Artificial intelligence: a modern approach

Optimizing Virtual Machine placement in IaaS data centers: Taxonomy, review and open issues

Talebian, H., Gani, A., Sookhak, M., Abdelatif, A. A., Yousafzai, A., Vasilakos, A. V., & Yu, F. R. (2020) · Cluster Computing · doi:10.1007/s10586-019-02954-w · Cited by 72

The Role of HCI in the Age of AI

Harper, R. H. R. (2019) · International Journal of Human–Computer Interaction · doi:10.1080/10447318.2019.1631527 · Cited by 68

Racism, responsibility and autonomy in HCI: Testing perceptions of an AI agent

Hong, J.-W., & Williams, D. (2019) · Computers in Human Behavior · doi:10.1016/j.chb.2019.06.012 · Cited by 69

Artificial intelligence and robot nurses: from nurse managers' perspective: a descriptive cross‐sectional study

Ergin, E., Karaarslan, D., Şahan, S., & Çınar Yücel, Ş. (2022) · Journal of Nursing Management · doi:10.1111/jonm.13646 · Cited by 70

Outcomes and complications after endovascular treatment of Brain Arteriovenous Malformations: a Prognostication Attempt using artificial Intelligence

Asadi, H., Kok, H. K., Looby, S., Brennan, P., O'Hare, A., & Thornton, J. (2016) · World Neurosurgery · doi:10.1016/j.wneu.2016.09.086 · Cited by 67

Learning from the Failure of Autonomous and Intelligent Systems: Accidents, Safety, and Sociotechnical Sources of Risk

Macrae, C. (2022) · Risk Analysis · doi:10.1111/risa.13850 · Cited by 76

AI, Explainability and Public Reason: The Argument from the Limitations of the Human Mind

Maclure, J. (2021) · Minds and Machines · doi:10.1007/s11023-021-09570-x · Cited by 67

Semantic Web meets Integrative Biology: a survey

Chen, H., Yu, T., & Chen, J. Y. (2013) · Briefings in Bioinformatics · doi:10.1093/bib/bbs014 · Cited by 65

Exploring English as a foreign language learners’ adoption and utilisation of ChatGPT for speaking practice through an extended technology acceptance model

Zou, B., Wang, C., Yan, Y., Du, X., & Ji, Y. (2025) · International Journal of Applied Linguistics · doi:10.1111/ijal.12658 · Cited by 66

The Typing Cure: Experiences with Large Language Model Chatbots for Mental Health Support

Song, I., Pendse, S. R., Kumar, N., & De Choudhury, M. (2024) · arXiv · doi:10.48550/arxiv.2401.14362 · Cited by 64

Predictive coding and thought

Williams, D. (2020) · Synthese · doi:10.1007/s11229-018-1768-x · Cited by 63

A review of medical artificial intelligence

Liu, R., Rong, Y., & Peng, Z. (2020) · Global Health Journal · doi:10.1016/j.glohj.2020.04.002 · Cited by 63

How machine learning activates data network effects in business models: theory advancement through an industrial case of promoting ecological sustainability

Haftor, D. M., Costa Climent, R., & Lundström, J. E. (2021) · Journal of Business Research · doi:10.1016/j.jbusres.2021.04.015 · Cited by 60

Is the Automation of Digital Mental Health Ethical? Applying an Ethical Framework to Chatbots for Cognitive Behaviour Therapy

Vilaza, G. N., & McCashin, D. (2021) · Frontiers in Digital Health · doi:10.3389/fdgth.2021.689736 · Cited by 60

A pedagogy of data and Artificial intelligence for student subjectification.

Loftus, M., & Madden, M. G. (2020) · Teaching in Higher Education · doi:10.1080/13562517.2020.1748593 · Cited by 59

LEGO-Net: Learning Regular Rearrangements of Objects in Rooms

Wei, Q. A., Ding, S., Park, J. J., Sajnani, R., Poulenard, A., Sridhar, S., & Guibas, L. (2023) · 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52729.2023.01825 · Cited by 60

Harnessing the Potential of Artificial Intelligence: Affordances, Constraints, and Strategic Implications for Professional Services

Yang, J., Amrollahi, A., & Marrone, M. (2024) · The Journal of Strategic Information Systems · doi:10.1016/j.jsis.2024.101864 · Cited by 59

Building truths in AI: Making predictive algorithms doable in healthcare

Henriksen, A., & Bechmann, A. (2020) · Information, Communication & Society · doi:10.1080/1369118x.2020.1751866 · Cited by 58

Machine learning for glass science and engineering: A review

Liu, H., Fu, Z., Yang, K., Xu, X., & Bauchy, M. (2019) · Journal of Non-Crystalline Solids: X · doi:10.1016/j.nocx.2019.100036 · Cited by 53

Voice Analysis with Machine Learning: One Step Closer to an Objective Diagnosis of Essential Tremor

Suppa, A., Asci, F., Saggio, G., Di Leo, P., Zarezadeh, Z., Ferrazzano, G., Ruoppolo, G., Berardelli, A., & Costantini, G. (2021) · Movement Disorders · doi:10.1002/mds.28508 · Cited by 54

The Promises and Pitfalls of LLMs as Feedback Providers: A Study of Prompt Engineering and the Quality of AI-Driven Feedback

Jacobsen, L. J., & Weber, K. E. (2023) · doi:10.31219/osf.io/cr257 · Cited by 57

Ontology driven interactive healthcare with wearable sensors

Kim, J., Kim, J., Lee, D., & Chung, K.-Y. (2014) · Multimedia Tools and Applications · doi:10.1007/s11042-012-1195-9 · Cited by 57

People cannot distinguish GPT-4 from a human in a Turing test

Jones, C. R., Rathi, I., Taylor, S., & Bergen, B. K. (2025) · Proceedings of the 2025 ACM Conference on Fairness, Accountability, and Transparency · doi:10.1145/3715275.3732108 · Cited by 76

The Impact of Artificial Intelligence on Human Sexuality: A Five-Year Literature Review 2020–2024

Döring, N., Le, T. D., Vowels, L. M., Vowels, M. J., & Marcantonio, T. L. (2024) · Current Sexual Health Reports · doi:10.1007/s11930-024-00397-y · Cited by 65

Challenges and opportunities in the application of artificial intelligence in gastroenterology and hepatology

Christou, C. D., & Tsoulfas, G. (2021) · World Journal of Gastroenterology · doi:10.3748/wjg.v27.i37.6191 · Cited by 54

How can we trust a robot?

Kuipers, B. (2018) · Communications of the ACM · doi:10.1145/3173087 · Cited by 54

Artificial intelligence and its potential in oncology

Londhe, V. Y., & Bhasin, B. (2019) · Drug Discovery Today · doi:10.1016/j.drudis.2018.10.005 · Cited by 53

Toward the Automation of Diagnostic Conversation Analysis in Patients with Memory Complaints

Mirheidari, B., Blackburn, D., Harkness, K., Walker, T., Venneri, A., Reuber, M., & Christensen, H. (2017) · Journal of Alzheimer’s Disease · doi:10.3233/jad-160507 · Cited by 54

Modeling and Measuring Individuals' Mental Representations of Complex Spatio-Temporal Decision Problems

Arentze, T. A., Dellaert, B. G. C., & Timmermans, H. J. P. (2008) · Environment and Behavior · doi:10.1177/0013916507309994 · Cited by 54

Municipal surveillance regulation and algorithmic accountability

Young, M., Katell, M., & Krafft, P. M. (2019) · Big Data & Society · doi:10.1177/2053951719868492 · Cited by 51

Artificial Intelligence and Groups: Effects of Attitudes and Discretion on Collaboration

Bezrukova, K., Griffith, T. L., Spell, C., Rice, V., & Yang, H. E. (2023) · Group & Organization Management · doi:10.1177/10596011231160574 · Cited by 51

Using AI-empowered assessments and personalized recommendations to promote online collaborative learning performance

Zheng, L., Fan, Y., Gao, L., Huang, Z., Chen, B., & Long, M. (2025) · Journal of Research on Technology in Education · doi:10.1080/15391523.2024.2304066 · Cited by 50

Deep Big Multilayer Perceptrons for Digit Recognition

Cireşan, D. C., Meier, U., Gambardella, L. M., & Schmidhuber, J. (2012) · Lecture Notes in Computer Science · doi:10.1007/978-3-642-35289-8_31 · Cited by 48

Hierarchical, model-based risk management of critical infrastructures

Baiardi, F., Telmon, C., & Sgandurra, D. (2009) · Reliability Engineering & System Safety · doi:10.1016/j.ress.2009.02.001 · Cited by 43

Computational organization of didactic contents for personalized virtual learning environments

Ramos de Melo, F., Flôres, E. L., Diniz de Carvalho, S., Gonçalves de Teixeira, R. A., Batista Loja, L. F., & de Sousa Gomide, R. (2014) · Computers & Education · doi:10.1016/j.compedu.2014.07.012 · Cited by 44

ChatGPT use in universities in South Africa through a socio-technical lens

Tarisayi, K. S. (2024) · Cogent Education · doi:10.1080/2331186x.2023.2295654 · Cited by 44

Assistance and Feedback Mechanism in an Intelligent Tutoring System for Teaching Conversion of Natural Language into Logic

Perikos, I., Grivokostopoulou, F., & Hatzilygeroudis, I. (2017) · International Journal of Artificial Intelligence in Education · doi:10.1007/s40593-017-0139-y · Cited by 44

Fully Autonomous Programming with Large Language Models

Liventsev, V., Grishina, A., Härmä, A., & Moonen, L. (2023) · Proceedings of the Genetic and Evolutionary Computation Conference · doi:10.1145/3583131.3590481 · Cited by 45

An international and cross-cultural perspective on “the wired consumer”: the digital divide and device difference dilemmas

Papadopoulos, N., & Cleveland, M. (2023) · Journal of Business Research · doi:10.1016/j.jbusres.2022.113473 · Cited by 49

A thousand brains: toward biologically constrained AI

Hole, K. J., & Ahmad, S. (2021) · SN Applied Sciences · doi:10.1007/s42452-021-04715-0 · Cited by 42

The Global Use of Artificial Intelligence in the Undergraduate Medical Curriculum: A Systematic Review

Varma, J. R., Fernando, S., Ting, B. Y., Aamir, S., & Sivaprakasam, R. (2023) · Cureus · doi:10.7759/cureus.39701 · Cited by 41

Exploring Time-Dependent Concerns about Pregnancy and Childbirth from Search Logs

Fourney, A., White, R. W., & Horvitz, E. (2015) · Proceedings of the 33rd Annual ACM Conference on Human Factors in Computing Systems · doi:10.1145/2702123.2702427 · Cited by 41

Rethinking How We Theorize AI in Organization and Management: A Problematizing Review of Rationality and Anthropomorphism

Ramaul, L., Ritala, P., Kostis, A., & Aaltonen, P. (2026) · Journal of Management Studies · doi:10.1111/joms.13246 · Cited by 41

Combining Induction, Deduction, and Structure for Verification and Synthesis

Seshia, S. A. (2015) · Proceedings of the IEEE · doi:10.1109/jproc.2015.2471838 · Cited by 38

The evolution of targeted killing practices: Autonomous weapons, future conflict, and the international order

Haas, M. C., & Fischer, S.-C. (2017) · Contemporary Security Policy · doi:10.1080/13523260.2017.1336407 · Cited by 37

Clinical insights: A comprehensive review of language models in medicine

Neveditsin, N., Lingras, P., & Mago, V. (2025) · PLOS Digital Health · doi:10.1371/journal.pdig.0000800 · Cited by 37

What rules? Framing the governance of artificial agency

Gahnberg, C. (2021) · Policy and Society · doi:10.1080/14494035.2021.1929729 · Cited by 35

Explainable Artificial Intelligence in Data Science: From Foundational Issues Towards Socio-technical Considerations

Borrego-Díaz, J., & Galán-Páez, J. (2022) · Minds and Machines · doi:10.1007/s11023-022-09603-z · Cited by 34

The assessment: artificial intelligence and financial services

Bholat, D., & Susskind, D. (2021) · Oxford Review of Economic Policy · doi:10.1093/oxrep/grab015 · Cited by 31

The role of automated technology in the creation of copyright works: The challenges of artificial intelligence

Zatarain, J. M. N. (2017) · International Review of Law, Computers & Technology · doi:10.1080/13600869.2017.1275273 · Cited by 30

Algorithmic Bias and Risk Assessments: Lessons from Practice

Hasan, A., Brown, S., Davidovic, J., Lange, B., & Regan, M. (2022) · Digital Society · doi:10.1007/s44206-022-00017-z · Cited by 29

Law and software agents: Are they “Agents” by the way?

Dahiyat, E. A. R. (2021) · Artificial Intelligence and Law · doi:10.1007/s10506-020-09265-1 · Cited by 31

Cognitive Network Inference through Bayesian Network Analysis

Quer, G., Meenakshisundaram, H., Tamma, B., Manoj, B. S., Rao, R., & Zorzi, M. (2010) · 2010 IEEE Global Telecommunications Conference GLOBECOM 2010 · doi:10.1109/glocom.2010.5683282 · Cited by 29

Incorporating Artificial Intelligence into Healthcare Workflows: Models and Insights

Dai, T., & Abràmoff, M. D. (2023) · Tutorials in Operations Research: Advancing the Frontiers of OR/MS: From Methodologies to Applications · doi:10.1287/educ.2023.0257 · Cited by 29

Computational psychopharmacology: a translational and pragmatic approach

Robbins, T. W., & Cardinal, R. N. (2019) · Psychopharmacology · doi:10.1007/s00213-019-05302-3 · Cited by 25

Artificial intelligence and machine learning: Definition of terms and current concepts in critical care research

Sun, K., Roy, A., & Tobin, J. M. (2024) · Journal of Critical Care · doi:10.1016/j.jcrc.2024.154792 · Cited by 28

Learning Relational Representations with Auto-encoding Logic Programs

Dumancic, S., Guns, T., Meert, W., & Blockeel, H. (2019) · Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2019/842 · Cited by 27

Bio-inspired smart electronic-skin based on inorganic perovskite nanoplates for application in photomemories and mechanoreceptors

Xu, Z., Wu, C., Zhu, Y., Ju, S., Ma, F., Guo, T., Li, F., & Kim, T. W. (2021) · Nanoscale · doi:10.1039/d0nr06550a · Cited by 26

In support of “no-fault” civil liability rules for artificial intelligence

Marchisio, E. (2021) · SN Social Sciences · doi:10.1007/s43545-020-00043-z · Cited by 25

AI innovation for advancing public service: The case of China's first Administrative Approval Bureau

Chen, T., Ran, L., & Gao, X. (2019) · Proceedings of the 20th Annual International Conference on Digital Government Research · doi:10.1145/3325112.3325243 · Cited by 25

Benefits of using artificial intelligence in medical predictions

Albu, A., & Stanciu, L. (2015) · 2015 E-Health and Bioengineering Conference (EHB) · doi:10.1109/ehb.2015.7391610 · Cited by 25

Sequential mediation of parasocial relationships for purchase intention: PLS-SEM and machine learning approach

Sharkasi, N., & Rezakhah, S. (2023) · arXiv · doi:10.48550/arxiv.2307.00005 · Cited by 25

AI-assisted assessment and treatment of aphasia: a review

Zhong, X. (2024) · Frontiers in Public Health · doi:10.3389/fpubh.2024.1401240 · Cited by 25

Fusion-based machine learning approach for classification of algae varieties exposed to different light sources in the growth stage

Gerdan Koc, D., Koc, C., & Ekinci, K. (2023) · Algal Research · doi:10.1016/j.algal.2023.103087 · Cited by 23

Artificial Intelligence Detection and Segmentation Models: A Systematic Review and Meta-Analysis of Brain Tumors in Magnetic Resonance Imaging

Wang, T.-W., Shiao, Y.-C., Hong, J.-S., Lee, W.-K., Hsu, M.-S., Cheng, H.-M., Yang, H.-C., Lee, C.-C., Pan, H.-C., You, W. C., et al. (2024) · Mayo Clinic Proceedings: Digital Health · doi:10.1016/j.mcpdig.2024.01.002 · Cited by 23

Trust Development in Military and Civilian Human–Agent Teams: The Effect of Social-Cognitive Recovery Strategies

Kox, E. S., Siegling, L. B., & Kerstholt, J. H. (2022) · International Journal of Social Robotics · doi:10.1007/s12369-022-00871-4 · Cited by 22

Redefining the role of supervisors in the era of artificial intelligence: implications for hybrid postgraduate research governance

Omodan, B. I. (2025) · Cogent Education · doi:10.1080/2331186x.2025.2536534 · Cited by 22

Strategic cognitive sequencing: a computational cognitive neuroscience approach

Herd, S. A., Krueger, K. A., Kriete, T. E., Huang, T.-R., Hazy, T. E., & O'Reilly, R. C. (2013) · Computational Intelligence and Neuroscience · doi:10.1155/2013/149329 · Cited by 20

Estimating and Penalizing Preference Shift in Recommender Systems

Carroll, M., Hadfield-Menell, D., Russell, S., & Dragan, A. (2021) · Fifteenth ACM Conference on Recommender Systems · doi:10.1145/3460231.3478849 · Cited by 20

AI-based competition of autonomous vehicle fleets with application to fleet modularity

Li, X., & Epureanu, B. I. (2020) · European Journal of Operational Research · doi:10.1016/j.ejor.2020.05.020 · Cited by 20

Patentability of AI-Generated Inventions: Is a Reform of the Patent System Needed?

Ramalho, A. (2018) · SSRN Electronic Journal · doi:10.2139/ssrn.3168703 · Cited by 20

Binary Classifier Calibration Using a Bayesian Non-Parametric Approach

Naeini, M. P., Cooper, G. F., & Hauskrecht, M. (2015) · Proceedings of the 2015 SIAM International Conference on Data Mining · doi:10.1137/1.9781611974010.24 · Cited by 19

Mental time-travel, semantic flexibility, and A.I. ethics

Arvan, M. (2023) · AI & SOCIETY · doi:10.1007/s00146-018-0848-2 · Cited by 18

The effects of artificial intelligence and victims’ deservingness information on citizens’ blame attribution towards administrative errors

Tao, L., Wan, J., & Wen, B. (2025) · Public Management Review · doi:10.1080/14719037.2024.2411632 · Cited by 18

A critical deconstruction of computer-based test application in Turkish State University

Ulum, Ö. G. (2020) · Education and Information Technologies · doi:10.1007/s10639-020-10199-z · Cited by 17

Perception of AI creativity: dimensional exploration and scale development

Yang, Y., & Xu, H. (2025) · The Journal of Creative Behavior · doi:10.1002/jocb.70028 · Cited by 15

Intent-based networking for the enterprise: a modern network architecture

Falkner, M., & Apostolopoulos, J. (2022) · Communications of the ACM · doi:10.1145/3538513 · Cited by 14

On the effects of cognitive mobility prediction in wireless multi-hop ad hoc networks

Mezzavilla, M., Quer, G., & Zorzi, M. (2014) · 2014 IEEE International Conference on Communications (ICC) · doi:10.1109/icc.2014.6883557 · Cited by 12

AI management an exploratory survey of the influence of GDPR and FAT principles

Addis, C., & Kutar, M. (2019) · 2019 IEEE SmartWorld, Ubiquitous Intelligence & Computing, Advanced & Trusted Computing, Scalable Computing & Communications, Cloud & Big Data Computing, Internet of People and Smart City Innovation (SmartWorld/SCALCOM/UIC/ATC/CBDCom/IOP/SCI) · doi:10.1109/smartworld-uic-atc-scalcom-iop-sci.2019.00102 · Cited by 11

Discovering study-specific gene regulatory networks

Bo, V., Curtis, T., Lysenko, A., Saqi, M., Swift, S., & Tucker, A. (2014) · PLoS ONE · doi:10.1371/journal.pone.0106524 · Cited by 10

Cognitive Call Admission Control for VoIP over IEEE 802.11 Using Bayesian Networks

Quer, G., Baldo, N., & Zorzi, M. (2011) · 2011 IEEE Global Telecommunications Conference - GLOBECOM 2011 · doi:10.1109/glocom.2011.6133955 · Cited by 10

From Galatea 2.2 to Watson – And Back?

Hildebrandt, M. (2013) · Ius Gentium: Comparative Perspectives on Law and Justice · doi:10.1007/978-94-007-6314-2_2 · Cited by 10

A Driving License for Intelligent Systems

Kandlhofer, M., & Steinbauer, G. (2018) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v32i1.11399 · Cited by 10

Exploring the implications of the metaverse: opportunities and challenges for dance movement therapy

Jane Patel, N. (2024) · Body, Movement and Dance in Psychotherapy · doi:10.1080/17432979.2024.2306581 · Cited by 9

How Do Algorithmic Decision‐Making Systems Used in Public Benefits Determinations Fail? Insights From Legal Challenges

Gules‐Guctas, E. (2026) · Public Administration Review · doi:10.1111/puar.70043 · Cited by 7

Enhancing memory retrieval in generative agents through LLM-trained cross attention networks

Hong, C., & He, Q. (2025) · Frontiers in Psychology · doi:10.3389/fpsyg.2025.1591618 · Cited by 7

Evaluating generative artificial intelligence adoption in management education using AI–human augmentation and expectation confirmation theory

Kumar, A., Baijal, A., & Kharat, M. (2025) · Journal of International Education in Business · doi:10.1108/jieb-07-2024-0086 · Cited by 6

Artificial Intelligence for Dynamic Spectrum Management

Liang, Y.-C. (2020) · Signals and Communication Technology · doi:10.1007/978-981-15-0776-2_6 · Cited by 5

ProSLM: A Prolog Synergized Language Model for explainable Domain Specific Knowledge Based Question Answering

Vakharia, P., Kufeldt, A., Meyers, M., Lane, I., & Gilpin, L. H. (2024) · Lecture Notes in Computer Science · doi:10.1007/978-3-031-71170-1_23 · Cited by 5

Global competition and convergence of AI Law

Hildebrandt, M. (2022) · doi:10.31235/osf.io/j36ke · Cited by 3

A Comprehensive Survey on Outlying Aspect Mining Methods

Samariya, D., Ma, J., & Aryal, S. (2020) · arXiv · doi:10.48550/arxiv.2005.02637 · Cited by 3

Untitled work

(2020) · doi:10.1017/9781108339216 · Cited by 2

The Benefits and Challenges of Digitally-Enabled Cardiology

Castiello, T. (2025) · British Journal of Hospital Medicine · doi:10.12968/hmed.2024.0899 · Cited by 2