovery, adaptability) that AI and BDA have been reported to improve, and (iii) synthesising the reported benefits of AI and BDA in the context of supply chain resilience.
Collapse
Affiliation(s)
| | - Conn Smyth
- Business Information Systems, NUI Galway, Galway, Ireland
| | - Samrat Gupta
- Information Systems Area, Indian Institute of Management Ahmedabad, Ahmedabad, Gujarat India
| | - Denis Dennehy
- School of Management, Swansea University, Swansea, UK
| |
Collapse
|
32
|
Artificial Intelligence in Biological Sciences. Life (Basel) 2022; 12:life12091430. [PMID: 36143468 PMCID: PMC9505413 DOI: 10.3390/life12091430] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2022] [Revised: 08/25/2022] [Accepted: 09/10/2022] [Indexed: 12/03/2022] Open
Abstract
Artificial intelligence (AI), currently a cutting-edge concept, has the potential to improve the quality of life of human beings. The fields of AI and biological research are becoming more intertwined, and methods for extracting and applying the information stored in live organisms are constantly being refined. As the field of AI matures with more trained algorithms, the potential of its application in epidemiology, the study of host–pathogen interactions and drug designing widens. AI is now being applied in several fields of drug discovery, customized medicine, gene editing, radiography, image processing and medication management. More precise diagnosis and cost-effective treatment will be possible in the near future due to the application of AI-based technologies. In the field of agriculture, farmers have reduced waste, increased output and decreased the amount of time it takes to bring their goods to market due to the application of advanced AI-based approaches. Moreover, with the use of AI through machine learning (ML) and deep-learning-based smart programs, one can modify the metabolic pathways of living systems to obtain the best possible outputs with the minimal inputs. Such efforts can improve the industrial strains of microbial species to maximize the yield in the bio-based industrial setup. This article summarizes the potentials of AI and their application to several fields of biology, such as medicine, agriculture, and bio-based industry.
Collapse
|
33
|
Organizational readiness to adopt artificial intelligence in the exhibition sector in Western Europe. INTERNATIONAL JOURNAL OF INFORMATION MANAGEMENT 2022. [DOI: 10.1016/j.ijinfomgt.2022.102497] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
|
34
|
Beyond effective use: Integrating wise reasoning in machine learning development. INTERNATIONAL JOURNAL OF INFORMATION MANAGEMENT 2022. [DOI: 10.1016/j.ijinfomgt.2022.102566] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022]
|
35
|
Dairy 4.0: Intelligent Communication Ecosystem for the Cattle Animal Welfare with Blockchain and IoT Enabled Technologies. APPLIED SCIENCES-BASEL 2022. [DOI: 10.3390/app12147316] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/10/2022]
Abstract
An intelligent ecosystem with real-time wireless technology is now playing a key role in meeting the sustainability requirements set by the United Nations. Dairy cattle are a major source of milk production all over the world. To meet the food demand of the growing population with maximum productivity, it is necessary for dairy farmers to adopt real-time monitoring technologies. In this study, we will be exploring and assimilating the limitless possibilities for technological interventions in dairy cattle to drastically improve their ecosystem. Intelligent systems for sensing, monitoring, and methods for analysis to be used in applications such as animal health monitoring, animal location tracking, milk quality, and supply chain, feed monitoring and safety, etc., have been discussed briefly. Furthermore, generalized architecture has been proposed that can be directly applied in the future for breakthroughs in research and development linked to data gathering and the processing of applications through edge devices, robots, drones, and blockchain for building intelligent ecosystems. In addition, the article discusses the possibilities and challenges of implementing previous techniques for different activities in dairy cattle. High computing power-based wearable devices, renewable energy harvesting, drone-based furious animal attack detection, and blockchain with IoT assisted systems for the milk supply chain are the vital recommendations addressed in this study for the effective implementation of the intelligent ecosystem in dairy cattle.
Collapse
|
36
|
Merhi MI. An evaluation of the critical success factors impacting artificial intelligence implementation. INTERNATIONAL JOURNAL OF INFORMATION MANAGEMENT 2022. [DOI: 10.1016/j.ijinfomgt.2022.102545] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/17/2022]
|
37
|
Ashrafi DM, Easmin R. Okay Google, Good to talk to you$\ldots$Examining the determinants affecting users' behavioral intention for adopting voice assistants: Does technology self-efficacy matter? INTERNATIONAL JOURNAL OF INNOVATION AND TECHNOLOGY MANAGEMENT 2022. [DOI: 10.1142/s0219877023500049] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
|
38
|
Herm LV, Heinrich K, Wanner J, Janiesch C. Stop ordering machine learning algorithms by their explainability! A user-centered investigation of performance and explainability. INTERNATIONAL JOURNAL OF INFORMATION MANAGEMENT 2022. [DOI: 10.1016/j.ijinfomgt.2022.102538] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/23/2023]
|
39
|
Human-Computer Interaction in Customer Service: The Experience with AI Chatbots—A Systematic Literature Review. ELECTRONICS 2022. [DOI: 10.3390/electronics11101579] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/04/2023]
Abstract
Artificial intelligence (AI) conversational agents (CA) or chatbots represent one of the technologies that can provide automated customer service for companies, a trend encountered in recent years. Chatbot use is beneficial for companies when associated with positive customer experience. The purpose of this paper is to analyze the overall customer experience with customer service chatbots in order to identify the main influencing factors for customer experience with customer service chatbots and to identify the resulting dimensions of customer experience (such as perceptions/attitudes and feelings and also responses and behaviors). The analysis uses the systematic literature review (SLR) method and includes a sample of 40 publications that present empirical studies. The results illustrate that the main influencing factors of customer experience with chatbots are grouped in three categories: chatbot-related, customer-related, and context-related factors, where the chatbot-related factors are further categorized in: functional features of chatbots, system features of chatbots and anthropomorphic features of chatbots. The multitude of factors of customer experience result in either positive or negative perceptions/attitudes and feelings of customers. At the same time, customers respond by manifesting their intentions and/or their behaviors towards either the technology itself (chatbot usage continuation and acceptance of chatbot recommendations) or towards the company (buying and recommending products). According to empirical studies, the most influential factors when using chatbots for customer service are response relevance and problem resolution, which usually result in positive customer satisfaction, increased probability for chatbots usage continuation, product purchases, and product recommendations.
Collapse
|
40
|
Gupta S, Modgil S, Lee CK, Sivarajah U. The future is yesterday: Use of AI-driven facial recognition to enhance value in the travel and tourism industry. INFORMATION SYSTEMS FRONTIERS : A JOURNAL OF RESEARCH AND INNOVATION 2022; 25:1179-1195. [PMID: 35529102 PMCID: PMC9059456 DOI: 10.1007/s10796-022-10271-8] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Accepted: 03/14/2022] [Indexed: 06/08/2023]
Abstract
This study aims to investigate the role of artificial intelligence (AI) driven facial recognition to enhance a value proposition by influencing different areas of services in the travel and tourism industry. We adopted semi-structured interviews to derive insights from 26 respondents. Thematic analysis reveals the development of four main themes (personalization, data-driven service offering, security and safety, and seamless payments). Further, we mapped the impact of AI- driven facial recognition to enhance value and experience for corporate guests. Findings indicate that AI-based facial recognition can facilitate the travel and tourism industry in understanding travelers' needs, optimization of service offers, and value-based services, whereas data-driven services can be realized in the form of customized trip planning, email, and calendar integration, and quick bill summarization. This contributes to strengthening the tourism literature through the lens of organizational information processing theory.
Collapse
Affiliation(s)
- Shivam Gupta
- Department of Information Systems, Supply Chain Management & Decision Support, NEOMA Business School, 59 Rue Pierre Taittinger, 51100 Reims, France
| | - Sachin Modgil
- Department of Operations Management, International Management Institute (IMI) Kolkata, 2/4 C, Judges Ct Rd, Alipore, Kolkata, West Bengal 700027 India
| | - Choong-Ki Lee
- College of Hotel & Tourism Management, Kyung Hee University, 26 Kyungheedae-ro, Hoegi-dong, Dongdaemun-gu, Seoul, South Korea
| | | |
Collapse
|
41
|
Papagiannidis E, Enholm IM, Dremel C, Mikalef P, Krogstie J. Toward AI Governance: Identifying Best Practices and Potential Barriers and Outcomes. INFORMATION SYSTEMS FRONTIERS : A JOURNAL OF RESEARCH AND INNOVATION 2022; 25:123-141. [PMID: 35464171 PMCID: PMC9018249 DOI: 10.1007/s10796-022-10251-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Accepted: 01/27/2022] [Indexed: 06/14/2023]
Abstract
In recent years artificial intelligence (AI) has been seen as a technology with tremendous potential for enabling companies to gain an operational and competitive advantage. However, despite the use of AI, businesses continue to face challenges and are unable to immediately realize performance gains. Furthermore, firms need to introduce robust AI systems and mitigate AI risks, which emphasizes the importance of creating suitable AI governance practices. This study, explores how AI governance is applied to promote the development of robust AI applications that do not introduce negative effects, based on a comparative case analysis of three firms in the energy sector. The study illustrates which practices are placed to produce knowledge that assists with decision making while at the same time overcoming barriers with recommended actions leading to desired outcomes. The study contributes by exploring the main dimensions relevant to AI's governance in organizations and by uncovering the practices that underpin them.
Collapse
Affiliation(s)
| | | | - Chirstian Dremel
- Norwegian University of Science and Technology, Trondheim, Norway
| | - Patrick Mikalef
- Norwegian University of Science and Technology, Trondheim, Norway
| | - John Krogstie
- Norwegian University of Science and Technology, Trondheim, Norway
| |
Collapse
|
42
|
Assessing behavioral data science privacy issues in government artificial intelligence deployment. GOVERNMENT INFORMATION QUARTERLY 2022. [DOI: 10.1016/j.giq.2022.101679] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/19/2022]
|
43
|
Use of Artificial Intelligence in Smart Cities for Smart Decision-Making: A Social Innovation Perspective. SUSTAINABILITY 2022. [DOI: 10.3390/su14020620] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
Abstract
The goal of this study is to investigate the direct and indirect relationships that exist between artificial intelligence (AI), social innovation (SI), and smart decision-making (SDM). This study used a survey design and collected cross-sectional data from South Korea and Pakistan using survey questionnaires. Four hundred sixty respondents from the public and private sectors were obtained and empirically analyzed using SPSS multiple regression. The study discovered a strong and positive mediating effect of SI between the relationship of AI and SDM, as predicted. Previous researchers have investigated some of the factors that influence the decision-making process. This study adds to the social science literature by examining the impact of a mediating factor on decision-making. The findings of this study will contribute to the local government in building smart cities such that the factor of social innovations should be involved in the decision-making process because smart decision-making would share such collected data with entrepreneurs, businesses, and industries and would benefit society and all relevant stakeholders, including such social innovators.
Collapse
|
44
|
Ravi K, Pushpa NB, Patra A. Will artificial intelligence assume a role in anatomy education? NATIONAL JOURNAL OF CLINICAL ANATOMY 2022. [DOI: 10.4103/njca.njca_85_22] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/04/2022] Open
|
45
|
Karanasios S. The pursuit of relevance and impact: A review of the immediate response of the information systems field to
COVID
‐19. INFORMATION SYSTEMS JOURNAL 2021. [DOI: 10.1111/isj.12372] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Affiliation(s)
- Stan Karanasios
- University of Queensland Business School, Business Information Systems The University of Queensland Brisbane Queensland Australia
| |
Collapse
|
46
|
Dai X, Zhang J, Chang V. Impacts of social networks in an agent-based artificial stock market. ENTERP INF SYST-UK 2021. [DOI: 10.1080/17517575.2021.2008514] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
Affiliation(s)
- Xiaoting Dai
- School of Business, Jiangnan University, Wuxi, China
| | - Jie Zhang
- Department of Accounting, International Business School (Suzhou), Xi’an Jiaotong-Liverpool University, Suzhou, China
| | - Victor Chang
- Cybersecurity, Information Systems and AI Research Group, School of Computing, Engineering and Digital Technologies, Teesside University, Middlesbrough, UK
| |
Collapse
|
47
|
Social influence on endorsement in social Q&A community: Moderating effects of temporal and spatial factors. INTERNATIONAL JOURNAL OF INFORMATION MANAGEMENT 2021. [DOI: 10.1016/j.ijinfomgt.2021.102396] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
|