{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,14]],"date-time":"2026-10-14T11:06:42Z","timestamp":1791976002158,"version":"4.4.0"},"reference-count":46,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2024,4,5]],"date-time":"2024-04-05T00:00:00Z","timestamp":1712275200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Diagnostics"],"abstract":"<jats:p>Open-source artificial intelligence models (OSAIM) find free applications in various industries, including information technology and medicine. Their clinical potential, especially in supporting diagnosis and therapy, is the subject of increasingly intensive research. Due to the growing interest in artificial intelligence (AI) for diagnostic purposes, we conducted a study evaluating the capabilities of AI models, including ChatGPT and Microsoft Bing, in the diagnosis of single-curve scoliosis based on posturographic radiological images. Two independent neurosurgeons assessed the degree of spinal deformation, selecting 23 cases of severe single-curve scoliosis. Each posturographic image was separately implemented onto each of the mentioned platforms using a set of formulated questions, starting from \u2018What do you see in the image?\u2019 and ending with a request to determine the Cobb angle. In the responses, we focused on how these AI models identify and interpret spinal deformations and how accurately they recognize the direction and type of scoliosis as well as vertebral rotation. The Intraclass Correlation Coefficient (ICC) with a \u2018two-way\u2019 model was used to assess the consistency of Cobb angle measurements, and its confidence intervals were determined using the F test. Differences in Cobb angle measurements between human assessments and the AI ChatGPT model were analyzed using metrics such as RMSEA, MSE, MPE, MAE, RMSLE, and MAPE, allowing for a comprehensive assessment of AI model performance from various statistical perspectives. The ChatGPT model achieved 100% effectiveness in detecting scoliosis in X-ray images, while the Bing model did not detect any scoliosis. However, ChatGPT had limited effectiveness (43.5%) in assessing Cobb angles, showing significant inaccuracy and discrepancy compared to human assessments. This model also had limited accuracy in determining the direction of spinal curvature, classifying the type of scoliosis, and detecting vertebral rotation. Overall, although ChatGPT demonstrated potential in detecting scoliosis, its abilities in assessing Cobb angles and other parameters were limited and inconsistent with expert assessments. These results underscore the need for comprehensive improvement of AI algorithms, including broader training with diverse X-ray images and advanced image processing techniques, before they can be considered as auxiliary in diagnosing scoliosis by specialists.<\/jats:p>","DOI":"10.3390\/diagnostics14070773","type":"journal-article","created":{"date-parts":[[2024,4,5]],"date-time":"2024-04-05T08:27:52Z","timestamp":1712305672000},"page":"773","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":18,"title":["Artificial Intelligence in Medical Imaging: Analyzing the Performance of ChatGPT and Microsoft Bing in Scoliosis Detection and Cobb Angle Assessment"],"prefix":"10.3390","volume":"14","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":[[2024,4,5]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"120","DOI":"10.1007\/s12032-022-01711-1","article-title":"Artificial intelligence and machine learning in precision and genomic medicine","volume":"39","author":"Quazi","year":"2022","journal-title":"Med. Oncol."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"190","DOI":"10.1016\/j.jebdp.2016.07.002","article-title":"Artificial intelligence expert systems with neural network machine learning may assist decision-making for extractions in orthodontic treatment planning","volume":"16","author":"Takada","year":"2016","journal-title":"J. Evid. Based Dent. Pract."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"baaa010","DOI":"10.1093\/database\/baaa010","article-title":"Artificial intelligence with multi-functional machine learning platform development for better healthcare and precision medicine","volume":"2020","author":"Ahmed","year":"2020","journal-title":"Database"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"524","DOI":"10.1213\/ANE.0000000000006047","article-title":"Artificial Intelligence and Machine Learning in Patient Blood Management: A Scoping Review","volume":"135","author":"Meier","year":"2022","journal-title":"Anesth. Analg."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"5975","DOI":"10.1038\/s41598-022-10066-6","article-title":"Rapid, automated nerve histomorphometry through open-source artificial intelligence","volume":"12","author":"Daeschler","year":"2022","journal-title":"Sci. Rep."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"976235","DOI":"10.3389\/frai.2022.976235","article-title":"CLIP knows image aesthetics","volume":"5","author":"Hentschel","year":"2022","journal-title":"Front. Artif. Intell."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Kurt, U., and \u00c7ayir, A. (2023, January 13\u201315). A Modern Turkish Poet: Fine-Tuned GPT-2. Proceedings of the 8th International Conference on Computer Science and Engineering (UBMK), Burdur, Turkiye.","DOI":"10.1109\/UBMK59864.2023.10286720"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Paik, I., and Wang, J.W. (2021). Improving Text-to-Code Generation with Features of Code Graph on GPT-2. Electronics, 10.","DOI":"10.3390\/electronics10212706"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"e32875","DOI":"10.2196\/32875","article-title":"Operationalizing and Implementing Pretrained, Large Artificial Intelligence Linguistic Models in the US Health Care System: Outlook of Generative Pretrained Transformer 3 (GPT-3) as a Service Model","volume":"10","author":"Sezgin","year":"2022","journal-title":"JMIR Med. Inform."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"210","DOI":"10.1038\/s41746-023-00958-w","article-title":"A study of generative large language model for medical research and healthcare","volume":"6","author":"Peng","year":"2023","journal-title":"NPJ Digit. Med."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"257","DOI":"10.18421\/SAR64-05","article-title":"Balancing the Equation: Investigating AI Advantages, Challenges, and Ethical Considerations in the Context of GPT-3, Natural Language Processing, and Researcher Roles","volume":"6","author":"Lubis","year":"2023","journal-title":"SAR J.-Sci. Res."},{"key":"ref_12","first-page":"e43958","article-title":"Artificial Intelligence (AI) in Radiology: A Deep Dive Into ChatGPT 4.0\u2019s Accuracy with the American Journal of Neuroradiology\u2019s (AJNR) \u201cCase of the Month\u201d","volume":"15","author":"Suthar","year":"2023","journal-title":"Cureus"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Lee, K.H., Lee, R.W., and Kwon, Y.E. (2024). Validation of a Deep Learning Chest X-ray Interpretation Model: Integrating Large-Scale AI and Large Language Models for Comparative Analysis with ChatGPT. Diagnostics, 14.","DOI":"10.3390\/diagnostics14010090"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"1131","DOI":"10.1007\/s10439-023-03335-6","article-title":"A Promising Start and Not a Panacea: ChatGPT\u2019s Early Impact and Potential in Medical Science and Biomedical Engineering Research","volume":"52","author":"Sohail","year":"2023","journal-title":"Ann. Biomed. Eng."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Doshi, R., Amin, K., Khosla, P., Bajaj, S., Chheang, S., and Forman, H.P. (2023). Utilizing Large Language Models to Simplify Radiology Reports: A comparative analysis of ChatGPT3. 5, ChatGPT4. 0, Google Bard, and Microsoft Bing. medRxiv.","DOI":"10.1101\/2023.06.04.23290786"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Bhardwaz, S., and Kumar, J. (2023, January 4\u20136). An Extensive Comparative Analysis of Chatbot Technologies\u2014ChatGPT, Google BARD and Microsoft Bing. Proceedings of the 2nd International Conference on Applied Artificial Intelligence and Computing (ICAAIC), Salem, India.","DOI":"10.1109\/ICAAIC56838.2023.10140214"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Imran, A.A.Z., Huang, C., Tang, H., Fan, W., Cheung, K., To, M., Qian, Z., and Terzopoulos, D. (2020). Analysis of Scoliosis from Spinal X-Ray Images. arXiv.","DOI":"10.1109\/CBMS49503.2020.00029"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Chen, P., Zhou, Z., Yu, H., Chen, K., and Yang, Y. (2022). Computerized-Assisted Scoliosis Diagnosis Based on Faster R-CNN and ResNet for the Classification of Spine X-Ray Images. Comput. Math. Methods Med., 2022.","DOI":"10.1155\/2022\/3796202"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Roy, S., Gr\u00fcnwald, A.T., Alves-Pinto, A., Maier, R., Cremers, D., Pfeiffer, D., and Lampe, R. (2019). A Noninvasive 3D Body Scanner and Software Tool towards Analysis of Scoliosis. Biomed. Res. Int., 2019.","DOI":"10.1155\/2019\/4715720"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"29","DOI":"10.21037\/jmai-23-94","article-title":"GPT-4 and medical image analysis: Strengths, weaknesses and future directions","volume":"6","author":"Waisberg","year":"2023","journal-title":"J. Med. Artif. Intell."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Brin, D., Sorin, V., Barash, Y., Konen, E., Glicksberg, B.S., Nadkarni, G., and Klang, E. (2023). Assessing GPT-4 Multimodal Performance in Radiological Image Analysis. medRxiv.","DOI":"10.1101\/2023.11.15.23298583"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"105292","DOI":"10.1016\/j.bspc.2023.105292","article-title":"Exploring the role of ChatGPT in medical image analysis","volume":"86","author":"Handa","year":"2023","journal-title":"Biomed. Signal Process."},{"key":"ref_23","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_24","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_25","doi-asserted-by":"crossref","first-page":"1127222","DOI":"10.3389\/fresc.2023.1127222","article-title":"Biomechanically designed Curve Specific Corrective Exercise for Adolescent Idiopathic Scoliosis gives significant outcomes in an Adult: A case report","volume":"4","author":"Maharathi","year":"2023","journal-title":"Front. Rehabil. Sci."},{"key":"ref_26","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_27","first-page":"19","article-title":"Adolescent Idiopathic Scoliosis: Common Questions and Answers","volume":"101","author":"Kuznia","year":"2020","journal-title":"Am. Fam. Physician."},{"key":"ref_28","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_29","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_30","unstructured":"Gamer, M., and Lemon, J. (2023, December 29). Various Coefficients of Interrater Reliability and Agreement, Available online: https:\/\/CRAN.R-project.org\/package=irr."},{"key":"ref_31","unstructured":"Makowski, D., L\u00fcdecke, D., Patil, I., Th\u00e9riault, R., Ben-Shachar, M., and Wiernik, B. (2023, December 29). Automated Results Reporting as a Practical Tool to Improve Reproducibility and Methodological Best Practices Adoption, Available online: https:\/\/easystats.github.io\/report\/."},{"key":"ref_32","unstructured":"Wickham, H., Fran\u00e7ois, R., Henry, L., M\u00fcller, K., and Vaughan, D. (2023, December 29). dplyr: A Grammar of Data Manipulation, Available online: https:\/\/CRAN.R-project.org\/package=dplyr."},{"key":"ref_33","unstructured":"Revelle, W.R. (2023, December 29). psych: Procedures for Psychological, Psychometric, and Personality Research, Available online: https:\/\/CRAN.R-project.org\/package=psych."},{"key":"ref_34","first-page":"18","article-title":"Exploring the capabilities and limitations of GPT and Chat GPT in natural language processing","volume":"10","author":"Thakkar","year":"2023","journal-title":"J. Med. Res. Anal."},{"key":"ref_35","first-page":"5","article-title":"ChatGPT: Where Is a Silver Lining? Exploring the realm of GPT and large language models","volume":"9","author":"Tikhonova","year":"2023","journal-title":"J. Lang. Educ."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Alzahem, A., Latif, S., Boulila, W., and Koub\u00e2a, A. (2023). Unlocking the Potential of Medical Imaging with ChatGPT\u2019s Intelligent Diagnostics. arXiv.","DOI":"10.1016\/j.procs.2023.10.496"},{"key":"ref_37","unstructured":"Nagarajan, V. (2021). Explaining generalization in deep learning: Progress and fundamental limits. arXiv."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Michail, A., Konstantinou, S., and Clematide, S. (2023). UZH_CLyp at SemEval-2023 Task 9: Head-First Fine-Tuning and ChatGPT Data Generation for Cross-Lingual Learning in Tweet Intimacy Prediction. arXiv.","DOI":"10.18653\/v1\/2023.semeval-1.140"},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Artsi, Y., Sorin, M.D.V., Konen, M.D.E., Glicksberg, B.S., Nadkarni, M.D.G., and Klang, M.D.E. (medRxiv, 2024). Large language models in simplifying radiological reports: Systematic review, medRxiv.","DOI":"10.1101\/2024.01.05.24300884"},{"key":"ref_40","unstructured":"(2024, January 05). Microsoft Bing\u2014How Bing Delivers Search Results. Available online: https:\/\/support.microsoft.com\/en-us\/topic\/how-bing-delivers-search-results-d18fc815-ac37-4723-bc67-9229ce3eb6a3."},{"key":"ref_41","unstructured":"(2024, January 05). Microsoft Bing\u2014Natural Language Processing Technology. Available online: https:\/\/learn.microsoft.com\/en-us\/azure\/architecture\/data-guide\/technology-choices\/natural-language-processing."},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Al-Karawi, D., Al-Zaidi, S., Polus, N., and Jassim, S. (2020). AI based Chest X-Ray (CXR) Scan Texture Analysis Algorithm for Digital Test of COVID-19 Patients. medRxiv.","DOI":"10.1101\/2020.05.05.20091561"},{"key":"ref_43","unstructured":"Yang, Y., Zhang, H., Gichoya, J.W., Katabi, D., and Ghassemi, M. (2023). The Limits of Fair Medical Imaging AI In The Wild. arXiv."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"955765","DOI":"10.3389\/fmed.2022.955765","article-title":"AI-CenterNet CXR: An artificial intelligence (AI) enabled system for localization and classification of chest X-ray disease","volume":"9","author":"Albahli","year":"2022","journal-title":"Front Med."},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Song, S.Y., Seo, M.S., Kim, C.W., Kim, Y.H., Yoo, B.C., Choi, H.J., Seo, S.H., Kang, S.W., Song, M.G., and Nam, D.C. (2023). AI-Driven Segmentation and Automated Analysis of the Whole Sagittal Spine from X-ray Images for Spinopelvic Parameter Evaluation. Bioengineering, 10.","DOI":"10.3390\/bioengineering10101229"},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Williams, D., Ward, M., Kelly, E., Shillabeer, D., Williams, J., Javadi, A., Holsgrove, T., Meakin, J., and Holt, C. (2023, January 25\u201326). BORS\/BJR travelling fellowship: Image-driven subject-specific spine models: Developing a novel tool to measure in vivo loading. Proceedings of the The British Orthopaedic Research Society (BORS) 2023 Meeting, Cambridge, UK.","DOI":"10.1302\/1358-992X.2023.16.050"}],"container-title":["Diagnostics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2075-4418\/14\/7\/773\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T14:23:54Z","timestamp":1760106234000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2075-4418\/14\/7\/773"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,4,5]]},"references-count":46,"journal-issue":{"issue":"7","published-online":{"date-parts":[[2024,4]]}},"alternative-id":["diagnostics14070773"],"URL":"https:\/\/doi.org\/10.3390\/diagnostics14070773","relation":{},"ISSN":["2075-4418"],"issn-type":[{"value":"2075-4418","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,4,5]]}}}