The DOI matches its Crossref record; 43 of the 44 references it cites were found in a registry or an index, and 1 are not checked yet.
ChatGPT outperforms humans in emotional awareness evaluations
Open accessAbstract
The artificial intelligence chatbot, ChatGPT, has gained widespread attention for its ability to perform natural language processing tasks and has the fastest-growing user base in history. Although ChatGPT has successfully generated theoretical information in multiple fields, its ability to identify and describe emotions is still unknown. Emotional awareness (EA), the ability to conceptualize one’s own and others’ emotions, is considered a transdiagnostic mechanism for psychopathology. This study utilized the Levels of Emotional Awareness Scale (LEAS) as an objective, performance-based test to analyze ChatGPT’s responses to twenty scenarios and compared its EA performance with that of the general population norms, as reported by a previous study. A second examination was performed one month later to measure EA improvement over time. Finally, two independent licensed psychologists evaluated the fit-to-context of ChatGPT’s EA responses. In the first examination, ChatGPT demonstrated significantly higher performance than the general population on all the LEAS scales (Z score = 2.84). In the second examination, ChatGPT’s performance significantly improved, almost reaching the maximum possible LEAS score (Z score = 4.26). Its accuracy levels were also extremely high (9.7/10). The study demonstrated that ChatGPT can generate appropriate EA responses, and that its performance may improve significantly over time. The study has theoretical and clinical implications, as ChatGPT can be used as part of cognitive training for clinical populations with EA impairments. In addition, ChatGPT’s EA-like abilities may facilitate psychiatric diagnosis and assessment and be used to enhance emotional language. Further research is warranted to better understand the potential benefits and risks of ChatGPT and refine it to promote mental health.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The cited source says what the claim attributes to it.
In the citing paper
“Nevertheless, ChatGPT demonstrates strong capabilities in emotional understanding, including generating appropriate affective responses and scoring highly on measures of emotional awareness [18, 69].”
From Emotional Coregulation in Close Relationships with AI Agents: A Survey of ChatGPT and Replika Users (2025) · cites this paper as [18]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing
Benjamini, Y., & Hochberg, Y. (1995) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.2517-6161.1995.tb02031.x · Cited by 109,482
Cicchetti, D. V. (1994) · Psychological Assessment · doi:10.1037/1040-3590.6.4.284 · Cited by 9,349 · cited 2×
“ICC was calculated, and good consistency (ICC = 0.720) and absolute agreement (ICC = 0.659) was found between the psychologists’ ratings (Cicchetti, 1994).”
BackgroundResultInfluentialBagby, R. M., Parker, J. D. A., & Taylor, G. J. (1994) · Journal of Psychosomatic Research · doi:10.1016/0022-3999(94)90005-1 · Cited by 5,293
Baron‐Cohen, S., Wheelwright, S., Hill, J., Raste, Y., & Plumb, I. (2001) · Journal of Child Psychology and Psychiatry · doi:10.1111/1469-7610.00715 · Cited by 4,555 · cited 1×
“While mental state recognition (emotion and/or thoughts) tests, such as false belief tasks (Birch and Bloom, 2007), the reading the mind in the eyes task (Baron-Cohen et al., 2001), or the strange stories test (Happé, 1994), and self-reported assessments like the Toronto alexithymia scale (Bagby et al.”
BackgroundBaron‐Cohen, S., Wheelwright, S. J., Hill, J., Raste, Y., & Plumb, I. (2001) · PubMed · Cited by 4,313
Kung, T. H., Cheatham, M., Medenilla, A., Sillos, C., De Leon, L., Elepaño, C., Madriaga, M., Aggabao, R., Diaz-Candido, G., Maningo, J., et al. (2023) · PLOS Digital Health · doi:10.1371/journal.pdig.0000198 · Cited by 4,115
Fitzpatrick, K. K., Darcy, A., & Vierhile, M. (2017) · JMIR Mental Health · doi:10.2196/mental.7785 · Cited by 2,730 · cited 1×
“Woebot employs learned skills such as identifying and challenging cognitive distortions to monitor symptoms and episodes of these mental health conditions (Fitzpatrick et al., 2017).”
BackgroundHappé, F. G. E. (1994) · Journal of Autism and Developmental Disorders · doi:10.1007/bf02172093 · Cited by 2,352
ChatGPT: Bullshit spewer or the end of traditional assessments in higher education?
(2023) · Journal of Applied Learning & Teaching · doi:10.37074/jalt.2023.6.1.9 · Cited by 1,806 · cited 3×
“org It is a large language model trained on abundant text data, which makes it capable of generating human-like responses to text-based inputs (Rudolph et al., 2023).”
BackgroundEmotional and social intelligence: Insights from the Emotional Quotient Inventory.
Bar‐On, R. (2000) · Cited by 1,018
Research works printed but not linked: 2
- Corp, 2021, IBM SPSS statistics for windows (version 28.0) [computer software]
- Hu, 2023, ChatGPT sets record for fastest-growing user base—analyst note
Cited by
Sorted by citations
The Rise and Potential of Large Language Model Based Agents: A Survey
Xi, Z., Chen, W., Guo, X., He, W., Ding, Y., Hong, B., Zhang, M., Wang, J., Jin, S., Zhou, E., et al. (2023) · arXiv · doi:10.48550/arxiv.2309.07864 · Cited by 972
The rise and potential of large language model based agents: a survey
Xi, Z., Chen, W., Guo, X., He, W., Ding, Y., Hong, B., Zhang, M., Wang, J., Jin, S., Zhou, E., et al. (2025) · Science China Information Sciences · doi:10.1007/s11432-024-4222-0 · Cited by 970
Tam, T. Y. C., Sivarajkumar, S., Kapoor, S., Stolyar, A. V., Polanska, K., McCarthy, K. R., Osterhoudt, H., Wu, X., Visweswaran, S., Fu, S., et al. (2024) · npj Digital Medicine · doi:10.1038/s41746-024-01258-7 · Cited by 424
Large language models empowered agent-based modeling and simulation: a survey and perspectives
Gao, C., Lan, X., Li, N., Yuan, Y., Ding, J., Zhou, Z., Xu, F., & Li, Y. (2024) · Humanities and Social Sciences Communications · doi:10.1057/s41599-024-03611-3 · Cited by 294
Large Language Models and Empathy: Systematic Review
Sorin, V., Brin, D., Barash, Y., Konen, E., Charney, A., Nadkarni, G., & Klang, E. (2024) · Journal of Medical Internet Research · doi:10.2196/52597 · Cited by 240
Khawaja, Z., & Bélisle-Pipon, J.-C. (2023) · Frontiers in Digital Health · doi:10.3389/fdgth.2023.1278186 · Cited by 214
Explainable Generative AI (GenXAI): a survey, conceptualization, and research agenda
Schneider, J. (2024) · Artificial Intelligence Review · doi:10.1007/s10462-024-10916-x · Cited by 214
Beyond human expertise: the promise and limitations of ChatGPT in suicide risk assessment
Elyoseph, Z., & Levkovich, I. (2023) · Frontiers in Psychiatry · doi:10.3389/fpsyt.2023.1213141 · Cited by 116
Self-citation(2023) · doi:10.2196/54369 · Cited by 127
Self-citationChatGPT in medicine: prospects and challenges: a review article
Tan, S., Xin, X., & Wu, D. (2024) · International Journal of Surgery · doi:10.1097/js9.0000000000001312 · Cited by 130
Suicide Risk Assessments Through the Eyes of ChatGPT-3.5 Versus ChatGPT-4: Vignette Study
Levkovich, I., & Elyoseph, Z. (2023) · JMIR Mental Health · doi:10.2196/51232 · Cited by 119
Self-citationMindfulDiary: Harnessing Large Language Model to Support Psychiatric Patients' Journaling
Kim, T., Bae, S., Kim, H. A., Lee, S. H., Hong, H., Yang, C.-M., & Kim, Y. (2024) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3613904.3642937 · Cited by 173
Regulating AI in Mental Health: Ethics of Care Perspective
Tavory, T. (2024) · JMIR Mental Health · doi:10.2196/58493 · Cited by 108
Ni, Y., & Jia, F. (2025) · Healthcare · doi:10.3390/healthcare13101205 · Cited by 144
ChatGPT: perspectives from human–computer interaction and psychology
Liu, J. (2024) · Frontiers in Artificial Intelligence · doi:10.3389/frai.2024.1418869 · Cited by 99
Can AI replace psychotherapists? Exploring the future of mental health care
Zhang, Z., & Wang, J. (2024) · Frontiers in Psychiatry · doi:10.3389/fpsyt.2024.1444382 · Cited by 112
Applications of large language models in psychiatry: a systematic review
Omar, M., Soffer, S., Charney, A. W., Landi, I., Nadkarni, G. N., & Klang, E. (2024) · Frontiers in Psychiatry · doi:10.3389/fpsyt.2024.1422807 · Cited by 85
Levkovich, I., & Elyoseph, Z. (2023) · Family Medicine and Community Health · doi:10.1136/fmch-2023-002391 · Cited by 91
Self-citationHaber, Y., Levkovich, I., Hadar‐Shoval, D., & Elyoseph, Z. (2024) · JMIR Mental Health · doi:10.2196/54781 · Cited by 83
Self-citationYu, S., Androsov, A., Yan, H., & Chen, Y. (2024) · Computers & Education · doi:10.1016/j.compedu.2024.105111 · Cited by 90
Kolding, S., Lundin, R. M., Hansen, L., & Østergaard, S. D. (2024) · Acta Neuropsychiatrica · doi:10.1017/neu.2024.50 · Cited by 97
Elyoseph, Z., Levkovich, I., & Shinan‐Altman, S. (2024) · Family Medicine and Community Health · doi:10.1136/fmch-2023-002583 · Cited by 77
Self-citationEvaluating Generative AI in Mental Health: Systematic Review of Capabilities and Limitations
Wang, L., Bhanushali, T., Huang, Z., Yang, J., Badami, S., & Hightow-Weidman, L. (2025) · JMIR Mental Health · doi:10.2196/70014 · Cited by 73
Henriksen, D., Creely, E., Gruber, N., & Leahy, S. (2025) · Journal of Teacher Education · doi:10.1177/00224871251325058 · Cited by 94
ChatGPT’s Response Consistency: A Study on Repeated Queries of Medical Examination Questions
Funk, P. F., Hoch, C. C., Knoedler, S., Knoedler, L., Cotofana, S., Sofo, G., Bashiri Dezfouli, A., Wollenberg, B., Guntinas-Lichius, O., & Alfertshofer, M. (2024) · European Journal of Investigation in Health, Psychology and Education · doi:10.3390/ejihpe14030043 · Cited by 69
Tang, Y., Chen, L., Chen, Z., Chen, W., Cai, Y., Du, Y., Yang, F., & Sun, L. (2024) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3613904.3642899 · Cited by 79
Irfan, B., Kuoppamäki, S., & Skantze, G. (2024) · Frontiers in Robotics and AI · doi:10.3389/frobt.2024.1363713 · Cited by 82
Rezai, A., Soyoof, A., & Reynolds, B. L. (2024) · European Journal of Education · doi:10.1111/ejed.12752 · Cited by 86
Kim, J., Leonte, K. G., Chen, M. L., Torous, J. B., Linos, E., Pinto, A., & Rodríguez, C. I. (2024) · npj Digital Medicine · doi:10.1038/s41746-024-01181-x · Cited by 67
Will users fall in love with ChatGPT? A perspective from the triangular theory of love
Chen, Q., Jing, Y., Gong, Y., & Tan, J. (2025) · Journal of Business Research · doi:10.1016/j.jbusres.2024.114982 · Cited by 66
Vowels, L. M., Francois-Walcott, R. R. R., & Darwiche, J. (2024) · Computers in Human Behavior: Artificial Humans · doi:10.1016/j.chbah.2024.100078 · Cited by 59
Elyoseph, Z., & Levkovich, I. (2024) · JMIR Mental Health · doi:10.2196/53043 · Cited by 66
Self-citationThe plasticity of ChatGPT’s mentalizing abilities: personalization for personality structures
Hadar‐Shoval, D., Elyoseph, Z., & Lvovsky, M. (2023) · Frontiers in Psychiatry · doi:10.3389/fpsyt.2023.1234397 · Cited by 54
Self-citationHumans Vs. Service Robots as Social Actors in Persuasion Settings
Lee, H., & Yi, Y. (2024) · Journal of Service Research · doi:10.1177/10946705241278842 · Cited by 58
Watari, T., Takagi, S., Sakaguchi, K., Nishizaki, Y., Shimizu, T., Yamamoto, Y., & Tokuda, Y. (2023) · JMIR Medical Education · doi:10.2196/52202 · Cited by 54
Comparing GPT-4 and Human Researchers in Health Care Data Analysis: Qualitative Description Study
Li, K. D., Fernandez, A., Schwartz, R., Ríos, N., Carlisle, M., Amend, G., Patel, H. V., & Breyer, B. N. (2024) · Journal of Medical Internet Research · doi:10.2196/56500 · Cited by 65
An Ethical Perspective on the Democratization of Mental Health With Generative AI
Elyoseph, Z., Gur, T., Haber, Y., Simon, T., Angert, T., Navon, Y., Tal, A., & Asman, O. (2024) · JMIR Mental Health · doi:10.2196/58011 · Cited by 64
Self-citationLarge language models can outperform humans in social situational judgments
Mittelstädt, J. M., Maier, J., Goerke, P., Zinn, F. D., & Hermes, M. (2024) · Scientific Reports · doi:10.1038/s41598-024-79048-0 · Cited by 69
Luo, X., Wang, Z., Tilley, J. L., Balarajan, S., Bassey, U.-A., & Cheang, C. I. (2025) · JMIR Mental Health · doi:10.2196/77951 · Cited by 48
Large language models are proficient in solving and creating emotional intelligence tests
Schlegel, K., Sommer, N. R., & Mortillaro, M. (2025) · Communications Psychology · doi:10.1038/s44271-025-00258-x · Cited by 46
Chat Generative Pre-Trained Transformer (ChatGPT) usage in healthcare
Zhang, Y., Pei, H., Zhen, S., Li, Q., & Liang, F. (2023) · Gastroenterology & Endoscopy · doi:10.1016/j.gande.2023.07.002 · Cited by 57
Akdogan, O., Uyar, G. C., Yesilbas, E., Baskurt, K., Malkoc, N. A., Ozdemir, N., Yazici, O., Oksuzoglu, B., Uner, A., Ozet, A., et al. (2025) · European Journal of Cancer · doi:10.1016/j.ejca.2025.115408 · Cited by 45
Large Language Models (LLMs) and Empathy – A Systematic ReviewPreprint
Sorin, V., Brin, D., Barash, Y., Konen, E., Charney, A. W., Nadkarni, G. N., & Klang, E. (2023) · medRxiv · doi:10.1101/2023.08.07.23293769 · Cited by 45
Debate and Dilemmas Regarding Generative AI in Mental Health Care: Scoping Review
Xian, X., Chang, A., Xiang, Y., & Liu, M. T. (2024) · Interactive Journal of Medical Research · doi:10.2196/53672 · Cited by 52
Yonatan‐Leus, R., & Brukner, H. (2024) · Counselling and Psychotherapy Research · doi:10.1002/capr.12832 · Cited by 46
Zhang, R., Zou, D., Cheng, G., & Xie, H. (2024) · Computers in Human Behavior · doi:10.1016/j.chb.2024.108457 · Cited by 52
Vision-Enabled Large Language and Deep Learning Models for Image-Based Emotion Recognition
Nadeem, M., Sohail, S. S., Javed, L., Anwer, F., Saudagar, A. K. J., & Muhammad, K. (2024) · Cognitive Computation · doi:10.1007/s12559-024-10281-5 · Cited by 42
Lim, S., Schmälzle, R., & Bente, G. (2025) · Computers in Human Behavior Artificial Humans · doi:10.1016/j.chbah.2025.100172 · Cited by 58
Nedbal, C., Naik, N., Castellani, D., Gauhar, V., Geraghty, R. M., & Somani, B. K. (2023) · Current Opinion in Urology · doi:10.1097/mou.0000000000001151 · Cited by 39
Acceptability lies in the eye of the beholder: Self-other biases in GenAI collaborations
Celiktutan, B., Klesse, A.-K., & Tuk, M. A. (2024) · International Journal of Research in Marketing · doi:10.1016/j.ijresmar.2024.05.006 · Cited by 41
Deceptively Simple yet Profoundly Impactful: Text Messaging Interventions to Support Health
Suffoletto, B. P. (2024) · Journal of Medical Internet Research · doi:10.2196/58726 · Cited by 39
Li, T., Zhan, Z., Ji, Y., & Li, T. (2025) · The Internet and Higher Education · doi:10.1016/j.iheduc.2025.101003 · Cited by 43
Hickman, L., Dunlop, P. D., & Wolf, J. L. (2024) · International Journal of Selection and Assessment · doi:10.1111/ijsa.12479 · Cited by 33
The Roles of Social Perception and AI Anxiety in Individuals’ Attitudes Toward ChatGPT in Education
Wang, C., Li, X., Liang, Z., Sheng, Y., Zhao, Q., & Chen, S. (2024) · International Journal of Human-Computer Interaction · doi:10.1080/10447318.2024.2365453 · Cited by 38
Esmaeilzadeh, P., Maddah, M., & Mirzaei, T. (2025) · Computers in Human Behavior Artificial Humans · doi:10.1016/j.chbah.2025.100127 · Cited by 47
Sufyan, N. S., Fadhel, F. H., Alkhathami, S., & Mukhadi, J. Y. A. (2024) · Frontiers in Psychology · doi:10.3389/fpsyg.2024.1353022 · Cited by 21
Kavitha, K., Joshith, V. P., & Sharma, S. (2024) · E-Learning and Digital Media · doi:10.1177/20427530241259099 · Cited by 49
Is Generative AI Increasing the Risk for Technology‐Mediated Trauma Among Vulnerable Populations?
Abdulai, A. (2024) · Nursing Inquiry · doi:10.1111/nin.12686 · Cited by 36
Wang, S., Mo, C., Chen, Y., Dai, X., Wang, H., & Shen, X. (2024) · JMIR Medical Education · doi:10.2196/55595 · Cited by 32
War, emotions, mental health, and artificial intelligence
Ćosić, K., Kopilaš, V., & Jovanović, T. (2024) · Frontiers in Psychology · doi:10.3389/fpsyg.2024.1394045 · Cited by 56
The human-AI dyad: Navigating the new frontier of entrepreneurial discourse
Jeremiah, F. (2024) · Futures · doi:10.1016/j.futures.2024.103529 · Cited by 38
Girton, M., Greene, D. N., Lambert-Messerlian, G. M., Keren, D. F., & Yu, M. (2024) · Clinical Chemistry · doi:10.1093/clinchem/hvae093 · Cited by 29
Effects of Applicants' Use of Generative AI in Personnel Selection: Towards a More Nuanced View?
Lievens, F., & Dunlop, P. D. (2024) · International Journal of Selection and Assessment · doi:10.1111/ijsa.12516 · Cited by 33
“It’s Not Only Attention We Need”: Systematic Review of Large Language Models in Mental Health Care
Bucher, A. M., Egger, S., Vashkite, I., Wu, W., & Schwabe, G. (2025) · JMIR Mental Health · doi:10.2196/78410 · Cited by 42
Mental Health Applications of Generative AI and Large Language Modeling in the United States
Banerjee, S. K., Dunn, P. J., Conard, S. E., & Ali, A. (2024) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph21070910 · Cited by 27
A data-driven approach to predicting consumer preferences for product customization
Powell, C., Zhu, E., Xiong, Y., & Yang, S. L. (2023) · Advanced Engineering Informatics · doi:10.1016/j.aei.2023.102321 · Cited by 25
Luo, X., Ghosh, S., Tilley, J. L., Besada, P., Wang, J., & Xiang, Y. (2025) · Digital Health · doi:10.1177/20552076251351088 · Cited by 42
Exploring the role of ChatGPT in oncology: providing information and support for cancer patients
Cè, M., Chiarpenello, V., Bubba, A., Felisaz, P. F., Oliva, G., Irmici, G., & Cellina, M. (2024) · BioMedInformatics · doi:10.3390/biomedinformatics4020049 · Cited by 24
Hadar‐Shoval, D., Asraf, K., Shinan‐Altman, S., Elyoseph, Z., & Levkovich, I. (2024) · Heliyon · doi:10.1016/j.heliyon.2024.e38056 · Cited by 27
Self-citationBeyond Personhood: Ethical Paradigms in the Generative Artificial Intelligence Era
Elyoseph, Z., Shoval, D. H., & Levkovich, I. (2024) · The American Journal of Bioethics · doi:10.1080/15265161.2023.2278546 · Cited by 24
Self-citationLarge Language Models for Mental Health Applications: Systematic Review (Preprint)Preprint
Guo, Z., Lai, A. G., Thygesen, J. H., Farrington, J., Keen, T., & Li, K. (2024) · doi:10.2196/preprints.57400 · Cited by 30
The high-dimensional psychological profile of ChatGPT
Zhang, Y., Shao, L. H., Yuan, X., Yuan, H., Che, Z., & Luo, S. (2025) · Science China Technological Sciences · doi:10.1007/s11431-025-2934-8 · Cited by 23
Suwała, S., Szulc, P., Guzowski, C., Kaminska, B. A., Dorobiała, J., Wojciechowska, K., Berska, M., Kubicka, O., Kosturkiewicz, O., Kosztulska, B., et al. (2024) · SAGE Open Medicine · doi:10.1177/20503121241257777 · Cited by 27
Beyond WEIRD: Can synthetic survey participants substitute for humans in global policy research?
Shrestha, P., Krpan, D., Koaik, F., Schnider, R., Sayess, D., & Binbaz, M. S. (2024) · Behavioral Science & Policy · doi:10.1177/23794607241311793 · Cited by 26
Yu, L., Ge, Y., Ansari, S., Imran, M. A., & Ahmad, W. (2025) · Sensors · doi:10.3390/s25154763 · Cited by 26
Burke‐Garcia, A., & Hicks, R. S. (2024) · Journal of Health Communication · doi:10.1080/10810730.2024.2357575 · Cited by 25
Haber, Y., Shoval, D. H., Levkovich, I., Yinon, D., Gigi, K., Pen, O., Angert, T., & Elyoseph, Z. (2025) · Frontiers in Digital Health · doi:10.3389/fdgth.2025.1512273 · Cited by 20
Self-citationHuang, Y., Wang, W., Zhou, J., Zhang, L., Lin, J., Liu, H., Hu, X., Zhou, Z., & Dong, W. (2025) · Information Processing & Management · doi:10.1016/j.ipm.2025.104152 · Cited by 22
Expanding horizons and navigating challenges for enhanced clinical workflows: ChatGPT in urology
Talyshinskii, A., Naik, N., Hameed, B. M. Z., Zhanbyrbekuly, U., Khairli, G., Гулиев, Б. Г., Juilebø-Jones, P., Tzelves, L. I., & Somani, B. K. (2023) · Frontiers in Surgery · doi:10.3389/fsurg.2023.1257191 · Cited by 21
Wang, K., Pan, Z., & Lu, Y.-B. (2024) · Kybernetes · doi:10.1108/k-04-2024-0894 · Cited by 17
Yu, S., Androsov, A., & Yan, H. (2025) · Computers & Education · doi:10.1016/j.compedu.2025.105307 · Cited by 22
Using GenAI to Train Mental Health Professionals in Suicide Risk Assessment
Elyoseph, Z., Levkovich, I., Haber, Y., & Levi‐Belz, Y. (2025) · The Journal of Clinical Psychiatry · doi:10.4088/jcp.24m15525 · Cited by 19
Self-citationShinan-Altman, S., Elyoseph, Z., & Levkovich, I. (2024) · PeerJ · doi:10.7717/peerj.17468 · Cited by 21
Self-citationArtificial intelligence in nurse education – a new sparring partner?
Krumsvik, R. J. (2024) · Nordic Journal of Digital Literacy · doi:10.18261/njdl.19.3.5 · Cited by 18
Okamoto, S., Kataoka, M., Itano, M., & Sawai, T. (2025) · BMC Medical Education · doi:10.1186/s12909-025-06801-y · Cited by 18
Etaiwi, W., & Alhijawi, B. (2025) · Array · doi:10.1016/j.array.2025.100478 · Cited by 25
Baucon, A., & de Carvalho, C. N. (2024) · Geoheritage · doi:10.1007/s12371-024-01011-2 · Cited by 23
Saviano, M., Thomas, A., Prete, M. D., Verderese, D., & Sasso, P. (2025) · European Journal of Innovation Management · doi:10.1108/ejim-05-2024-0520 · Cited by 25
Averse to what: Consumer aversion to algorithmic labels, but not their outputs?
Mariadassou, S., Klesse, A.-K., & Boegershausen, J. (2024) · Current Opinion in Psychology · doi:10.1016/j.copsyc.2024.101839 · Cited by 19
Khorev, V. S., Kiselev, A. R., Badarin, A., Antipov, V. M., Drapkina, O. M., Kurkin, S., & Hramov, A. E. (2024) · The European Physical Journal Special Topics · doi:10.1140/epjs/s11734-024-01289-x · Cited by 17
Hadar‐Shoval, D., Haber, Y., Tal, A., Simon, T., Elyoseph, T., & Elyoseph, Z. (2024) · JMIR Neurotechnology · doi:10.2196/64182 · Cited by 23
Self-citationBiassoni, F., & Gnerre, M. (2025) · Patient Education and Counseling · doi:10.1016/j.pec.2025.108663 · Cited by 17
ChatGPT in Oncology Diagnosis and Treatment: Applications, Legal and Ethical Challenges
Zhou, Z., Qin, P., Cheng, X., Shao, M., Ren, Z., Zhao, Y., Li, Q., & Liu, L. (2025) · Current Oncology Reports · doi:10.1007/s11912-025-01649-3 · Cited by 17
Maltby, J., Rayes, T., Nage, A., Sharif, S., Omar, M., & Nichani, S. (2024) · PLoS ONE · doi:10.1371/journal.pone.0307164 · Cited by 31
Confronting and managing ethical dilemmas in social work using ChatGPT
Segal, M. (2024) · European Journal of Social Work · doi:10.1080/13691457.2024.2377786 · Cited by 15
Seok, J., Lee, B. H., Kim, D., Bak, S., Kim, S., Kim, S.-T., & Park, S.-M. (2025) · International Journal of Human-Computer Interaction · doi:10.1080/10447318.2024.2443263 · Cited by 19
Does Social Bot Help Socialize? Evidence from a Microblogging Platform
Gao, Y. A., Zhang, M. M., & Lysyakov, M. (2025) · Information Systems Research · doi:10.1287/isre.2024.1089 · Cited by 18
Huhtanen, H., Nyman, M. J., Karlsson, A., & Hirvonen, J. (2025) · Radiology Artificial Intelligence · doi:10.1148/ryai.230620 · Cited by 14
Copyright Protection for AI-Generated Works
Wang, F. F. (2023) · Amicus Curiae · doi:10.14296/ac.v5i1.5663 · Cited by 14
GPT-4 Emulates Average-Human Emotional Cognition from a Third-Person Perspective
Tak, A. N., & Gratch, J. S. (2024) · International Conference on Affective Computing and Intelligent Interaction and workshops · doi:10.1109/acii63134.2024.00043 · Cited by 13
Metadata
- Authors
- Elyoseph, Z., Hadar-Shoval, D., Asraf, K., & Lvovsky, M.
- Affiliations
- The Max Stern Yezreel Valley College (IL)
- Corresponding author
- Elyoseph, Zohar
- Venue
- Frontiers in Psychology
- Publisher
- Frontiers Media SA
- Published
- 2023-05-26
- Volume
- 14
- Pages
- 1199058-1199058
- ISSN
- 1664-1078 (ISSN-L)
- PMC
- PMC10254409
- OpenAlex
- W4378470708
- Cited by
- 324
- References
- 43
- Citations by year
- 2026: 63 · 2025: 122 · 2024: 117 · 2023: 22
- FWCI
- 83.77
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Frontiers in Psychology (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Emotion and Mood Recognition
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Physical Sciences › Computer Science › Artificial Intelligence › Ethics and Social Impacts of AI
- Keywords
- ChatGPT, emotional awareness, Levels of Emotional Awareness Scale, large language models, emotion recognition, AI chatbots, transdiagnostic mechanisms, psychopathology, cognitive training, psychiatric diagnosis, emotional expression, human-AI comparison
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review