Open-source artificial intelligence models are finding free application in various industries, including computer science and medicine. Their clinical potential, especially in assisting diagnosis and therapy, is the subject of increasingly intensive research. Due to the growing interest in AI for diagnostics, we conducted a study evaluating the abilities of AI models, including ChatGPT, Microsoft Bing, and Scholar AI, in classifying single-curve scoliosis based on radiological descriptions. Fifty-six posturographic images depicting single-curve scoliosis were selected and assessed by two independent neurosurgery specialists, who classified them as mild, moderate, or severe based on Cobb angles. Subsequently, descriptions were developed that accurately characterized the degree of spinal deformation, based on the measured values of Cobb angles. These descriptions were then provided to AI language models to assess their proficiency in diagnosing spinal pathologies. The artificial intelligence models conducted classification using the provided data. Our study also focused on identifying specific sources of information and criteria applied in their decision-making algorithms, aiming for a deeper understanding of the determinants influencing AI decision processes in scoliosis classification. The classification quality of the predictions was evaluated using performance evaluation metrics such as sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), accuracy, and balanced accuracy. Our study strongly supported our hypothesis, showing that among four AI models, ChatGPT 4 and Scholar AI Premium excelled in classifying single-curve scoliosis with perfect sensitivity and specificity. These models demonstrated unmatched rater concordance and excellent performance metrics. In comparing real and AI-generated scoliosis classifications, they showed impeccable precision in all posturographic images, indicating total accuracy (1.0, MAE = 0.0) and remarkable inter-rater agreement, with a perfect Fleiss’ Kappa score. This was consistent across scoliosis cases with a Cobb’s angle range of 11–92 degrees. Despite high accuracy in classification, each model used an incorrect angular range for the mild stage of scoliosis. Our findings highlight the immense potential of AI in analyzing medical data sets. However, the diversity in competencies of AI models indicates the need for their further development to more effectively meet specific needs in clinical practice.

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—such as ChatGPT 4, Microsoft Copilot, Gemini, PopAi, You Chat, Claude, and the specialized PMC-LLaMA 13B—assessing 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–Wilk test for normality, with non-normal distributions described using medians and interquartile ranges. Inter-rater reliability was assessed using Fleiss’ kappa, and performance metrics, like accuracy, sensitivity, specificity, and F1 scores, were used to evaluate the AI systems’ 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.

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 ‘What do you see in the image?’ 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 ‘two-way’ 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.

Assessing severe scoliosis requires the analysis of posturographic X-ray images. One way to analyse these images may involve the use of open-source artificial intelligence models (OSAIMs), such as the contrastive language–image pretraining (CLIP) system, which was designed to combine images with text. This study aims to determine whether the CLIP model can recognise visible severe scoliosis in posturographic X-ray images. This study used 23 posturographic images of patients diagnosed with severe scoliosis that were evaluated by two independent neurosurgery specialists. Subsequently, the X-ray images were input into the CLIP system, where they were subjected to a series of questions with varying levels of difficulty and comprehension. The predictions obtained using the CLIP models in the form of probabilities ranging from 0 to 1 were compared with the actual data. To evaluate the quality of image recognition, true positives, false negatives, and sensitivity were determined. The results of this study show that the CLIP system can perform a basic assessment of X-ray images showing visible severe scoliosis with a high level of sensitivity. It can be assumed that, in the future, OSAIMs dedicated to image analysis may become commonly used to assess X-ray images, including those of scoliosis.

Background/Objectives: Open-source artificial intelligence models (OSAIMs), such as BiomedCLIP, hold great potential for medical image analysis. While OSAIMs are increasingly utilized for general image interpretation, their adaptation for specialized medical tasks, such as evaluating scoliosis on posturographic X-ray images, is still developing. This study aims to evaluate the effectiveness of BiomedCLIP in detecting and classifying scoliosis types (single-curve and double-curve) and in assessing scoliosis severity. Methods: The study was conducted using a dataset of 262 anonymized posturographic X-ray images from pediatric patients (ages 2–17) with diagnosed scoliosis. The images were collected between January 2021 and July 2024. Two neurosurgical experts manually analyzed the Cobb angles and scoliosis stages (mild, moderate, severe). BiomedCLIP’s performance in detecting scoliosis and its type was evaluated using metrics such as accuracy, sensitivity, specificity, and AUC (Area Under the Curve). Statistical analyses, including Pearson correlation and ROC curve analysis, were applied to assess the model’s performance. Results: BiomedCLIP demonstrated moderate sensitivity in detecting scoliosis, with stronger performance in severe cases (AUC = 0.87). However, its predictive accuracy was lower for mild and moderate stages (AUC = 0.75 and 0.74, respectively). The model struggled with correctly identifying single-curve scoliosis (sensitivity = 0.35, AUC = 0.53), while it performed better in recognizing double-curve cases (sensitivity = 0.78, AUC = 0.53). Overall, the model’s predictions correlated moderately with observed Cobb angles (r = 0.37, p < 0.001). Conclusions: BiomedCLIP shows promise in identifying advanced scoliosis, but its performance is limited in early-stage detection and in distinguishing between scoliosis types, particularly single-curve scoliosis. Further model refinement and broader training datasets are essential to enhance its clinical applicability in scoliosis assessment.

Primary intracranial melanoma is a very rare brain tumor, especially when accompanied by benign intramedullary melanocytoma. Distinguishing between a primary central nervous system (CNS) lesion and metastatic melanoma is extremely difficult, especially when the primary cutaneous lesion is not visible. Here we report a 13-year-old girl admitted to the Neurosurgery Department of the Institute of Polish Mother’s Health Centre in Lodz due to upper limb paresis. An intramedullary tumor of the cervical C3–C4 and an accompanying syringomyelic cavity C1–C7 were revealed. The child underwent partial removal of the tumor due to the risk of damage to spinal cord motor centers. The removed part of the tumor was diagnosed as melanocytoma. Eight months later, a neurological examination revealed paresis of the right sixth cranial nerve, accompanied by bilateral optic disc edema. Diagnostic imaging revealed a brain tumor. The girl underwent resection of both detected the tumors and an additional satellite lesion revealed during the surgery. The removed tumors were diagnosed as malignant melanomas in pathomorphological examination. Molecular analysis revealed NRASQ61K mutation in both the intracranial and the intramedullary tumor. It should be noted that in cases where available evidence is inconclusive, an integrative diagnostic process is essential to reach a definitive diagnosis.