Assessing Generative Pretrained Transformers (GPT) in Clinical Decision-Making: Comparative Analysis of GPT-3.5 and GPT-4.

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.

Blood brain barrier: A review of its anatomy and physiology in health and disease.

The blood-brain barrier (BBB) is the principal regulator of transport of molecules and cells into and out of the central nervous system (CNS). It comprises endothelial cells, pericytes, immune cells, astrocytes, and basement membrane, collectively known as the neurovascular unit. The development of the barrier involves many complex pathways from all the progenitors of the neurovascular unit, but the timing of its formation is not entirely known. The coordinated activities of all the components of the neurovascular unit and other tissues ensure that materials required for growth and maintenance are allowed into the CNS while extraneous ones are excluded. This review summarizes current knowledge of the anatomy, development, and physiology of the BBB, and alterations that occur in disease conditions. Clin. Anat. 31:812-823, 2018. © 2018 Wiley Periodicals, Inc.

Deep Learning in Coeliac Disease: A Systematic Review on Novel Diagnostic Approaches to Disease Diagnosis.

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.

Vitamin D, autoimmunity and recurrent pregnancy loss: More than an association.

Recurrent pregnancy loss (RPL) affects close to 1% of couples; however, the etiology is known in only about 50% of the cases. Recent studies show that autoimmune dysregulation is a probable cause of RPL, which in some cases may be overlooked. In order for a pregnancy to proceed to term, early modulation of immunologic response is required to induce tolerance to the semi-allogenic fetus. Certain subsets of both the innate and adaptive immune responses play a role in the induction of fetomaternal tolerance. A relatively predominant T-cell helper (Th) 2 and T regulatory (Treg) cell population seem to favor a better pregnancy outcome, whereas Th1 and Th17 cell populations appear to have an opposite effect. Lately, the role of vitamin D in the modulation of immune response was established. Vitamin D has been shown to promote a more favorable environment for pregnancy through various mechanisms, such as enhancement of the shift toward Th2 cells and regulation of immune cell differentiation and cytokine secretion. Therefore, it seems that vitamin D deficiency sways the balance toward a worse outcome and may play a part in recurrent pregnancy loss. This review sheds light on the immunologic changes, which occur in early pregnancy and the regulatory role vitamin D has in the maintenance of this delicate balance.