PERSISTING THROAT PAIN AFTER COVID-19 INFECTION: A CASE REPORT OF SUBACUTE THYROIDITIS

Case Report: A 25-year-old female patient was admitted to the family medicine clinic with complaints of sore throat, fever, and right ear pain that persisted for 15 days despite receiving ant biotherapy treatment. In her medical history, she had a COVID-19 infection 30 days ago. The thyroid examination revealed painful palpation with a slight increase in the size of the thyroid gland. The patient's blood tests and thyroid ultrasonography were evaluated as subacute thyroiditis.Although it is impossible to establish a definite causal relationship between COVID-19 and subacute thyroiditis in this case, we think such a relationship is possible.Since thyroid-related diseases should also be considered in case of sore throat that persists despite treatment and thyroid examination should not be skipped during a physical examination, it is deemed appropriate to present this case with literature.A 25-year-old female patient was admitted to the family medicine clinic with complaints of sore throat, fever, and right ear pain that persisted for 15 days despite receiving ant biotherapy treatment. In her medical history, she had a COVID-19 infection 30 days ago. The thyroid examination revealed painful palpation with a slight increase in the size of the thyroid gland. The patient's blood tests and thyroid ultrasonography were evaluated as subacute thyroiditis. Although it is impossible to establish a definite causal relationship between COVID-19 and subacute thyroiditis in this case, we think such a relationship is possible.  Since thyroid-related diseases should also be considered in case of sore throat that persists despite treatment and thyroid examination should not be skipped during a physical examination, it is deemed appropriate to present this case with literature

Mapping artificial intelligence adoption in hepatology practice and research: challenges and opportunities in MENA region

BackgroundArtificial intelligence (AI) is increasingly relevant to hepatology, yet real-world adoption in the Middle East and North Africa (MENA) is uncertain. We assessed awareness, use, perceived value, barriers, and policy priorities among hepatology clinicians in the region.MethodsA cross-sectional online survey targeted hepatologists and gastroenterologists across 17 MENA countries. The survey assessed clinical and research applications of AI, perceived benefits, clinical and research use, barriers, ethical considerations, and institutional readiness. Descriptive statistics and thematic analysis were performed.ResultsOf 285 invited professionals, 236 completed the survey (response rate: 82.8%). While 73.2% recognized the transformative potential of AI, only 14.4% used AI tools daily, primarily for imaging analysis and disease prediction. AI tools were used in research by 39.8% of respondents, mainly for data analysis, manuscript writing assistance, and predictive modeling. Major barriers included inadequate training (60.6%), limited AI tool access (53%), and insufficient infrastructure (53%). Ethical concerns focused on data privacy, diagnostic accuracy, and over-reliance on automation. Despite these challenges, 70.3% expressed strong interest in AI training., and 43.6% anticipating routine clinical integration within 1–3 years.ConclusionMENA hepatologists are optimistic about AI but report limited routine use and substantial readiness gaps. Priorities include scalable training, interoperable infrastructure and standards, clear governance with human-in-the-loop safeguards, and region-specific validation to enable safe, equitable implementation

Artificial intelligence-supported web application design and development for reducing polypharmacy side effects and supporting rational drug use in geriatric patients

IntroductionThe main complications of polypharmacy, which is known as the simultaneous use of more than five drugs, are potentially inappropriate medicines(PIMs), drug–drug, and drug-disease interaction. It is aimed to prepare an auxiliary tool to reduce the complications of polypharmacy and to support rational drug use(RDU), by evaluating the patient with age, drugs, and chronic diseases in this study.Materials and methodsIn the first phase of this study, as methodological research, an up-to-date and comprehensive auxiliary tool as a reference method was generated with a database containing interaction information of 430 most commonly used drug agents and chronic diseases in geriatrics in the light of current and valid 6 PIM criteria for geriatric patients, and medication prospectuses, relevant current articles, and guidelines. Then, an artificial intelligence(AI) supported web application was designed and developed to facilitate the practical use of the tool. Afterward, the data of a cross-sectional observational single-center study were used for the rate and time of PIM and drug interaction detection with the web application. The proposed web application is publicly available at https://fastrational.com/.ResultsWhile the PIM coverage rate with the proposed tool was 75.3%, the PIM coverage rate of EU(7)-PIM, US-FORTA, TIME-to-STOPP, Beers 2019, STOPP, Priscus criteria in the web application database respectively(63.5%–19.5%) from the highest to the lowest. The proposed tool includes all PIMs, drug–drug, and drug-disease interaction information detected with other criteria. A general practitioner detects interactions for a patient without the web application in 2278 s on average, while the time with the web application is decreased to 33.8 s on average, and this situation is statistically significant.DiscussionIn the literature and this study, the PIM criteria alone are insufficient to include actively used medicines and it shows heterogeneity. In addition, many studies showed that the biggest obstacle to drug regulation in practice is “time constraints.” The proposed comprehensive auxiliary tool analyzes age, drugs, and diseases specifically for the patient 60 times faster than the manual method, and it provides quick access to the relevant references, and ultimately supports RDU for the clinician, with the first and only AI-supported web application.</jats:sec

Evaluating the Capabilities of Generative AI Tools in Understanding Medical Papers: Qualitative Study

BackgroundReading medical papers is a challenging and time-consuming task for doctors, especially when the papers are long and complex. A tool that can help doctors efficiently process and understand medical papers is needed. ObjectiveThis study aims to critically assess and compare the comprehension capabilities of large language models (LLMs) in accurately and efficiently understanding medical research papers using the STROBE (Strengthening the Reporting of Observational Studies in Epidemiology) checklist, which provides a standardized framework for evaluating key elements of observational study. MethodsThe study is a methodological type of research. The study aims to evaluate the understanding capabilities of new generative artificial intelligence tools in medical papers. A novel benchmark pipeline processed 50 medical research papers from PubMed, comparing the answers of 6 LLMs (GPT-3.5-Turbo, GPT-4-0613, GPT-4-1106, PaLM 2, Claude v1, and Gemini Pro) to the benchmark established by expert medical professors. Fifteen questions, derived from the STROBE checklist, assessed LLMs’ understanding of different sections of a research paper. ResultsLLMs exhibited varying performance, with GPT-3.5-Turbo achieving the highest percentage of correct answers (n=3916, 66.9%), followed by GPT-4-1106 (n=3837, 65.6%), PaLM 2 (n=3632, 62.1%), Claude v1 (n=2887, 58.3%), Gemini Pro (n=2878, 49.2%), and GPT-4-0613 (n=2580, 44.1%). Statistical analysis revealed statistically significant differences between LLMs (P<.001), with older models showing inconsistent performance compared to newer versions. LLMs showcased distinct performances for each question across different parts of a scholarly paper—with certain models like PaLM 2 and GPT-3.5 showing remarkable versatility and depth in understanding. ConclusionsThis study is the first to evaluate the performance of different LLMs in understanding medical papers using the retrieval augmented generation method. The findings highlight the potential of LLMs to enhance medical research by improving efficiency and facilitating evidence-based decision-making. Further research is needed to address limitations such as the influence of question formats, potential biases, and the rapid evolution of LLM models