Scientific literature: Information overload

Why we publish where we do: Faculty publishing values and their relationship to review, promotion and tenure expectations

Niles, M. T., Schimanski, L. A., McKiernan, E. C., & Alperín, J. P. (2020) · PLoS ONE · doi:10.1371/journal.pone.0228914 · Cited by 244

Development of a liver disease–specific large language model chat interface using retrieval-augmented generation

Ge, J., Sun, S., Owens, J., Galvez, V., Gologorskaya, O., Lai, J. C., Pletcher, M. J., & Lai, K. (2024) · Hepatology · doi:10.1097/hep.0000000000000834 · Cited by 199

Fundamental research questions in subterranean biology

Mammola, S., Amorim, I. R., Bichuette, M. E., Borges, P. A. V., Cheeptham, N., Cooper, S. J., Culver, D. C., Deharveng, L., Eme, D., Ferreira, R. L., et al. (2020) · Biological reviews/Biological reviews of the Cambridge Philosophical Society · doi:10.1111/brv.12642 · Cited by 145

Toward autonomous laboratories: Convergence of artificial intelligence and experimental automation

Xie, Y., Sattari, K., Zhang, C., & Lin, J. (2022) · Progress in Materials Science · doi:10.1016/j.pmatsci.2022.101043 · Cited by 129

The Peer Review Process: Past, Present, and Future

Drozdz, J. A., & Ladomery, M. (2024) · British Journal of Biomedical Science · doi:10.3389/bjbs.2024.12054 · Cited by 115

Towards evidence‐based conservation of subterranean ecosystems

Mammola, S., Meierhofer, M. B., Borges, P. A. V., Colado, R., Culver, D. C., Deharveng, L., Delić, T., Lorenzo, T. D., Dražina, T., Ferreira, R. L., et al. (2022) · Biological reviews/Biological reviews of the Cambridge Philosophical Society · doi:10.1111/brv.12851 · Cited by 104

From Google Gemini to OpenAI Q* (Q-Star): A Survey on Reshaping the Generative Artificial Intelligence (AI) Research Landscape

McIntosh, T. R., Susnjak, T., Liu, T., Watters, P., Xu, D., Liu, D., & Halgamuge, M. N. (2025) · Technologies · doi:10.3390/technologies13020051 · Cited by 102

Using Social Media for Rapid Information Dissemination in a Pandemic: #PedsICU and Coronavirus Disease 2019

Kudchadkar, S. R., & Carroll, C. L. (2020) · Pediatric Critical Care Medicine · doi:10.1097/pcc.0000000000002474 · Cited by 81

A protocol for reproducible functional diversity analyses

Palacio, F. X., Callaghan, C. T., Cardoso, P., Hudgins, E. J., Jarzyna, M. A., Ottaviani, G., Riva, F., Graco‐Roza, C., Shirey, V. M., & Mammola, S. (2022) · Ecography · doi:10.1111/ecog.06287 · Cited by 79

A Survey on Event Extraction for Natural Language Understanding: Riding the Biomedical Literature Wave

Frisoni, G., Moro, G., & Carbonaro, A. (2021) · IEEE Access · doi:10.1109/access.2021.3130956 · Cited by 76

Scientific research in news media: a case study of misrepresentation, sensationalism and harmful recommendations

Dempster, G., Sutherland, G., & Keogh, L. (2022) · Journal of Science Communication · doi:10.22323/2.21010206 · Cited by 71

Challenges and Advances in Information Extraction from Scientific Literature: a Review

Hong, Z., Ward, L. T., Chard, K., Blaiszik, B. J., & Foster, I. T. (2021) · JOM · doi:10.1007/s11837-021-04902-9 · Cited by 78

Performance of 4 Pre-Trained Sentence Transformer Models in the Semantic Query of a Systematic Review Dataset on Peri-Implantitis

Galli, C., Donos, N., & Calciolari, E. (2024) · Information · doi:10.3390/info15020068 · Cited by 87

Impact of the reference list features on the number of citations

Mammola, S., Fontaneto, D., Martínez, A., & Chichorro, F. (2020) · Scientometrics · doi:10.1007/s11192-020-03759-0 · Cited by 67

BioMedBERT: A Pre-trained Biomedical Language Model for QA and IR

Chakraborty, S., Bisong, E., Bhatt, S., Wagner, T., Elliott, R., & Mosconi, F. (2020) · International Conference on Computational Linguistics · doi:10.18653/v1/2020.coling-main.59 · Cited by 61

Evaluating literature reviews conducted by humans versus ChatGPT: comparative study

Mostafapour, M., Fortier, J. H., Pacheco, K., Murray, H., & Garber, G. (2024) · JMIR AI · doi:10.2196/56537 · Cited by 55

Identifying Future-Proof Science

Vickers, P. (2022) · doi:10.1093/oso/9780192862730.001.0001 · Cited by 57

Large language models overcome the challenges of unstructured text data in ecology

Castro, A., Pinto, J., Reino, L., Pipek, P., & Capinha, C. (2024) · Ecological Informatics · doi:10.1016/j.ecoinf.2024.102742 · Cited by 52

A bibliometric analysis of past and emergent trends in animal welfare science

Freire, R., & Nicol, C. J. (2019) · Animal Welfare · doi:10.7120/09627286.28.4.465 · Cited by 57

Threddy: An Interactive System for Personalized Thread-based Exploration and Organization of Scientific Literature

Kang, H. B., Chang, J. C., Kim, Y., & Kittur, A. (2022) · ACM Symposium on User Interface Software and Technology (UIST) · doi:10.1145/3526113.3545660 · Cited by 48

SciSight: Combining faceted navigation and research group detection for COVID-19 exploratory scientific search

Hope, T., Portenoy, J., Vasan, K., Borchardt, J., Horvitz, E., Weld, D. S., Hearst, M. A., & West, J. D. (2020) · Conference on Empirical Methods in Natural Language Processing: System Demonstrations (EMNLP) · doi:10.18653/v1/2020.emnlp-demos.18 · Cited by 53

Irreproducibility in searches of scientific literature: A comparative analysis

Pozsgai, G., Löveï, G. L., Vasseur, L., Gurr, G. M., Batáry, P., Korponai, J. L., Littlewood, N. A., Liu, J., Móra, A., Obrycki, J. J., et al. (2021) · Ecology and Evolution · doi:10.1002/ece3.8154 · Cited by 45

100 articles every ecologist should read

Courchamp, F., & Bradshaw, C. J. A. (2017) · Nature Ecology & Evolution · doi:10.1038/s41559-017-0370-9 · Cited by 41

neuroGPT-X: toward a clinic-ready large language model

Guo, E., Gupta, M., Sinha, S., Rössler, K., Tatagiba, M. S., Akagami, R., Al‐Mefty, O., Sugiyama, T., Stieg, P. E., Pickett, G. E., et al. (2023) · Journal of neurosurgery · doi:10.3171/2023.7.jns23573 · Cited by 50

Generative AI & machine learning in surgical education

Hla, D. A., & Hindin, D. I. (2024) · Current Problems in Surgery · doi:10.1016/j.cpsurg.2024.101701 · Cited by 37

Protect our environment from information overload

Hołyst, J. A., Mayr, P., Thelwall, M. A., Frommholz, I., Havlin, S., Sela, A., Kenett, Y. N., Helić, D., Rehar, A., Maček, S. R., et al. (2024) · Nature Human Behaviour · doi:10.1038/s41562-024-01833-8 · Cited by 35

Peer-reviewing in Surgical Journals: Revolutionize or Perish?

Chloros, G. D., Giannoudis, V. P., & Giannoudis, P. V. (2021) · Annals of Surgery · doi:10.1097/sla.0000000000004756 · Cited by 34

Keeping Up With the Medical Literature: Why, How, and When?

Kamtchum-Tatuene, J., & Zafack, J. G. (2021) · Stroke · doi:10.1161/strokeaha.121.036141 · Cited by 32

Evidence appraisal: a scoping review, conceptual framework, and research agenda

Goldstein, A., Venker, E., & Weng, C. (2017) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocx050 · Cited by 30

SemEval-2021 Task 11: NLPContributionGraph - Structuring Scholarly NLP Contributions for a Research Knowledge Graph

D’Souza, J., Auer, S., & Ted Pedersen, A. (2021) · International Workshop on Semantic Evaluation (SemEval) · doi:10.18653/v1/2021.semeval-1.44 · Cited by 35

How Do Your Biomedical Named Entity Recognition Models Generalize to Novel Entities?

Kim, H., & Kang, J. (2022) · IEEE Access · doi:10.1109/access.2022.3157854 · Cited by 32

BioReader: a Retrieval-Enhanced Text-to-Text Transformer for Biomedical Literature

Frisoni, G., Mizutani, M., Moro, G., & Valgimigli, L. (2022) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2022.emnlp-main.390 · Cited by 29

Dimensions, Measures, and Contexts in Psychological Investigations of Curiosity: A Scoping Review

Yow, Y. J., Ramsay, J. E., Lin, P. K. F., & Marsh, N. V. (2022) · Behavioral Sciences · doi:10.3390/bs12120493 · Cited by 30

Establishing Institutional Scores With the Rigor and Transparency Index: Large-scale Analysis of Scientific Reporting Quality

Menke, J., Eckmann, P., Ozyurt, I. B., Roelandse, M., Anderson, N., Grethe, J., Gamst, A., & Bandrowski, A. (2022) · Journal of Medical Internet Research · doi:10.2196/37324 · Cited by 25

为什么信息超载损害决策?基于有限认知资源的解释

Che, J., Sun, H., Xiao, C., & Aimei, L. I. (2019) · Advances in Psychological Science · doi:10.3724/sp.j.1042.2019.01758 · Cited by 24

Fast, scalable, and automated identification of articles for biodiversity and macroecological datasets

Cornford, R., Deinet, S., Palma, A. D., Hill, S. L. L., McRae, L., Pettit, B., Marconi, V., Purvis, A., & Freeman, R. (2020) · Global Ecology and Biogeography · doi:10.1111/geb.13219 · Cited by 25

A bibliometric review of the water security concept in Central Asia

Xenarios, S., Assubayeva, A., Xie, L., Sehring, J., Amirkhanov, D., Sultanov, A., & Fazli, S. (2020) · Environmental Research Letters · doi:10.1088/1748-9326/abc717 · Cited by 24

SciSight: Combining faceted navigation and research group detection for COVID-19 exploratory scientific searchPreprint

Hope, T., Portenoy, J., Vasan, K., Borchardt, J., Horvitz, E., Weld, D. S., Hearst, M. A., & West, J. D. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.05.23.112284 · Cited by 24

scite: a smart citation index that displays the context of citations and classifies their intent using deep learning

Nicholson, J. M., Mordaunt, M., Lopez, P., Uppala, A., Rosati, D., Rodrigues, N. P., Grabitz, P., & Rife, S. C. (2021) · bioRxiv (published in Quantitative Science Studies) · doi:10.1101/2021.03.15.435418 · Cited by 202

Verbal Communication in Robotics: A Study on Salient Terms, Research Fields and Trends in the Last Decades Based on a Computational Linguistic Analysis

Vargas, A. M., Cominelli, L., Dell’Orletta⋄, F., & Scilingo, E. P. (2021) · Frontiers in Computer Science · doi:10.3389/fcomp.2020.591164 · Cited by 25

Proceedings of the Ninth International Workshop on Health Text Mining and Information Analysis

(2018) · doi:10.18653/v1/w18-56 · Cited by 23

Collective intelligence for AI-assisted chemical synthesis

Li, H., Sarkar, S., Lu, W., Loftus, P. O., Qiu, T., Shee, Y., Cuomo, A. E., Webster, J.-P., Kelly, H. R., Manee, V., et al. (2026) · Nature · doi:10.1038/s41586-026-10131-4 · Cited by 28

Modeling New Trends in Bone Regeneration, using the BERTopic Approach

Guizzardi, S., Colangelo, M. T., Mirandola, P., & Galli, C. (2023) · Regenerative Medicine · doi:10.2217/rme-2023-0096 · Cited by 23

Use of Twitter Amplifiers by Medical Professionals to Combat Misinformation During the COVID-19 Pandemic

Royan, R., Pendergrast, T. R., Rios, M. D., Rotolo, S. M., Trueger, N. S., Bloomgarden, E., Behrens, D. M., Jain, S., & Arora, V. M. (2022) · Journal of Medical Internet Research · doi:10.2196/38324 · Cited by 21

From Data to Wisdom: Biomedical Knowledge Graphs for Real-World Data Insights

Hänsel, K., Dudgeon, S. N., Cheung, K., Durant, T. J. S., & Schulz, W. L. (2023) · Journal of Medical Systems · doi:10.1007/s10916-023-01951-2 · Cited by 21

Reading functional requirements using machine learning-based language processing

Akay, H., & Kim, S. (2021) · CIRP Annals · doi:10.1016/j.cirp.2021.04.021 · Cited by 24

Topic Modeling for Faster Literature Screening Using Transformer-Based Embeddings

Galli, C., Cusano, C., Meleti, M., Donos, N., & Calciolari, E. (2024) · Metrics · doi:10.3390/metrics1010002 · Cited by 20

Discovering new pathways toward integration between health and sustainable development goals with natural language processing and network science

Smith, T. B., Vacca, R., Mantegazza, L., & Capua, I. (2023) · Globalization and Health · doi:10.1186/s12992-023-00943-8 · Cited by 20

Toward Representing Research Contributions in Scholarly Knowledge Graphs Using Knowledge Graph Cells

Vogt, L., D’Souza, J., Stocker, M., & Auer, S. (2020) · ACM/IEEE Joint Conference on Digital Libraries · doi:10.1145/3383583.3398530 · Cited by 20

The data paper as a sociolinguistic epistemic object: A content analysis on the rhetorical moves used in data paper abstracts

Li, K., & Jiao, C. (2021) · Journal of the Association for Information Science and Technology · doi:10.1002/asi.24585 · Cited by 23

Information retrieval from scientific abstract and citation databases: A query-by-documents approach based on Monte-Carlo sampling

Lechtenberg, F., Farreres, J., Galvan-Cara, A.-L., Somoza-Tornos, A., Espuña, A., & Graells, M. (2022) · Expert Systems with Applications · doi:10.1016/j.eswa.2022.116967 · Cited by 22

Discovering drug–target interaction knowledge from biomedical literature

Hou, Y., Xia, Y., Wu, L., Xie, S., Yang, F., Zhu, J., Qin, T., & Liu, T. (2022) · Bioinformatics · doi:10.1093/bioinformatics/btac648 · Cited by 20

Overcoming the “Ostrich Effect”: A Narrative Review on the Incentives and Consequences of Questionable Research Practices in Kinesiology

Tiller, N. B., & Panteleimon Ekkekakis, A. (2023) · Kinesiology Review · doi:10.1123/kr.2022-0039 · Cited by 17

Should We Publish Fewer Papers?

Jin, S. (2024) · ACS Energy Letters · doi:10.1021/acsenergylett.4c01991 · Cited by 18

How Data Scientists Review the Scholarly Literature

Mysore, S., Jasim, M., Song, H., Akbar, S., Randall, A. K. C., & Mahyar, N. (2023) · Conference on Human Information Interaction and Retrieval · doi:10.1145/3576840.3578309 · Cited by 19

An online multi-source summarization algorithm for text readability in topic-based search

Curiel, A., Gutiérrez-Soto, C., & Rojano-Cáceres, J. R. (2020) · Computer Speech & Language · doi:10.1016/j.csl.2020.101143 · Cited by 15

Entity Recognition and Relation Extraction from Scientific and Technical Texts in Russian

Bruches, E. P., Pauls, A., Batura, T. V., & Isachenko, V. (2020) · Science and Artificial Intelligence Conference (S.A.I.ence) · doi:10.1109/s.a.i.ence50533.2020.9303196 · Cited by 15

Wormicloud: a new text summarization tool based on word clouds to explore the C. elegans literature

Arnaboldi, V., Cho, J., & Sternberg, P. W. (2021) · Database · doi:10.1093/database/baab015 · Cited by 15

Information Seeking Behaviors, Attitudes, and Choices of Academic Physicists

Gordon, I. D., Chaves, D., Dearborn, D., Hendrikx, S., Hutchinson, R., Popovich, C., & WHITE, M. J. (2022) · Science & Technology Libraries · doi:10.1080/0194262x.2021.1991546 · Cited by 15

Risk of bias assessment tools often addressed items not related to risk of bias and used numerical scores

Siedler, M. R., Kawtharany, H., Azzam, M., Ezgü, D., Alshorman, A., Mikati, I. K. E., Abid, S., Choaib, A., Hamarsha, Q., Murad, M. H., et al. (2025) · Journal of Clinical Epidemiology · doi:10.1016/j.jclinepi.2025.111684 · Cited by 14

What Is Future-Proof Science?

Vickers, P. (2022) · Oxford University Press eBooks · doi:10.1093/oso/9780192862730.003.0001 · Cited by 14

A Unified Nanopublication Model for Effective and User-Friendly Access to the Elements of Scientific Publishing

Bucur, C.-I., Kuhn, T., & Ceolin, D. (2020) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-61244-3_7 · Cited by 8

Unsupervised Event Graph Representation and Similarity Learning on Biomedical Literature

Frisoni, G., Moro, G., Carlassare, G., & Carbonaro, A. (2021) · Sensors · doi:10.3390/s22010003 · Cited by 14

Evidence‐based research in orthopaedics, sports medicine and rehabilitation—Why new studies should rely on earlier work

Prill, R., Pieper, D., Klugar, M., Ayeni, O. R., Karlsson, J., & Lund, H. (2024) · Knee Surgery Sports Traumatology Arthroscopy · doi:10.1002/ksa.12047 · Cited by 14

Qlarify: Recursively Expandable Abstracts for Dynamic Information Retrieval over Scientific Papers

Fok, R., Chang, J. C., August, T., Zhang, A. X., & Weld, D. S. (2024) · ACM Symposium on User Interface Software and Technology (UIST) · doi:10.1145/3654777.3676397 · Cited by 46

Evaluative Frameworks and Scientific Knowledge for Undergraduate STEM Students: An Illustrative Case Study Perspective

Mercer, K., & Weaver, K. D. (2020) · Science & Technology Libraries · doi:10.1080/0194262x.2020.1796891 · Cited by 12

Low resource recognition and linking of biomedical concepts from a large ontology

Mohan, S., Angell, R., Monath, N., & McCallum, A. (2021) · ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics (ACM-BCB) · doi:10.1145/3459930.3469524 · Cited by 13

Educator's blueprint: A how‐to guide for creating a high‐quality infographic

Gottlieb, M. A., Ibrahim, A. M., Martin, L. J., Yılmaz, Y., & Chan, T. M. (2022) · AEM Education and Training · doi:10.1002/aet2.10793 · Cited by 15

Factors affecting global flow of scientific knowledge in environmental sciences

Sonne, C., Dietz, R., & Alstrup, A. K. O. (2019) · The Science of The Total Environment · doi:10.1016/j.scitotenv.2019.135012 · Cited by 12

PMIDigest: Interactive Review of Large Collections of PubMed Entries to Distill Relevant Information

Novoa, J. A. R., Chagoyen, M., Benito, C., Moreno, F. J., & Pazos, F. (2023) · Genes · doi:10.3390/genes14040942 · Cited by 12

Supercharge Your Academic Productivity with Generative Artificial Intelligence

Lonsdale, H., O’Reilly-Shah, V. N., Padiyath, A., & Allan F. Simpao, M. (2024) · Journal of Medical Systems · doi:10.1007/s10916-024-02093-9 · Cited by 13

From data deluge to publomics: How AI can transform animal research

Ineichen, B. V., Rosso, M., & Macleod, M. R. (2023) · Lab Animal · doi:10.1038/s41684-023-01256-4 · Cited by 12

Ensuring effective scholarly communication: traditions and innovations of peer review

Тихонова, Е. В., & Raitskaya, L. (2021) · Science Editor and Publisher · doi:10.24069/2542-0267-2021-1-6-17 · Cited by 14

A bibliometric review on development economics research in Vietnam from 2008 to 2020

Ho, M., Le, N.-T. B., Ho, T. M., & Vuong, Q. (2021) · Quality & Quantity · doi:10.1007/s11135-021-01258-9 · Cited by 13

Towards efficient navigation in digital libraries: Leveraging popularity, semantics and communities to recommend scholarly articles

Yadav, P., & Pervin, N. (2022) · Journal of Informetrics · doi:10.1016/j.joi.2022.101336 · Cited by 11

Prospective Assessment of a Critical Appraisal Teaching Programme on Medical Students’ Confidence and Performance in Appraising Medical Literature

Lee, A. E., Ardissino, M., Bednarczuk, N. F., Tennyson, M., & Khajuria, A. (2020) · The Journal of the Royal College of Physicians of Edinburgh · doi:10.4997/jrcpe.2020.118 · Cited by 11

Scalable Knowledge Graph Analytics at 136 Petaflop/s

Kannan, R., Sao, P., Lu, H., Herrmannová, D., Thakkar, V., Patton, R. M., Vuduc, R. W., & Potok, T. E. (2020) · International Conference for High Performance Computing, Networking, Storage and Analysis (SC) · doi:10.1109/sc41405.2020.00010 · Cited by 11

neuroGPT-X: Towards an Accountable Expert Opinion Tool for Vestibular SchwannomaPreprint

Guo, E., Gupta, M., Sinha, S., Rössler, K., Tatagiba, M. S., Akagami, R., Al‐Mefty, O., Sugiyama, T., Stieg, P. E., Pickett, G. E., et al. (2023) · medRxiv · doi:10.1101/2023.02.25.23286117 · Cited by 11

Application of large language model combined with retrieval enhanced generation technology in digestive endoscopic nursing

Fu, Z., Fu, S., Huang, Y., He, W., Zhong, Z., Guo, Y. X., & Lin, Y. (2024) · Frontiers in Medicine · doi:10.3389/fmed.2024.1500258 · Cited by 12

A surge of AI-driven publications: the impact on health professionals and potential mitigating solutions

Arzilli, G., Maggio, E. D., Angelis, L. D., Baglivo, F., Savoia, E., Privitera, G. P., & Rizzo, C. (2025) · Frontiers in Public Health · doi:10.3389/fpubh.2025.1680630 · Cited by 13

MD‐PhD graduates remain underrepresented in orthopaedic surgery: National MD‐PhD Program Outcome Survey update

von Kaeppler, E. P., Akabas, M. H., Brass, L. F., & Amanatullah, D. F. (2020) · Journal of Orthopaedic Research® · doi:10.1002/jor.24669 · Cited by 10

Reluctance to pursue breakthrough research: A signaling explanation

Besancenot, D., & Radu Vranceanu, B. (2024) · Research Policy · doi:10.1016/j.respol.2024.104974 · Cited by 10

Beyond the null: Recognizing and reporting true negative findings

Schweinfurth, M. K., & Frommen, J. G. (2024) · iScience · doi:10.1016/j.isci.2024.111676 · Cited by 11

Applications of Large Language Models in Medical Research: From Systematic Reviews to Clinical Studies

Gong, E. J., Bang, C. S., & Shin, Y. S. (2026) · Bioengineering · doi:10.3390/bioengineering13030365 · Cited by 14

Can a Transformer Assist in Scientific Writing? Generating Semantic Web Paper Snippets with GPT-2

Meroño-Peñuela, A., Spagnuelo, D., & GPT-2 (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-62327-2_27 · Cited by 12

From Deutsche Zeitschrift to International journal of legal medicine—100 years of legal medicine through the lens of journal articles, Part 4: International journal of legal medicine from 1990 to 2022

Fracasso, T., Wirth, I., Pfeiffer, H., & Schmeling, A. (2023) · International Journal of Legal Medicine · doi:10.1007/s00414-023-03107-w · Cited by 9

Gender and Racial Diversity in Relation to Publication Rates at the Canadian Association of Radiology Annual Scientific Meetings 2016 to 2019

Joarder, I., Ahmadi, S., & Khosa, F. (2023) · Canadian Association of Radiologists Journal · doi:10.1177/08465371231210473 · Cited by 9

A structural topic model approach to scientific reorientation of economics and chemistry after German reunification

Rehs, A. (2020) · Scientometrics · doi:10.1007/s11192-020-03640-0 · Cited by 9

Reexamining “Free Energy Rate Density” as a Complexity Metric

Solis, K. (2024) · Journal of Big History · doi:10.22339/jbh.v7i1.7102 · Cited by 8

Publishing clinical trial results in plain language: a clash of ethical principles?

Pal, A., Klingmann, I., Wangmo, T., & Elger, B. S. (2024) · Current Medical Research and Opinion · doi:10.1080/03007995.2024.2308729 · Cited by 8

Competency in invasion science: addressing stagnation challenges by promoting innovation and creative thinking

Haubrock, P. J., Kurtul, I., Macêdo, R. L., Mammola, S., Franco, A. C. S., & Soto, I. (2024) · Environmental Management · doi:10.1007/s00267-024-02035-8 · Cited by 15

Peer review in the age of artificial intelligence: a comparative study of human and AI-generated review reports

Rajakumar, H. K., Sankaran, K. A., Ashok, M. P., & Rachoori, S. (2026) · Postgraduate Medical Journal · doi:10.1093/postmj/qgag005 · Cited by 10

A Knowledge Representation Model for Studying Knowledge Creation, Usage, and Evolution

Liang, Z., Liu, F., Mao, J., & Lü, K. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-71292-1_9 · Cited by 8

A precision‐preferred comprehensive information extraction system for clinical articles in traditional Chinese Medicine

Xia, Y., Cai, J., Li, Y., Dou, Z., Zhang, Y., Wu, L., Huang, Z., Xu, S., Sun, J., Liu, Y., et al. (2021) · International Journal of Intelligent Systems · doi:10.1002/int.22748 · Cited by 8

Chemical identification and indexing in PubMed full-text articles using deep learning and heuristics

de Almeida, T. D., Antunes, R., Silva, J., Almeida, J. R., & Matos, S. (2022) · Database · doi:10.1093/database/baac047 · Cited by 9

Clinical Research in Renal Transplantation: A Bibliometric Perspective on a Half-century of Innovation and Progress

Jiang, D., Mantas, A., Studier‐Fischer, A., Fuchs, J., Uluk, D., Loos, M., Mieth, M., Zeier, M., Husen, P., Golriz, M., et al. (2024) · Transplantation · doi:10.1097/tp.0000000000004887 · Cited by 8

Creativity research in medicine and nursing: A scoping review

Thabane, A., Saleh, S., Pallapothu, S., McKechnie, T., Staibano, P., Busse, J. W., Calic, G., Sonnadara, R., Parpia, S., & Bhandari, M. (2025) · PLoS ONE · doi:10.1371/journal.pone.0317209 · Cited by 8

HiPerRAG: High-Performance Retrieval Augmented Generation for Scientific Insights

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