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.

COVID pandemic initiated in early 2019 and the origin from where it initiated was Wuhan city of China. It changed the whole world. A huge population died due to COVID-19 in spite of taking precautions. New treatments and vaccines are introduced for the treatment and prevention. Among successful treatments, antivirals were found effective against COVID-19. But there is a need to find derivatives, which could be more effective for the treatment of COVID-19. The current research is focused on computational studies on one of the antiviral, darunavir. A computational strategy, molecular docking and molecular dynamic simulation techniques is presented to discover the potent analogues of darunavir for inhibiting protease 3CLpro of SARS-CoV2. The newly discovered X-ray structure (PDB ID: 6LU7) was selected for docking study and generated analogues were docked. The docking results showed that the compounds were bound in the active site of receptor with good binding affinity. It was concluded that compounds D8 and D15 were have good binding affinity value of -9.85 and -8.95 kcal/mol, respectively and these compounds were selected for molecular dynamic simulation (MDS) study to check their stability in pocket of receptor.

Introduction: The COVID-19 pandemic that originated in Wuhan, China in December 2019 results in respiratory and gastrointestinal infections. Elderly patients are at high risk. Preventive measures like avoiding contact with COVID-19 patients and wearing N95 masks can contribute to reducing the risk of infection, but vaccination remains crucial. Methodology: A cross-sectional survey-based study was conducted among the elderly population (≥ 50 years) in Pakistan. A 16-items questionnaire explored the socio-demographic profile of the participants, including questions about their age, gender, etc. The other sections included questions regarding vaccine registration, vaccine acceptance, and hesitation and fears towards vaccines. The items were developed to collect the data on the basis of the Likert scale. Results: There was a total of 3059 respondents. The majority of the participants (n = 2726, 89.1%) were aged below 70 years, while 10.9% (n = 333) were aged above 70 years. Many participants (47.1%) expressed their concerns about the efficacy of COVID-19 vaccine booster dose. Despite their willingness to vaccinate with the cOVID-19 vaccine, 24.7% female participants expressed concerns related to the safety of the vaccine. Among all the respondents, 23.1% participants had no concerns about the cOVID-19 vaccine booster dose. The study identified a reluctance and lack of confidence in the efficiency of the COVID-19 booster dose. Conclusions: The alarming situation is rigidity towards vaccination among the elderly population. Necessary measures must be taken by the health department of Pakistan to manage this reluctant behavior and increase confidence on the efficiency of the COVID-19 booster dose.