{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,21]],"date-time":"2026-05-21T10:28:26Z","timestamp":1779359306832,"version":"3.51.4"},"reference-count":56,"publisher":"Springer Science and Business Media LLC","issue":"5","license":[{"start":{"date-parts":[[2024,5,27]],"date-time":"2024-05-27T00:00:00Z","timestamp":1716768000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,5,27]],"date-time":"2024-05-27T00:00:00Z","timestamp":1716768000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Cogn Comput"],"published-print":{"date-parts":[[2024,9]]},"DOI":"10.1007\/s12559-024-10281-5","type":"journal-article","created":{"date-parts":[[2024,5,27]],"date-time":"2024-05-27T04:01:27Z","timestamp":1716782487000},"page":"2566-2579","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":31,"title":["Vision-Enabled Large Language and Deep Learning Models for Image-Based Emotion Recognition"],"prefix":"10.1007","volume":"16","author":[{"given":"Mohammad","family":"Nadeem","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shahab Saquib","family":"Sohail","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Laeeba","family":"Javed","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Faisal","family":"Anwer","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Abdul Khader Jilani","family":"Saudagar","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Khan","family":"Muhammad","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2024,5,27]]},"reference":[{"key":"10281_CR1","doi-asserted-by":"crossref","unstructured":"Sai S, Mittal U, Chamola V, Huang K, Spinelli I, Scardapane S, Tan Z, Hussain A. Machine un-learning: an overview of techniques, applications, and future directions. Cogn Comput. 2023;1\u201325.","DOI":"10.1007\/s12559-023-10219-3"},{"issue":"1","key":"10281_CR2","doi-asserted-by":"publisher","first-page":"41","DOI":"10.1002\/isaf.1531","volume":"30","author":"DE O\u2019Leary","year":"2023","unstructured":"O\u2019Leary DE. An analysis of three chatbots: BlenderBot, ChatGPT and Lamda. Intell Syst Accounting Fin Manage. 2023;30(1):41\u201354.","journal-title":"Intell Syst Accounting Fin Manage"},{"key":"10281_CR3","unstructured":"Taylor R, Kardas M, Cucurull G, Scialom T, Hartshorn A, Saravia E, Poulton A, Kerkez V, Stojnic R. Galactica: a large language model for science. arXiv:2211.09085 [Preprint]. 2022. Available from: http:\/\/arxiv.org\/abs\/2211.09085."},{"key":"10281_CR4","unstructured":"Touvron H, Lavril T, Izacard G, Martinet X, Lachaux M-A, Lacroix T, Rozi\u00e8re B, Goyal N, Hambro E, Azhar F et\u00a0al. LLaMa: open and efficient foundation language models. arXiv:2302.13971 [Preprint]. 2023. Available from: http:\/\/arxiv.org\/abs\/2302.13971."},{"key":"10281_CR5","unstructured":"Taori R, Gulrajani I, Zhang T, Dubois Y, Li X, Guestrin C, Liang P, Hashimoto TB. Stanford Alpaca: an instruction-following LLaMa model. 2023. https:\/\/github.com\/tatsu-lab\/stanford_alpaca."},{"key":"10281_CR6","first-page":"38176","volume":"35","author":"M Bakker","year":"2022","unstructured":"Bakker M, Chadwick M, Sheahan H, Tessler M, Campbell-Gillingham L, Balaguer J, McAleese N, Glaese A, Aslanides J, Botvinick M, et al. Fine-tuning language models to find agreement among humans with diverse preferences. Adv Neural Inf Process Syst. 2022;35:38176\u201389.","journal-title":"Adv Neural Inf Process Syst."},{"issue":"7972","key":"10281_CR7","doi-asserted-by":"publisher","first-page":"172","DOI":"10.1038\/s41586-023-06291-2","volume":"620","author":"K Singhal","year":"2023","unstructured":"Singhal K, Azizi S, Tu T, Mahdavi SS, Wei J, Chung HW, Scales N, Tanwani A, Cole-Lewis H, Pfohl S, et al. Large language models encode clinical knowledge. Nature. 2023;620(7972):172\u201380.","journal-title":"Nature"},{"key":"10281_CR8","doi-asserted-by":"publisher","unstructured":"Ray PP. ChatGPT: a comprehensive review on background, applications, key challenges, bias, ethics, limitations and future scope. Internet Things Cyber-Phys Syst. 2023;3:121\u201354. https:\/\/doi.org\/10.1016\/j.iotcps.2023.04.003.","DOI":"10.1016\/j.iotcps.2023.04.003"},{"key":"10281_CR9","doi-asserted-by":"publisher","first-page":"126708","DOI":"10.1016\/j.neucom.2023.126708","volume":"557","author":"B Zhao","year":"2023","unstructured":"Zhao B, Jin W, Del Ser J, Yang G. ChatAgri: exploring potentials of ChatGPT on cross-linguistic agricultural text classification. Neurocomputing. 2023;557:126708.","journal-title":"Neurocomputing"},{"key":"10281_CR10","unstructured":"Zhang H, Li X, Bing L. Video-LLaMa: an instruction-tuned audio-visual language model for video understanding. arXiv:2306.02858 [Preprint]. Available from: http:\/\/arxiv.org\/abs\/2306.02858."},{"key":"10281_CR11","doi-asserted-by":"publisher","first-page":"143657","DOI":"10.1109\/ACCESS.2023.3339553","volume":"11","author":"V Hassija","year":"2023","unstructured":"Hassija V, Chakrabarti A, Singh A, Chamola V, Sikdar B. Unleashing the potential of conversational AI: Amplifying Chat-GPT\u2019s capabilities and tackling technical hurdles. IEEE Access. 2023;11:143657\u201382. https:\/\/doi.org\/10.1109\/ACCESS.2023.3339553.","journal-title":"IEEE Access."},{"key":"10281_CR12","doi-asserted-by":"publisher","first-page":"103662","DOI":"10.1016\/j.frl.2023.103662","volume":"53","author":"M Dowling","year":"2023","unstructured":"Dowling M, Lucey B. ChatGPT for (finance) research: the Bananarama conjecture. Financ Res Lett. 2023;53:103662.","journal-title":"Financ. Res. Lett."},{"issue":"1","key":"10281_CR13","doi-asserted-by":"publisher","first-page":"51","DOI":"10.1136\/leader-2023-000797","volume":"8","author":"E Loh","year":"2023","unstructured":"Loh E. Chatgpt and generative AI chatbots: Challenges and opportunities for science, medicine and medical leaders. BMJ Leader. 2023;8(1):51\u20134. https:\/\/doi.org\/10.1136\/leader-2023-000797.","journal-title":"BMJ Leader"},{"issue":"1","key":"10281_CR14","doi-asserted-by":"publisher","first-page":"33","DOI":"10.1007\/s10916-023-01925-4","volume":"47","author":"M Cascella","year":"2023","unstructured":"Cascella M, Montomoli J, Bellini V, Bignami E. Evaluating the feasibility of ChatGPT in healthcare: an analysis of multiple clinical and research scenarios. J Med Syst. 2023;47(1):33.","journal-title":"J. Med. Syst."},{"key":"10281_CR15","doi-asserted-by":"crossref","unstructured":"Sohail SS, Farhat F, Himeur Y, Nadeem M, Madsen D\u00d8, Singh Y, Atalla S, Mansoor W. Decoding ChatGPT: a taxonomy of existing research, current challenges, and possible future directions. J King Saud Univ Comput Inf Sci. 2023;101675.","DOI":"10.2139\/ssrn.4413921"},{"key":"10281_CR16","first-page":"742","volume-title":"14th IIAI International Congress on Advanced Applied Informatics (IIAI-AAI)","author":"M Sashida","year":"2023","unstructured":"Sashida M, Izumi K, Sakaji H. Extraction SDGS-related sentences from sustainability reports using Bert and ChatGPT. In: 14th IIAI International Congress on Advanced Applied Informatics (IIAI-AAI). IEEE; 2023. p. 742\u20135."},{"key":"10281_CR17","doi-asserted-by":"crossref","unstructured":"Mosaiyebzadeh F, Pouriyeh S, Parizi R, Dehbozorgi N, Dorodchi M, Mac\u00eado\u00a0Batista D. Exploring the role of ChatGPT in education: applications and challenges. In: Proceedings of the 24th Annual Conference on Information Technology Education. 2023. p. 84\u20139.","DOI":"10.1145\/3585059.3611445"},{"issue":"6","key":"10281_CR18","doi-asserted-by":"publisher","first-page":"178","DOI":"10.1038\/s41397-023-00316-9","volume":"23","author":"GP Patrinos","year":"2023","unstructured":"Patrinos GP, Sarhangi N, Sarrami B, Khodayari N, Larijani B, Hasanzad M. Using ChatGPT to predict the future of personalized medicine. Pharmacogenomics J. 2023;23(6):178\u201384.","journal-title":"Pharmacogenomics J"},{"issue":"5","key":"10281_CR19","doi-asserted-by":"publisher","first-page":"5","DOI":"10.1109\/MIS.2023.3305861","volume":"38","author":"MM Amin","year":"2023","unstructured":"Amin MM, Cambria E, Schuller BW. Can ChatGPT\u2019s responses boost traditional natural language processing? IEEE Intell Syst. 2023;38(5):5\u201311.","journal-title":"IEEE Intell. Syst."},{"key":"10281_CR20","unstructured":"Chamola V, Bansal G, Das TK, Hassija V, Reddy NSS, Wang J, Zeadally S, Hussain A, Yu FR, Guizani M, et\u00a0al. Beyond reality: the pivotal role of generative AI in the metaverse. arXiv:2308.06272 [Preprint]. 2023. Available from: http:\/\/arxiv.org\/abs\/2308.06272."},{"key":"10281_CR21","doi-asserted-by":"publisher","first-page":"221","DOI":"10.1146\/annurev-bioeng-071516-044442","volume":"19","author":"D Shen","year":"2017","unstructured":"Shen D, Wu G, Suk H-I. Deep learning in medical image analysis. Annu Rev Biomed Eng. 2017;19:221\u201348.","journal-title":"Annu. Rev. Biomed. Eng."},{"key":"10281_CR22","doi-asserted-by":"publisher","first-page":"122","DOI":"10.1109\/ICRCICN.2018.8718718","volume-title":"2018 Fourth International Conference on Research in Computational Intelligence and Communication Networks (ICRCICN)","author":"F Sultana","year":"2018","unstructured":"Sultana F, Sufian A, Dutta P. Advancements in image classification using convolutional neural network. In: 2018 Fourth International Conference on Research in Computational Intelligence and Communication Networks (ICRCICN). IEEE; 2018. p. 122\u20139."},{"key":"10281_CR23","first-page":"367","volume":"2020","author":"P Dhruv","year":"2019","unstructured":"Dhruv P, Naskar S. Image classification using convolutional neural network (CNN) and recurrent neural network (RNN): a review. Machine Learning and Information Processing: Proceedings of ICMLIP. 2019;2020:367\u201381.","journal-title":"Machine Learning and Information Processing: Proceedings of ICMLIP"},{"key":"10281_CR24","first-page":"1877","volume":"33","author":"T Brown","year":"2020","unstructured":"Brown T, Mann B, Ryder N, Subbiah M, Kaplan JD, Dhariwal P, Neelakantan A, Shyam P, Sastry G, Askell A, et al. Language models are few-shot learners. Adv Neural Inf Process Syst. 2020;33:1877\u2013901.","journal-title":"Adv. Neural. Inf. Process. Syst."},{"issue":"1","key":"10281_CR25","doi-asserted-by":"publisher","first-page":"9","DOI":"10.1111\/j.1467-6494.2005.00368.x","volume":"74","author":"RS Lazarus","year":"2006","unstructured":"Lazarus RS. Emotions and interpersonal relationships: toward a person-centered conceptualization of emotions and coping. J Pers. 2006;74(1):9\u201346.","journal-title":"J. Pers."},{"key":"10281_CR26","doi-asserted-by":"publisher","first-page":"115","DOI":"10.3389\/fpsyg.2013.00115","volume":"4","author":"EA Elliott","year":"2013","unstructured":"Elliott EA, Jacobs AM. Facial expressions, emotions, and sign languages. Front Psychol. 2013;4:115.","journal-title":"Front. Psychol."},{"key":"10281_CR27","doi-asserted-by":"publisher","first-page":"106172","DOI":"10.1016\/j.knosys.2020.106172","volume":"204","author":"H Li","year":"2020","unstructured":"Li H, Xu H. Deep reinforcement learning for robust emotional classification in facial expression recognition. Knowl-Based Syst. 2020;204:106172.","journal-title":"Knowl.-Based Syst."},{"key":"10281_CR28","doi-asserted-by":"crossref","unstructured":"Sun C, Shrivastava A, Singh S, Gupta A. Revisiting unreasonable effectiveness of data in deep learning era. In: Proceedings of the IEEE International Conference on Computer Vision. 2017. p. 843\u201352.","DOI":"10.1109\/ICCV.2017.97"},{"issue":"1","key":"10281_CR29","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s40537-019-0197-0","volume":"6","author":"C Shorten","year":"2019","unstructured":"Shorten C, Khoshgoftaar TM. A survey on image data augmentation for deep learning. J Big Data. 2019;6(1):1\u201348.","journal-title":"J Big Data"},{"key":"10281_CR30","first-page":"656","volume-title":"Transfer learning for image classification","author":"M Shaha","year":"2018","unstructured":"Shaha M, Pawar M. 2018 Second International Conference on Electronics, Communication and Aerospace Technology (ICECA). In: Transfer learning for image classification. IEEE; 2018. p. 656\u201360."},{"key":"10281_CR31","doi-asserted-by":"publisher","first-page":"84","DOI":"10.1007\/978-3-030-01418-6_9","volume-title":"Artificial Neural Networks and Machine Learning\u2013ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part I 27","author":"Y Fan","year":"2018","unstructured":"Fan Y, Lam JC, Li VO. Multi-region ensemble convolutional neural network for facial expression recognition. In: Artificial Neural Networks and Machine Learning\u2013ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part I 27. Springer; 2018. p. 84\u201394."},{"issue":"11","key":"10281_CR32","doi-asserted-by":"publisher","first-page":"227","DOI":"10.3390\/a12110227","volume":"12","author":"Y Wang","year":"2019","unstructured":"Wang Y, Li Y, Song Y, Rong X. Facial expression recognition based on auxiliary models. Algorithms. 2019;12(11):227.","journal-title":"Algorithms"},{"key":"10281_CR33","unstructured":"Nord\u00e9n F, von Reis\u00a0Marlevi F. A comparative analysis of machine learning algorithms in binary facial expression recognition. 2019. http:\/\/www.diva-portal.org\/smash\/record.jsf?pid=diva2%3A1329976 &dswid=3676."},{"issue":"7","key":"10281_CR34","first-page":"1928","volume":"8","author":"JD Bodapati","year":"2019","unstructured":"Bodapati JD, Veeranjaneyulu N. Facial emotion recognition using deep CNN based features. Int J Innov Technol Explor Eng. 2019;8(7):1928\u201331.","journal-title":"Int J Innov Technol Explor Eng"},{"key":"10281_CR35","unstructured":"Ravi A. Pre-trained convolutional neural network features for facial expression recognition. arXiv:1812.06387 [Preprint]. Available from: http:\/\/arxiv.org\/abs\/1812.06387."},{"key":"10281_CR36","doi-asserted-by":"publisher","first-page":"35811","DOI":"10.1007\/s11042-020-09405-4","volume":"79","author":"M Rescigno","year":"2020","unstructured":"Rescigno M, Spezialetti M, Rossi S. Personalized models for facial emotion recognition through transfer learning. Multimed Tools Appl. 2020;79:35811\u201328.","journal-title":"Multimed Tools Appl"},{"key":"10281_CR37","doi-asserted-by":"crossref","unstructured":"Chowdary MK, Nguyen TN, Hemanth DJ. Deep learning-based facial emotion recognition for human\u2013computer interaction applications. Neural Comput Applic. 2021;1\u201318.","DOI":"10.1007\/s00521-021-06012-8"},{"key":"10281_CR38","doi-asserted-by":"publisher","first-page":"103834","DOI":"10.1016\/j.micpro.2021.103834","volume":"82","author":"D Lakshmi","year":"2021","unstructured":"Lakshmi D, Ponnusamy R. Facial emotion recognition using modified hog and LBP features with deep stacked autoencoders. Microprocess Microsyst. 2021;82:103834.","journal-title":"Microprocess. Microsyst."},{"key":"10281_CR39","doi-asserted-by":"publisher","first-page":"301","DOI":"10.1007\/978-981-16-1866-6_22","volume-title":"Mobile Computing and Sustainable Informatics: Proceedings of ICMCSI 2021","author":"S Mishra","year":"2022","unstructured":"Mishra S, Joshi B, Paudyal R, Chaulagain D, Shakya S. Deep residual learning for facial emotion recognition. In: Mobile Computing and Sustainable Informatics: Proceedings of ICMCSI 2021. Springer; 2022. p. 301\u201313."},{"issue":"12","key":"10281_CR40","doi-asserted-by":"publisher","first-page":"18669","DOI":"10.1007\/s11042-022-14186-z","volume":"82","author":"S Eluri","year":"2023","unstructured":"Eluri S. A novel leaky rectified triangle linear unit based deep convolutional neural network for facial emotion recognition. Multimed Tools Appl. 2023;82(12):18669\u201389.","journal-title":"Multimed Tools Appl"},{"key":"10281_CR41","doi-asserted-by":"publisher","first-page":"608","DOI":"10.1109\/LSP.2021.3065598","volume":"28","author":"S-Y Tseng","year":"2021","unstructured":"Tseng S-Y, Narayanan S, Georgiou P. Multimodal embeddings from language models for emotion recognition in the wild. IEEE Signal Process Lett. 2021;28:608\u201312.","journal-title":"IEEE Signal Process. Lett."},{"key":"10281_CR42","first-page":"1","volume-title":"2022 14th International Conference on Quality of Multimedia Experience (QoMEX)","author":"M Lammerse","year":"2022","unstructured":"Lammerse M, Hassan SZ, Sabet SS, Riegler MA, Halvorsen P. Human vs. GPT-3: the challenges of extracting emotions from child responses. In: 2022 14th International Conference on Quality of Multimedia Experience (QoMEX). IEEE; 2022. p. 1\u20134."},{"key":"10281_CR43","doi-asserted-by":"crossref","unstructured":"Elyoseph Z, Hadar-Shoval D, Asraf K, Lvovsky M. ChatGPT outperforms humans in emotional awareness evaluations. Front Psychol. 2023;14:1199058.","DOI":"10.3389\/fpsyg.2023.1199058"},{"key":"10281_CR44","unstructured":"Feng S, Sun G, Lubis N, Zhang C, Ga\u0161i\u0107 M. Affect recognition in conversations using large language models. arXiv:2309.12881 [Preprint]. 2023. Available from: http:\/\/arxiv.org\/abs\/2309.12881."},{"key":"10281_CR45","unstructured":"Lei S, Dong G, Wang X, Wang K, Wang S. InstructERC: reforming emotion recognition in conversation with a retrieval multi-task LLMS framework. arXiv:2309.11911 [Preprint]. 2023. Available from: http:\/\/arxiv.org\/abs\/2309.11911."},{"key":"10281_CR46","doi-asserted-by":"publisher","first-page":"117","DOI":"10.1007\/978-3-642-42051-1_16","volume-title":"Neural Information Processing: 20th International Conference, ICONIP 2013, Daegu, Korea, November 3-7, 2013. Proceedings, Part III 20","author":"IJ Goodfellow","year":"2013","unstructured":"Goodfellow IJ, Erhan D, Carrier PL, Courville A, Mirza M, Hamner B, Cukierski W, Tang Y, Thaler D, Lee D-H, et al. Challenges in representation learning: a report on three machine learning contests. In: Neural Information Processing: 20th International Conference, ICONIP 2013, Daegu, Korea, November 3-7, 2013. Proceedings, Part III 20. Springer; 2013. p. 117\u201324."},{"key":"10281_CR47","unstructured":"Simonyan K, Zisserman A. Very deep convolutional networks for large-scale image recognition. arXiv:1409.1556 [Preprint]. 2014. Available from: http:\/\/arxiv.org\/abs\/1409.1556."},{"key":"10281_CR48","doi-asserted-by":"crossref","unstructured":"He K, Zhang X, Ren S, Sun J. Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2016. p. 770\u20138.","DOI":"10.1109\/CVPR.2016.90"},{"key":"10281_CR49","unstructured":"Liu H, Li C, Wu Q, Lee YJ. Visual instruction tuning. Adv Neural Inf Process Syst. 2024;36."},{"key":"10281_CR50","first-page":"8748","volume-title":"International Conference on Machine Learning","author":"A Radford","year":"2021","unstructured":"Radford A, Kim JW, Hallacy C, Ramesh A, Goh G, Agarwal S, Sastry G, Askell A, Mishkin P, Clark J, et al. Learning transferable visual models from natural language supervision. In: International Conference on Machine Learning. PMLR; 2021. p. 8748\u201363."},{"key":"10281_CR51","unstructured":"Chiang W-L, Li Z, Lin Z, Sheng Y, Wu Z, Zhang H, Zheng L, Zhuang S, Zhuang Y, Gonzalez JE et\u00a0al. Vicuna: an open-source chatbot impressing GPT-4 with 90%* ChatGPT quality. 2023. https:\/\/vicuna.lmsys.org. Accessed 14 Apr 2023."},{"key":"10281_CR52","unstructured":"OpenAI. GPT-4 technical report. arXiv:2303.08774 [Preprint]. 2023. Available from: http:\/\/arxiv.org\/abs\/2303.08774."},{"key":"10281_CR53","unstructured":"Yosinski J, Clune J, Bengio Y, Lipson H. How transferable are features in deep neural networks? Adv Neural Inf Proces Syst. 2014;27."},{"issue":"2","key":"10281_CR54","doi-asserted-by":"publisher","first-page":"15","DOI":"10.1109\/MIS.2023.3254179","volume":"38","author":"MM Amin","year":"2023","unstructured":"Amin MM, Cambria E, Schuller BW. Will affective computing emerge from foundation models and general artificial intelligence? a first evaluation of ChatGPT. IEEE Intell Syst. 2023;38(2):15\u201323.","journal-title":"IEEE Intell. Syst."},{"issue":"6","key":"10281_CR55","doi-asserted-by":"publisher","first-page":"e1512","DOI":"10.1002\/widm.1512","volume":"13","author":"QM Areeb","year":"2023","unstructured":"Areeb QM, Nadeem M, Sohail SS, Imam R, Doctor F, Himeur Y, Hussain A, Amira A. Filter bubbles in recommender systems: fact or fallacy-a systematic review. Wiley Interdiscip Rev Data Min Knowl Discov. 2023;13(6):e1512.","journal-title":"Wiley Interdiscip Rev Data Min Knowl Discov"},{"issue":"1","key":"10281_CR56","doi-asserted-by":"publisher","first-page":"120","DOI":"10.1038\/s41746-023-00873-0","volume":"6","author":"B Mesk\u00f3","year":"2023","unstructured":"Mesk\u00f3 B, Topol EJ. The imperative for regulatory oversight of large language models (or generative AI) in healthcare. npj Digital Medicine. 2023;6(1):120.","journal-title":"npj Digital Medicine."}],"container-title":["Cognitive Computation"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s12559-024-10281-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s12559-024-10281-5\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s12559-024-10281-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,8,7]],"date-time":"2024-08-07T10:18:14Z","timestamp":1723025894000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s12559-024-10281-5"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,5,27]]},"references-count":56,"journal-issue":{"issue":"5","published-print":{"date-parts":[[2024,9]]}},"alternative-id":["10281"],"URL":"https:\/\/doi.org\/10.1007\/s12559-024-10281-5","relation":{},"ISSN":["1866-9956","1866-9964"],"issn-type":[{"value":"1866-9956","type":"print"},{"value":"1866-9964","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,5,27]]},"assertion":[{"value":"14 January 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"7 April 2024","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"27 May 2024","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"This article does not contain any studies with human participants or animals performed by any of the authors.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics Approval"}},{"value":"Informed consent was not required as no humans or animals were involved.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Informed Consent"}},{"value":"The authors declare no competing interests.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing Interests"}}]}}