Background and Aim: Large language models (LLMs) demonstrate significant potential in assisting with medical image interpretation. However, the diagnostic accuracy of general-purpose LLMs on image-based internal medicine cases and the added value of brief clinical history remain unclear. This study evaluated three general-purpose LLMs (ChatGPT, Gemini, and DeepSeek) on expert-curated cases to quantify diagnostic accuracy with image-only input versus image plus brief clinical context. Methods: We conducted a comparative evaluation using 138 expert-curated cases from Harrison’s Visual Case Challenge. Each case was presented to the models in two distinct phases: Phase 1 (image only) and Phase 2 (image plus a brief clinical history). The primary endpoint was top-1 diagnostic accuracy for the textbook diagnosis, comparing performance with versus without a brief clinical history. Secondary/Exploratory analyses compared models and assessed agreement between model-generated differential lists and the textbook differential. Statistical analysis included Wilson 95% confidence intervals, McNemar’s tests, Cochran’s Q with Benjamini–Hochberg correction, and Wilcoxon signed-rank tests. Results: The inclusion of clinical history substantially improved diagnostic accuracy for all models. ChatGPT’s accuracy increased from 50.7% in Phase 1 to 80.4% in Phase 2. Gemini’s accuracy improved from 39.9% to 72.5%, and DeepSeek’s accuracy rose from 30.4% to 75.4%. In Phase 2, diagnostic accuracy reached at least 65% across most disease nature and organ system categories. However, agreement with the reference differential diagnoses remained modest, with average overlap rates of 6.99% for ChatGPT, 36.39% for Gemini, and 32.74% for DeepSeek. Conclusions: The provision of brief clinical history significantly enhances the diagnostic accuracy of large language models on visual internal medicine cases. In this benchmark, performance differences between models were smaller in Phase 2 than in Phase 1. While diagnostic precision improves markedly, the models’ ability to generate comprehensive differential diagnoses that align with expert consensus is still limited. These findings underscore the utility of context-aware, multimodal LLMs for educational support and structured diagnostic practice in supervised settings while also highlighting the need for more sophisticated, semantics-sensitive benchmarks for evaluating diagnostic reasoning.

The Saudi Patient Safety Center cross-sectional survey, based on the Agency for Healthcare Research and Quality Hospital Survey on Patient Safety Culture 2.0, was conducted from September 3 to October 28, 2023 to summarize the patient safety culture profile in Eastern Health Cluster hospitals and to identify benchmark, hospital-size, and staff-category patterns relevant for improvement. Ten composites were computed as percent-positive scores and descriptively compared with Saudi and US benchmarks. Of the 13,507 eligible staff, 10,853 responded (80%), and 83% reported having direct contact with patients. The mean percent-positive score for all was 63.29%. Teamwork (74.49%) and organizational learning–continuous improvement (71.76%) had the highest scores. The lowest scores were for Staffing and Work Pace (43.93%), Response to Error (51.09%), and Hospital Management Support for Patient Safety (57.73%). All composites had scores below Saudi benchmarks and smaller hospitals (≤200 beds) had higher scores than larger hospitals (>200 beds). Reporting Patient Safety Events had a score of 66.95%, with 53% of respondents reporting no patient safety events in the past 12 months, suggesting that voluntary reporting may not reflect the true picture of patient safety. The key areas for improvement are staffing, work pace, nonpunitive approach to error, and leadership support. Nurse-to-patient staffing and acuity review should be combined with just culture practices, closed-loop feedback, and active event detection via trigger tools and chart review.

This chapter focuses on the activation and modulating function of the gut-brain axis in relation to memory and other cognitive functions. The gut-brain axis is a two-way communication system between the gastrointestinal system and the brain, closely regulated by the gut microbiota. Recent studies show that gut microbiota can modulate memory through neuroinflammation, neurotransmitter interactions, and effects on synaptic plasticity. Disruption of the gut microbiota, known as gut dysbiosis, has been linked to cognitive disorders and neurodegenerative diseases. This chapter will look at the effects of the gut microbiota on memory and explain how the pathways and signaling through the microbiota affect this relationship. It will also examine therapies that are thought to regulate the composition of the human gut microbiota to improve one’s memory and prevent aging. Additionally, the chapter discusses how gut-targeted therapies may be promising approaches toward enhancing cognitive health and optimal memory performance.

Background/Objectives: Blast-crisis chronic myeloid leukemia (BC-CML) represents a genomically unstable, therapeutically refractory phase driven by clonal evolution beyond BCR::ABL1 dependence. Clinical outcomes remain poor, with median survival under one year. Mutation-level analyses inadequately resolve biological heterogeneity or inform rational patient-specific therapy. We therefore implemented a mutational process–anchored framework integrating genomics, machine learning (ML), and artificial intelligence (AI)–guided drug prioritization at single-patient resolution. Methods: Whole-exome sequencing, COSMIC mutational signature deconvolution, unsupervised ML clustering, and AI-guided pharmacogenomic prioritization were applied to 224 patients with CML (190 chronic phase, 15 accelerated phase, 19 blast crisis) and 19 healthy controls. Results: Mutation burden increased significantly with disease progression, and blast crisis exhibited complex co-mutation architectures involving TP53, BRCA2, IDH1/2, DNMT3A, JAK2, and CSF3R. While gene-level patterns were heterogeneous, dominant mutational processes converged across cases. COSMIC signature extraction stratified BC-CML into three biologically coherent clusters: homologous recombination deficiency/genomic instability (Signatures 3/5), epigenetic–metabolic dysregulation (Signatures 1/2), and oxidative stress–associated cytokine signaling mutagenesis (Signatures 13/18). These process-defined states mechanistically link DNA repair failure, chromatin remodeling, and inflammatory signaling to blast transformation. Integration with an AI-guided pharmacogenomic framework translated signature biology into rational, cluster-specific therapeutic prioritization, including PARP inhibition, IDH targeting, CDK4/6 blockade, JAK pathway inhibition, and redox-modulating strategies. Dominant signature resolution reduced multidrug ambiguity and supported single-patient therapeutic parsimony. External concordance assessment across independent pharmacogenomic datasets supported predicted vulnerabilities. Conclusions: This study establishes a mechanistically interpretable, process-anchored precision oncology framework for BC-CML linking genomic architecture to actionable therapeutic decision-making warranting clinical validation. The approach can further be implemented for AL-AI-prioritized precision treatment of other relapsed, refractory and metastatic cancers.