Background: Artificial intelligence, particularly chatbot systems, is becoming an instrumental tool in health care, aiding clinical decision-making and patient engagement. Objective: This study aims to analyze the performance of ChatGPT-3.5 and ChatGPT-4 in addressing complex clinical and ethical dilemmas, and to illustrate their potential role in health care decision-making while comparing seniors’ and residents’ ratings, and specific question types. Methods: A total of 4 specialized physicians formulated 176 real-world clinical questions. A total of 8 senior physicians and residents assessed responses from GPT-3.5 and GPT-4 on a 1-5 scale across 5 categories: accuracy, relevance, clarity, utility, and comprehensiveness. Evaluations were conducted within internal medicine, emergency medicine, and ethics. Comparisons were made globally, between seniors and residents, and across classifications. Results: Both GPT models received high mean scores (4.4, SD 0.8 for GPT-4 and 4.1, SD 1.0 for GPT-3.5). GPT-4 outperformed GPT-3.5 across all rating dimensions, with seniors consistently rating responses higher than residents for both models. Specifically, seniors rated GPT-4 as more beneficial and complete (mean 4.6 vs 4.0 and 4.6 vs 4.1, respectively; P<.001), and GPT-3.5 similarly (mean 4.1 vs 3.7 and 3.9 vs 3.5, respectively; P<.001). Ethical queries received the highest ratings for both models, with mean scores reflecting consistency across accuracy and completeness criteria. Distinctions among question types were significant, particularly for the GPT-4 mean scores in completeness across emergency, internal, and ethical questions (4.2, SD 1.0; 4.3, SD 0.8; and 4.5, SD 0.7, respectively; P<.001), and for GPT-3.5’s accuracy, beneficial, and completeness dimensions. Conclusions: ChatGPT’s potential to assist physicians with medical issues is promising, with prospects to enhance diagnostics, treatments, and ethics. While integration into clinical workflows may be valuable, it must complement, not replace, human expertise. Continued research is essential to ensure safe and effective implementation in clinical environments.

Celiac disease (CD) presents a complex interplay of both innate and adaptive immune responses that drive a variety of pathological manifestations. Recent studies highlight the role of immune-mediated pathogenesis, pinpointing the involvement of antibodies against tissue transglutaminases (TG2, TG3, TG6), specific HLA molecules (DQ2/8), and the regulatory role of interleukin-15, among other cellular and molecular pathways. These aspects illuminate the systemic nature of CD, reflecting its wide-reaching impact that extends beyond gastrointestinal symptoms to affect other physiological systems and giving rise to a range of pathological landscapes, including refractory CD (RCD) and, in severe cases, enteropathy-associated T cell lymphoma. The existing primary therapeutic strategy, a gluten-free diet (GFD), poses significant challenges, such as low adherence rates, necessitating alternative treatments. Emerging therapies target various stages of the disease pathology, from preventing immunogenic gluten peptide absorption to enhancing intestinal epithelial integrity and modulating the immune response, heralding potential breakthroughs in CD management. As the understanding of CD deepens, novel therapeutic avenues are emerging, paving the way for more effective and sophisticated treatment strategies with the aim of enhancing the quality of life of CD patients. This review aims to delineate the immunopathology of CD and exploring its implications on other systems, its complications and the development of novel treatments.

Smelling is a critical sense utilized daily. Consequently, smelling impairment or anosmia may lead to a reduction in life quality. Systemic diseases and particular autoimmune conditions can impair olfactory function; among others are Systemic Lupus Erythematosus, Sjögren Syndrome, and Rheumatoid Arthritis. Interactions between the olfactory process and the immune systems cause this phenomenon. Alongside autoimmune conditions, in the recent COVID-19 pandemic, anosmia was also described as a prevalent infection symptom. Nevertheless, the occurrence of anosmia is significantly less common in Omicron-infected patients. Several theories have been proposed to explain this phenomenon. One possibility is that the Omicron variant preferentially enters host cells via endocytosis, rather than plasma cell membrane fusion. This endosomal pathway is less dependent on the activation of Transmembrane serine protease 2 (TMPRSS2), expressed at the olfactory epithelium. As a result, the Omicron variant may have reduced efficiency in penetrating the olfactory epithelium, leading to a lower prevalence of anosmia. Furthermore, olfactory changes are known to be associated with inflammatory conditions. The Omicron variant elicits a less robust autoimmune and inflammatory response, believed to reduce the probability of anosmia. This review elaborates on the commonalities and differences in autoimmune and COVID-19 omicron-associated anosmia.

Background: Coeliac disease affects approximately 1% of the global population with the diagnosis often relying on invasive and time-demanding methods. Deep learning, a powerful tool in medical science, shows potential for non-invasive, accurate coeliac disease diagnosis, though challenges remain. Objective: This systematic review aimed to evaluate the current state of deep-learning applications in coeliac disease diagnosis and identify potential areas for future research that could enhance diagnostic accuracy, sensitivity, and specificity. Methods: A systematic review was conducted using the following databases: PubMed, Embase, Web of Science, and Scopus. PRISMA guidelines were applied. Two independent reviewers identified research articles using deep learning for coeliac disease diagnosis and severity assessment. Only original research articles with performance metrics data were included. The quality of the diagnostic accuracy studies was assessed using the QUADAS-2 tool, categorizing studies based on risk of bias and concerns about applicability. Due to heterogeneity, a narrative synthesis was conducted to describe the applications and efficacy of the deep-learning techniques (DLT) in coeliac disease diagnosis. Results: The initial search across four databases yielded 417 studies with 195 being removed due to duplicity. Finally, eight studies were found to be suitable for inclusion after rigorous evaluation. They were all published between 2017 and 2023 and focused on using DLT for coeliac disease diagnosis or assessing disease severity. Different deep-learning architectures were applied. Accuracy levels ranged from 84% to 95.94% with the GoogLeNet model achieving 100% sensitivity and specificity for video capsule endoscopy images. Conclusions: DLT hold substantial potential in coeliac disease diagnosis. They offer improved accuracy and the prospect of mitigating clinician bias. However, key challenges persist, notably the requirement for more extensive and diverse datasets, especially to detect milder forms of coeliac disease. These methods are in their nascent stages, underscoring the need of integrating multiple data sources to achieve comprehensive coeliac disease diagnosis.

Background: Sarcoidosis is a multisystem granulomatous disorder with a variable clinical course and complications. The relationship between sarcoidosis and malignancies remains unclear, including specific malignancy associations with sarcoidosis and whether the association is short-term, long-term, or a result of misdiagnoses or coincidence. This study investigated the association between sarcoidosis and malignancy by analyzing the varying intervals between the diagnoses of these two conditions to clarify their inter-relationship. Methods: This retrospective cohort study included almost 24,000 sarcoidosis patients and matched controls at a 1:5 ratio in patients diagnosed between 2000 and 2015 in Israel. Patients had a median age of 57 years. Malignancy rates were compared across several timeframes: overall, within one year before or after sarcoidosis diagnosis and more than one year. Logistic regression models were employed to estimate odds ratios for the association between sarcoidosis and malignancy, adjusting for sociodemographic and clinical variables. Results: Sarcoidosis patients had a significantly higher prevalence of malignancies (19.5%) compared to controls (13.6%) (p < 0.001). The association remained significant for both hematologic malignancies (OR: 2.94, 95% CI: 2.41–3.57) and solid malignancies (OR: 1.41, 95% CI: 1.27–1.55). The strongest association was observed with lymphoma, particularly within the first year of sarcoidosis diagnosis (OR: 14.88, 95% CI: 8.83–25.1). Elevated odds for malignancies persisted both within one year and beyond, including sarcoma and soft tissue cancers and genitourinary malignancies. Conclusions: Our study confirms a significant association between sarcoidosis and both hematologic and solid malignancies in both the short and long term across various timeframes. These findings emphasize the need for increased clinical vigilance in sarcoidosis patients and highlight the importance of further research into the shared genetic and environmental mechanisms that may underlie this relationship.

Objective: The association between chronic inflammatory conditions and cardiovascular disease is well established. Considering FMF, few studies exist investigating the risk of ischaemic heart disease, and none address the risk of stroke. We aimed to evaluate the incidence and risk for stroke in FMF patients compared with the general population. Methods: A retrospective cohort study using the electronic database of Clalit Health Services (CHS), the largest health organization in Israel. All FMF patients diagnosed between 2000 and 2016 were included and matched with control according to age, gender and place of residence. Follow-up continued until the first diagnosis of stroke or death. The incidence of stroke was compared between the groups using univariate and multivariate models adjusting for cardiovascular risk-factors. Results: A total of 9769 FMF patients and a similar number of controls were followed up for a median period of 12.5 years. The mean age at the beginning of the follow-up was 25.7 years. In total, 208 FMF patients were diagnosed with stroke compared with 148 controls, resulting in an incidence rate (per 10 000 persons-years) of 19.8 (95% CI 17.2, 22.7) and 13.9 (95% CI 11.8, 16.4), respectively, and a crude HR of 1.42 (95% CI 1.15–1.76; P < 0.001). In a multivariate analysis, FMF patients who developed amyloidosis with related or non-related renal failure demonstrated significant stroke risk (HR = 2.16; 95% CI 1.38, 3.38; P < 0.001), as well as for those who did not develop these complications (HR = 1.32; 95% CI 1.04, 1.67; P < 0.05). Conclusion: FMF patients are at increased risk for stroke regardless of known complications.