{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,6]],"date-time":"2026-10-06T05:45:42Z","timestamp":1791265542273,"version":"4.1.0"},"reference-count":67,"publisher":"MDPI AG","issue":"14","license":[{"start":{"date-parts":[[2024,7,9]],"date-time":"2024-07-09T00:00:00Z","timestamp":1720483200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["JCM"],"abstract":"<jats:p>Background: Open-source artificial intelligence models (OSAIMs) are increasingly being applied in various fields, including IT and medicine, offering promising solutions for diagnostic and therapeutic interventions. In response to the growing interest in AI for clinical diagnostics, we evaluated several OSAIMs\u2014such as ChatGPT 4, Microsoft Copilot, Gemini, PopAi, You Chat, Claude, and the specialized PMC-LLaMA 13B\u2014assessing their abilities to classify scoliosis severity and recommend treatments based on radiological descriptions from AP radiographs. Methods: Our study employed a two-stage methodology, where descriptions of single-curve scoliosis were analyzed by AI models following their evaluation by two independent neurosurgeons. Statistical analysis involved the Shapiro\u2013Wilk test for normality, with non-normal distributions described using medians and interquartile ranges. Inter-rater reliability was assessed using Fleiss\u2019 kappa, and performance metrics, like accuracy, sensitivity, specificity, and F1 scores, were used to evaluate the AI systems\u2019 classification accuracy. Results: The analysis indicated that although some AI systems, like ChatGPT 4, Copilot, and PopAi, accurately reflected the recommended Cobb angle ranges for disease severity and treatment, others, such as Gemini and Claude, required further calibration. Particularly, PMC-LLaMA 13B expanded the classification range for moderate scoliosis, potentially influencing clinical decisions and delaying interventions. Conclusions: These findings highlight the need for the continuous refinement of AI models to enhance their clinical applicability.<\/jats:p>","DOI":"10.3390\/jcm13144013","type":"journal-article","created":{"date-parts":[[2024,7,10]],"date-time":"2024-07-10T09:23:08Z","timestamp":1720603388000},"page":"4013","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":17,"title":["Assessing the Accuracy of Artificial Intelligence Models in Scoliosis Classification and Suggested Therapeutic Approaches"],"prefix":"10.3390","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0998-4435","authenticated-orcid":false,"given":"Artur","family":"Fabijan","sequence":"first","affiliation":[{"name":"Department of Neurosurgery, Polish-Mother\u2019s Memorial Hospital Research Institute, 93-338 Lodz, Poland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4031-0930","authenticated-orcid":false,"given":"Agnieszka","family":"Zawadzka-Fabijan","sequence":"additional","affiliation":[{"name":"Department of Rehabilitation Medicine, Faculty of Health Sciences, Medical University of Lodz, 90-419 Lodz, Poland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Robert","family":"Fabijan","sequence":"additional","affiliation":[{"name":"Independent Researcher, Luton LU2 0GS, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Krzysztof","family":"Zakrzewski","sequence":"additional","affiliation":[{"name":"Department of Neurosurgery, Polish-Mother\u2019s Memorial Hospital Research Institute, 93-338 Lodz, Poland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Emilia","family":"Nowos\u0142awska","sequence":"additional","affiliation":[{"name":"Department of Neurosurgery, Polish-Mother\u2019s Memorial Hospital Research Institute, 93-338 Lodz, Poland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Bartosz","family":"Polis","sequence":"additional","affiliation":[{"name":"Department of Neurosurgery, Polish-Mother\u2019s Memorial Hospital Research Institute, 93-338 Lodz, Poland"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2024,7,9]]},"reference":[{"key":"ref_1","first-page":"e52485","article-title":"A Comparative Analysis of AI Models in Complex Medical Decision-Making Scenarios: Evaluating ChatGPT, Claude AI, Bard, and Perplexity","volume":"16","author":"Uppalapati","year":"2024","journal-title":"Cureus"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Zhang, H., Huang, C., Wang, D., Li, K., Han, X., Chen, X., and Li, Z. (2023). Artificial Intelligence in Scoliosis: Current Applications and Future Directions. J. Clin. Med., 12.","DOI":"10.3390\/jcm12237382"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Zong, H., Li, J., Wu, E., Wu, R., Lu, J., and Shen, B. (2024). Performance of ChatGPT on Chinese national medical licensing examinations: A five-year examination evaluation study for physicians, pharmacists and nurses. BMC Med. Educ., 24.","DOI":"10.1186\/s12909-024-05125-7"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"872","DOI":"10.1002\/jdd.13485","article-title":"Artificial intelligence: ChatGPT as a disruptive didactic strategy in dental education","volume":"88","year":"2024","journal-title":"J. Dent. Educ."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"e0367","DOI":"10.1097\/HC9.0000000000000367","article-title":"Artificial intelligence compared with human-derived patient educational materials on cirrhosis","volume":"8","author":"Pradhan","year":"2024","journal-title":"Hepatol. Commun."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"1412","DOI":"10.1038\/s41433-024-02958-w","article-title":"Google DeepMind\u2019s gemini AI versus ChatGPT: A comparative analysis in ophthalmology","volume":"38","author":"Masalkhi","year":"2024","journal-title":"Eye"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"3297","DOI":"10.1007\/s00405-024-08511-5","article-title":"Synergizing ChatGPT and general AI for enhanced medical diagnostic processes in head and neck imaging","volume":"281","author":"Maniaci","year":"2024","journal-title":"Eur. Arch. Otorhinolaryngol."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Morreel, S., Verhoeven, V., and Mathysen, D. (2024). Microsoft Bing outperforms five other generative artificial intelligence chatbots in the Antwerp University multiple choice medical license exam. PLoS Digit Health, 3.","DOI":"10.1371\/journal.pdig.0000349"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Zakka, C., Shad, R., Chaurasia, A., Dalal, A.R., Kim, J.L., Moor, M., Fong, R., Phillips, C., Alexander, K., and Ashley, E. (2024). Almanac\u2014Retrieval-Augmented Language Models for Clinical Medicine. NEJM AI, 1.","DOI":"10.1056\/AIoa2300068"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Aiumtrakul, N., Thongprayoon, C., Arayangkool, C., Vo, K.B., Wannaphut, C., Suppadungsuk, S., Krisanapan, P., Garcia Valencia, O.A., Qureshi, F., and Miao, J. (2024). Personalized Medicine in Urolithiasis: AI Chatbot-Assisted Dietary Management of Oxalate for Kidney Stone Prevention. J. Pers. Med., 14.","DOI":"10.3390\/jpm14010107"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"1054","DOI":"10.1016\/j.amepre.2024.02.006","article-title":"Accuracy of Online Artificial Intelligence Models in Primary Care Settings","volume":"66","author":"Kassab","year":"2024","journal-title":"Am. J. Prev. Med."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"465","DOI":"10.1007\/s10157-023-02451-w","article-title":"Performance of ChatGPT and Bard in self-assessment questions for nephrology board renewal","volume":"28","author":"Noda","year":"2024","journal-title":"Clin. Exp. Nephrol."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"e51391","DOI":"10.2196\/51391","article-title":"Learning to Make Rare and Complex Diagnoses with Generative AI Assistance: Qualitative Study of Popular Large Language Models","volume":"10","author":"Abdullahi","year":"2024","journal-title":"JMIR Med. Educ."},{"key":"ref_14","unstructured":"Google Gemini AI (2024, April 10). The Future of Artificial Intelligence. Available online: https:\/\/digitalfloats.com\/google-gemini-the-future-of-440artificial-intelligence\/."},{"key":"ref_15","unstructured":"(2024, April 10). Gemini. Available online: https:\/\/gemini.google.com\/advanced?utm_source=deepmind&utm_medium=owned&utm_cam-442paign=gdmsite_learn."},{"key":"ref_16","unstructured":"(2024, April 10). You Chat: What Is You Chat?. Available online: https:\/\/about.you.com\/introducing-youchat-the-ai-search-assistant-that-lives-in-your-search-engine-eff7badcd655\/."},{"key":"ref_17","first-page":"e51584","article-title":"Comparing Artificial Intelligence and Senior Residents in Oral Lesion Diagnosis: A Comparative Study","volume":"16","author":"Albagieh","year":"2024","journal-title":"Cureus"},{"key":"ref_18","unstructured":"(2024, April 10). Constitutional AI: Harmlessness from AI Feedback. Available online: https:\/\/www.anthropic.com\/news\/constitutional-ai-harmlessness-from-ai-feedback."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"104938","DOI":"10.1016\/j.jdent.2024.104938","article-title":"Accuracy and consistency of chatbots versus clinicians for answering pediatric dentistry questions: A pilot study","volume":"144","author":"Rokhshad","year":"2024","journal-title":"J. Dent."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"e53043","DOI":"10.2196\/53043","article-title":"Comparing the Perspectives of Generative AI, Mental Health Experts, and the General Public on Schizophrenia Recovery: Case Vignette Study","volume":"11","author":"Elyoseph","year":"2024","journal-title":"JMIR Ment. Health"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"139","DOI":"10.1302\/2633-1462.52.BJO-2023-0113.R1","article-title":"Is ChatGPT a trusted source of information for total hip and knee arthroplasty patients?","volume":"5","author":"Wright","year":"2024","journal-title":"Bone Jt. Open"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Wu, C., Lin, W., Zhang, X., Zhang, Y., Xie, W., and Wang, Y. (2024). PMC-LLaMA: Toward building open-source language models for medicine. J. Am. Med. Inform. Assoc., ocae045.","DOI":"10.1093\/jamia\/ocae045"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"904","DOI":"10.3171\/2023.2.JNS23419","article-title":"ChatGPT versus the neurosurgical written boards: A comparative analysis of artificial intelligence\/machine learning performance on neurosurgical board-style questions","volume":"139","author":"Hopkins","year":"2023","journal-title":"J. Neurosurg."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"e38445","DOI":"10.1097\/MD.0000000000038445","article-title":"The assessment of the validity, safety, and utility of ChatGPT for patients with herniated lumbar disc: A preliminary study","volume":"103","author":"Yang","year":"2024","journal-title":"Medicine"},{"key":"ref_25","first-page":"e43502","article-title":"The Expanding Role of ChatGPT (Chat-Generative Pre-Trained Transformer) in Neurosurgery: A Systematic Review of Literature and Conceptual Framework","volume":"15","author":"Roman","year":"2023","journal-title":"Cureus"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Maharathi, S., Iyengar, R., and Chandrasekhar, P. (2023). Biomechanically designed Curve Specific Corrective Exercise for Adolescent Idiopathic Scoliosis gives significant outcomes in an Adult: A case report. Front. Rehabil. Sci., 4.","DOI":"10.3389\/fresc.2023.1127222"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"108","DOI":"10.1016\/j.compbiomed.2016.01.026","article-title":"Improvement of automated image stitching system for DR X-ray images","volume":"71","author":"Yang","year":"2016","journal-title":"Comput. Biol. Med."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"99","DOI":"10.1016\/j.ejrad.2018.09.014","article-title":"Radiation dose for pediatric scoliosis patients undergoing whole spine radiography: Effect of the radiographic length in an auto-stitching digital radiography system","volume":"108","author":"Hwang","year":"2018","journal-title":"Eur. J. Radiol."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"1704","DOI":"10.1097\/BRS.0000000000003670","article-title":"Risk Factors Predicting C-Versus S-shaped Sagittal Spine Profiles in Natural, Relaxed Sitting: An Important Aspect in Spinal Realignment Surgery","volume":"45","author":"Hey","year":"2020","journal-title":"Spine"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"6357171","DOI":"10.1155\/2019\/6357171","article-title":"Cobb Angle Measurement of Spine from X-Ray Images Using Convolutional Neural Network","volume":"2019","author":"Horng","year":"2019","journal-title":"Comput. Math. Methods Med."},{"key":"ref_31","unstructured":"(2024, April 10). Patient Examination AO Surgery Reference. Available online: https:\/\/surgeryreference.aofoundation.org\/spine\/deformities\/adolescent-idiopathic-scoliosis\/further-reading\/patient-examination."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Fabijan, A., Polis, B., Fabijan, R., Zakrzewski, K., Nowos\u0142awska, E., and Zawadzka-Fabijan, A. (2023). Artificial Intelligence in Scoliosis Classification: An Investigation of Language-Based Models. J. Pers. Med., 13.","DOI":"10.3390\/jpm13121695"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Fabijan, A., Fabijan, R., Zawadzka-Fabijan, A., Nowos\u0142awska, E., Zakrzewski, K., and Polis, B. (2023). Evaluating Scoliosis Severity Based on Posturographic X-ray Images Using a Contrastive Language\u2013Image Pretraining Model. Diagnostics, 13.","DOI":"10.3390\/diagnostics13132142"},{"key":"ref_34","unstructured":"Touvron, H., Lavril, T., Izacard, G., Martinet, X., Lachaux, M.A., Lacroix, T., Rozi\u00e8re, B., Goyal, N., Hambro, E., and Azhar, F. (2023). LLaMA: Open and Efficient Foundation Language Models. arXiv."},{"key":"ref_35","unstructured":"(2024, April 10). Llama-Precise Parameters Guidelines. Available online: https:\/\/github.com\/oobabooga\/text-generation-webui\/wiki\/03-%E2%80%90-Parameters-Tab."},{"key":"ref_36","unstructured":"(2024, April 10). PMC-LLaMA Prompt Schema. Available online: https:\/\/github.com\/chaoyi-wu\/pmc-llama."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"322","DOI":"10.1037\/0033-2909.88.2.322","article-title":"Integration and generalization of kappas for multiple raters","volume":"88","author":"Conger","year":"1980","journal-title":"Psychol. Bull."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"378","DOI":"10.1037\/h0031619","article-title":"Measuring nominal scale agreement among many raters","volume":"76","author":"Fleiss","year":"1971","journal-title":"Psychol. Bull."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Fleiss, J.L., Levin, B., and Paik, M.C. (2003). Statistical Methods for Rates and Proportions, John Wiley & Sons. [3rd ed.].","DOI":"10.1002\/0471445428"},{"key":"ref_40","unstructured":"R Core Team (2021). R: A Language and Environment for Statistical Computing, R Foundation for Statistical Computing. Available online: https:\/\/www.R-project.org\/."},{"key":"ref_41","unstructured":"Gamer, M., Lemon, J., and Singh, P. (2024, April 10). irr: Various Coefficients of Interrater Reliability and Agreement, R Package Version 0.84.1, irr. Available online: https:\/\/CRAN.R-project.org\/package=irr."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"1","DOI":"10.18637\/jss.v028.i05","article-title":"Building Predictive Models in R Using the caret Package","volume":"28","author":"Kuhn","year":"2008","journal-title":"J. Stat. Softw."},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Sarkar, D. (2008). lattice: Multivariate Data Visualization with R, Springer.","DOI":"10.1007\/978-0-387-75969-2"},{"key":"ref_44","unstructured":"Makowski, D., L\u00fcdecke, D., Patil, I., Th\u00e9riault, R., Ben-Shachar, M., and Wiernik, B. (2024, April 10). Automated Results Reporting as a Practical Tool to Improve Reproducibility and Methodological Best Practices Adoption. CRAN, Available online: https:\/\/easystats.github.io\/report\/."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"570","DOI":"10.32614\/RJ-2021-053","article-title":"Reproducible Summary Tables with the gtsummary Package","volume":"13","author":"Sjoberg","year":"2021","journal-title":"R J."},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Wickham, H. (2016). ggplot2: Elegant Graphics for Data Analysis, Springer.","DOI":"10.1007\/978-3-319-24277-4_9"},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Chung, H., Park, C., Kang, W.S., and Lee, J. (2021). Gender Bias in Artificial Intelligence: Severity Prediction at an Early Stage of COVID-19. Front. Physiol., 12.","DOI":"10.3389\/fphys.2021.778720"},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Liu, J., Sun, J., and Zhou, X. (2022, January 11\u201313). Comparison of ResNet-50 and vision transformer models for trash classification. Proceedings of the Third International Conference on Artificial Intelligence and Computer Engineering (ICAICE 2022), Wuhan, China.","DOI":"10.1117\/12.2671208"},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"1093","DOI":"10.1016\/j.ins.2022.06.091","article-title":"Deconv-transformer (DecT): A histopathological image classification model for breast cancer based on color deconvolution and transformer architecture","volume":"608","author":"He","year":"2022","journal-title":"Inf. Sci."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"88","DOI":"10.54254\/2755-2721\/20\/20231079","article-title":"Transformer model: Explainability and prospectiveness","volume":"20","author":"Nuobu","year":"2023","journal-title":"Appl. Comput. Eng."},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Kanca, E., Ayas, S., Kablan, E.B., and Ekinci, M. (2023, January 5\u20138). Performance Comparison of Vision Transformer-Based Models in Medical Image Classification. Proceedings of the 2023 31st Signal Processing and Communications Applications Conference (SIU), Istanbul, Turkiye.","DOI":"10.1109\/SIU59756.2023.10223892"},{"key":"ref_52","unstructured":"Niu, X., Bai, B., Deng, L., and Han, W. (2024). Beyond Scaling Laws: Understanding Transformer Performance with Associative Memory. arXiv."},{"key":"ref_53","unstructured":"Gholami, S., and Omar, M. (2023). Do Generative Large Language Models need billions of parameters?. arXiv, 6589v1."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"100183","DOI":"10.1016\/j.caeai.2023.100183","article-title":"Performance of ChatGPT on the US Fundamentals of Engineering Exam: Comprehensive Assessment of Proficiency and Potential Implications for Professional Environmental Engineering Practice","volume":"5","author":"Pursnani","year":"2023","journal-title":"Comput. Educ. Artif. Intell."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"198","DOI":"10.3390\/ime2030019","article-title":"Prompt Engineering in Medical Education","volume":"2","author":"Heston","year":"2023","journal-title":"Int. Med. Educ."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"470","DOI":"10.3390\/geotechnics4020026","article-title":"An Investigation into the Utility of Large Language Models in Geotechnical Education and Problem Solving","volume":"4","author":"Chen","year":"2024","journal-title":"Geotechnics"},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"1356","DOI":"10.1093\/jamia\/ocae039","article-title":"Empowering Personalized Pharmacogenomics with Generative AI Solutions","volume":"31","author":"Murugan","year":"2024","journal-title":"J. Am. Med. Inform. Assoc."},{"key":"ref_58","doi-asserted-by":"crossref","unstructured":"Feijen, M., Egorova, A.D., Beeres, S.L.M.A., and Treskes, R.W. (2021). Early Detection of Fluid Retention in Patients with Advanced Heart Failure: A Review of a Novel Multisensory Algorithm, HeartLogicTM. Sensors, 21.","DOI":"10.3390\/s21041361"},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"102041","DOI":"10.1016\/j.eclinm.2023.102041","article-title":"A panel of seven protein tumour markers for effective and affordable multi-cancer early detection by artificial intelligence: A large-scale and multicentre case-control study","volume":"61","author":"Luan","year":"2023","journal-title":"EClinicalMedicine"},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"eabo4802","DOI":"10.1126\/scitranslmed.abo4802","article-title":"Improving breast cancer diagnostics with deep learning for MRI","volume":"14","author":"Witowski","year":"2022","journal-title":"Sci. Transl. Med."},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"1802","DOI":"10.1093\/ndt\/gfu211","article-title":"Optimal and early detection of acute kidney injury requires effective clinical decision support systems","volume":"29","author":"Handler","year":"2014","journal-title":"Nephrol. Dial. Transplant."},{"key":"ref_62","doi-asserted-by":"crossref","unstructured":"Ahmed, S., Singh, M., Doherty, B., Ramlan, E., Harkin, K., and Coyle, D. (2022, January 26\u201327). Multiple Severity-Level Classifications for IT Incident Risk Prediction. Proceedings of the 2022 9th International Conference on Soft Computing & Machine Intelligence (ISCMI), Toronto, ON, Canada.","DOI":"10.1109\/ISCMI56532.2022.10068477"},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"6102","DOI":"10.3934\/mbe.2022285","article-title":"A new hybrid ensemble machine-learning model for severity risk assessment and post-COVID prediction system","volume":"19","author":"Shakhovska","year":"2022","journal-title":"Math. Biosci. Eng."},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"A15535","DOI":"10.1161\/circ.142.suppl_3.15535","article-title":"Abstract 15535: Automated Artificial Intelligence-based Myocardial Scar Quantification for Risk Assessment Following Myocardial Infarction","volume":"142","author":"Lange","year":"2020","journal-title":"Circulation"},{"key":"ref_65","doi-asserted-by":"crossref","unstructured":"Zhu, S., Wang, K., and Li, C. (2021). Crash Injury Severity Prediction Using an Ordinal Classification Machine Learning Approach. Int. J. Environ. Res. Public Health, 18.","DOI":"10.3390\/ijerph182111564"},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"2320","DOI":"10.1007\/s00464-024-10807-w","article-title":"The performance of artificial intelligence large language model-linked chatbots in surgical decision-making for gastroesophageal reflux disease","volume":"38","author":"Huo","year":"2024","journal-title":"Surg. Endosc."},{"key":"ref_67","first-page":"e59857","article-title":"Accuracy and Repeatability of ChatGPT Based on a Set of Multiple-Choice Questions on Objective Tests of Hearing","volume":"16","author":"Kochanek","year":"2024","journal-title":"Cureus"}],"container-title":["Journal of Clinical Medicine"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2077-0383\/13\/14\/4013\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T15:12:28Z","timestamp":1760109148000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2077-0383\/13\/14\/4013"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,7,9]]},"references-count":67,"journal-issue":{"issue":"14","published-online":{"date-parts":[[2024,7]]}},"alternative-id":["jcm13144013"],"URL":"https:\/\/doi.org\/10.3390\/jcm13144013","relation":{},"ISSN":["2077-0383"],"issn-type":[{"value":"2077-0383","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,7,9]]}}}