Artificial intelligence (AI) has emerged as a transformative force in precision medicine, revolutionizing the integration and analysis of health records, genetics, and immunology data. This comprehensive review explores the clinical applications of AI-driven analytics in unlocking personalized insights for patients with autoimmune rheumatic diseases. Through the synergistic approach of integrating AI across diverse data sets, clinicians gain a holistic view of patient health and potential risks. Machine learning models excel at identifying high-risk patients, predicting disease activity, and optimizing therapeutic strategies based on clinical, genomic, and immunological profiles. Deep learning techniques have significantly advanced variant calling, pathogenicity prediction, splicing analysis, and MHC-peptide binding predictions in genetics. AI-enabled immunology data analysis, including dimensionality reduction, cell population identification, and sample classification, provides unprecedented insights into complex immune responses. The review highlights real-world examples of AI-driven precision medicine platforms and clinical decision support tools in rheumatology. Evaluation of outcomes demonstrates the clinical benefits and impact of these approaches in revolutionizing patient care. However, challenges such as data quality, privacy, and clinician trust must be navigated for successful implementation. The future of precision medicine lies in the continued research, development, and clinical integration of AI-driven strategies to unlock personalized patient care and drive innovation in rheumatology.

Fall episodes are not unusual among community residents, especially the elderly, and lower muscle strength is an important issue to address in order to prevent falls. A community health survey was conducted in a suburban area of Taiwan, and 1067 older adults were selected for enrollment in the present study. All the enrolled subjects had been visited at their homes; the subjects’ strength of both hands and muscle mass of both legs were measured and well-established questionnaires were finished by certificated paramedic staffs. The incidence of fall episodes in the previous 1 year in the Yilan elderly population was 15.1%, and the female predominance was significant. A significantly higher prevalence of cataracts was found in group who experienced a fall in the past year (64% vs. 54.9% in the non-fall group). Mild or more severe dementia was much more prevalent in the group who experienced a recent fall (33.8% vs. 25.7% in the non-fall group). The strength of both hands tested as the physical function was 17.6 ± 8.0 kg in the recent fall group, significantly weaker than that in the non-fall group (20.7 ± 8.7 kg). Multivariate regression analysis revealed a greater weekly exercise duration and greater strength of both hands reduced the occurrence of falls among the whole and the female population. The standardized effect sizes of hand grip strength between both groups, not trivial, were 0.29 and 0.37 for the total population and the female subpopulation respectively. Less weekly exercise duration and weaker muscle strength were f ound to be independent risk factors of fall episode(s) in an elderly Taiwanese population, especially in the female sub-population. Muscle strength, measured by average of both hands grip strength, was the most significantly factor of one-year fall episode(s) accessed retrospectively.

Statins are the class of drug most commonly used to treat hyperlipidemia and have been used during pregnancy to prevent or treat preeclampsia (PE)^1,2 owing to their ability to reverse an angiogenic imbalance and correct endothelial dysfunction.³ Other benefits associated with statins are control of gestational dyslipidemia from accelerated atherosclerotic disease in the child,⁴ prevention of umbilical vein endothelial dysfunction,⁵ and spontaneous preterm birth (PTB).⁶ However, the safety of statins with regard to the fetus is of concern. The first statin drug developed, lovastatin, was designated category X (contraindicated) during pregnancy because of its possible association with congenital anomalies. The designation was based on animal studies showing developmental toxic effects and the recognition that cholesterol biosynthesis is critical to prenatal development.⁷ In the 1980s and 1990s, animal studies of statins demonstrated skeletal malformations, gastroschisis, and a low birth weight (LBW) in rats and rabbits.^8,9 A case report of a baby born with VACTERL (vertebral, anal, cardiac, tracheal, esophageal, renal, and limb abnormalities) after in utero exposure to lovastatin has also raised concerns.¹⁰ Owing to ethical concerns, no large randomized clinical trial (RCT) of statin use during pregnancy has been performed, and only a few human cohort studies, case series, and 1 small RCT have focused on the use of statins during pregnancy. In most of the prior studies, the study population was small, and confounders (such as maternal comorbidities) were not fully documented.

Objective: Metformin is the standard first-line drug for patients with Type 2 diabetes (T2DM). However, the optimal second-line oral anti-diabetic agent (ADA) remains unclear. We investigated the cardiovascular risk of various ADAs used as add-on medication to metformin in T2DM patients from a nationwide cohort. Methods: T2DM patients using different add-on oral ADAs after an initial metformin therapy of > 90 days were identified from the Taiwan National Health Insurance Database. Five classes of ADAs, including sulphonylureas (SU), glinides, thiazolidinediones (TZD), alpha-glucosidase inhibitors (AGI), and dipeptidyl peptidase-4 inhibitors (DPP-4I) were selected for analysis. The reference group was the SU added to metformin. Patients were excluded if aged < 20 years, had a history of stroke or acute coronary syndrome (ACS), or were receiving insulin treatment. The primary outcomes included any major adverse cardiovascular event (MACE) including ACS, ischemic/hemorrhagic stroke, and death. A Cox regression model was used to estimate the hazard ratio (HR) for MACE. Results: A total of 26,742 patients receiving their add-on drug to metformin of either SU (n = 24,277), glinides (n = 962), TZD (n = 581), AGI (n = 808), or DPP-4I (n = 114) were analyzed. After a mean follow-up duration of 6.6 ± 3.4 years, a total of 4775 MACEs occurred. Compared with the SU+metformin group (reference), the TZD+metformin (adjusted HR: 0.66; 95% CI 0.50–0.88, p = 0.004) and AGI+metformin (adjusted HR: 0.74; 95% CI 0.59–0.94, p = 0.01) groups showed a significantly lower risk of MACE. Conclusion: Both TZD and AGI, when used as an add-on drug to metformin were associated with lower MACE risk when compared with SU added to metformin in this retrospective cohort study. Trial registration CE13152B-3. Registered 7 Mar, 2013, retrospectively registered