Lung cancer remains one of the leading causes of cancer-related deaths worldwide. While computed tomography (CT) imaging plays a crucial role in the detection of lung lesions, the potential use of artificial intelligence (AI) tools in assisting diagnosis is increasingly being explored. This study evaluates the diagnostic performance of GPT-4o, a multimodal AI model, in detecting lung lesions on non-contrast chest CT images of patients admitted to the emergency department. We retrospectively analyzed CT scans of 112 adult patients (56 with radiologically confirmed lung lesions and 56 healthy controls) admitted to the emergency department in 2024. GPT-4o was prompted to interpret four selected CT slices per patient at three different temperature settings (low, medium, high). Diagnoses were compared to radiologist assessments for accuracy. GPT-4o demonstrated varying diagnostic performance depending on temperature settings. Sensitivity and specificity at medium temperature were 62.5% and 64.29%, respectively. The highest accuracy (75%) was achieved at low temperature, while high temperature resulted in reduced performance (accuracy: 43.75%). Misinterpretation of lesion location was more common at higher temperatures. GPT-4o demonstrates moderate diagnostic performance in evaluating non-contrast chest CT scans for lung lesions. Its accuracy is highest when using low temperature settings, which favour more conservative and consistent image interpretations. However, the tool showed limitations in detecting lesion locations and in overall sensitivity and specificity. GPT-4o shows potential as a supportive tool in the detection of lung lesions on CT scans, particularly at low temperature settings. However, its diagnostic performance is moderate and should not replace expert radiologist evaluation. Future improvements and prospective validation studies are needed before routine clinical use.

Artificial intelligence (AI) is a rapidly developing technology that has the potential to revolutionise healthcare in recent years. AI is known to have various applications that can be used to improve the diagnosis of diseases, treatments and patient care. In addition, by analysing large data sets that will form the basis of diagnosis and treatment, it is aimed to capture details that may escape the human eye and reveal new information. AI enables physicians to make faster, more accurate diagnoses and select the most suitable treatments for patients. Administrative and clinical operations in the healthcare sector are undergoing a radical change under the influence of the digital transformation process. In this context, innovative steps towards process automation are being implemented at an increasing pace. Artificial intelligence technologies, in particular, have demonstrated remarkable adaptation by being integrated into both administrative and medical applications in the healthcare field. These technologies increase the efficiency of patient management systems along with the optimization of diagnosis and treatment processes, thus providing a significant reduction in both operational and clinical costs. Artificial intelligence-supported solutions reshape the organizational structure of healthcare services, optimize resource use and increase service quality. This transformation has a wide range of effects from patient care to institutional operations and contributes to the sustainability of healthcare systems.

Tietze syndrome is a rare inflammatory disease that can be seen in all age groups and has characteristic features such as swelling, chest pain, tenderness and pain localized in the anterior chest wall, usually in the second or third costal region. The differential diagnosis includes many diseases that cause chest pain. Although the diagnosis is made by excluding other inflammatory pathologies and causes of chest pain, it usually does not require the use of additional diagnostic methods. Its etiology is not fully known, but heavy exercise and minor traumas are considered. Anti-inflammatory drugs and lifestyle changes are recommended in the treatment of Tietze syndrome. Surgical treatment can be applied for refractory cases, but it is generally not necessary. In this case, we report a 38-year-old female patient who presented with chest pain for 1 month, had normal laboratory values and the patient’s pathologies were excluded. This case underscores the importance of clinical examination in diagnosing Tietze syndrome, avoiding unnecessary invasive tests.

Aim: The Albumin-Bilirubin (ALBI) score, originally developed to assess liver function in patients with hepatocellular carcinoma, has recently gained attention as a prognostic marker in critically ill patients. This study aimed to evaluate the effectiveness of the ALBI score in predicting mortality among intensive care unit (ICU) patients. Material and Methods: This retrospective study included 157 adult patients admitted to the ICU diverse clinical conditions, including sepsis, respiratory failure, trauma, acute neurological events, and post-cardiac arrest, between November 2023 and November 2024. Clinical data, laboratory parameters, ALBI, SOFA, and APACHE II scores were assessed at ICU admission. The ALBI score is calculated as follows: (log10 bilirubin [μmol/L] × 0.66) + (albumin [g/L] × −0.085). Results: Among the study participants (mean age: 71.7 ± 18.6 years; 55.4% male), 42.7% had diabetes mellitus, 21.7% chronic kidney disease (CKD), 38.9% chronic obstructive pulmonary disease, 73.9% cardiovascular disease, and 19.7% malignancies. The overall mortality rate was 42.7%. Multivariable Cox regression analysis revealed that CKD (HR: 1.90; 95% CI: 1.07–3.39; p = 0.029), higher ALBI scores (HR: 1.65; 95% CI: 1.03–2.65; p = 0.038) and higher APACHE II scores (HR: 1.04; 95% CI: 1.00–1.08; p = 0.031) were independent predictors of mortality. Conclusion: The ALBI score is an independent predictor of mortality among critically ill patients with heterogeneous clinical conditions in general ICU settings. By integrating hepatic dysfunction, nutritional status, and systemic inflammation, ALBI complements traditional prognostic tools and may enhance clinical decision-making and risk stratification.

Behçet Hastalığı, çeşitli organları tutabilen sistemik bir bozukluk olmakla beraber vaskülit olarak sınıflandırılmaktadır. Vaskülitler damar yapılarındaki inflamasyonla karakterize bir hastalık olarak bilinmektedirler. Behçet hastalığı, erkeklerde daha sık görülmekle birlikte, hastalık bu cinsiyette daha şiddetli seyretmektedir. Hastalığın etiyolojisi multifaktöriyel olup, genetik ve çevresel faktörlerin etkileşimi sonucu ortaya çıktığı düşünülmektedir. Tekrarlayan oral ve genital ülserasyonlar, karakteristik deri lezyonları, artrit, üveit, retinal vaskülit, santral sinir sistemi disfonksiyonu, venöz tromboz ve intestinal lezyonlar gibi çeşitli bulgularla kendini göstermektedir. Büyük damar tutulumu %2-5 arasındadır. Vena Kava Superior Sendromu nadir bir komplikasyon olarak görülmektedir. Bu vakada skrotal bölgede ülseri olan ve son bir aydır eklem ağrıları şikayetiyle başvuran hastanın, Behçet hastalığı açısından tetkik edilirken gelişen vena kava superior sendromlu 35 yaşındaki erkek hastayı sunuyoruz.

Ejmi Eurasian Journal of Medical Investigation